ALBERT

Description: The Web has become the primary source of information containing both structured and unstructured information. A good example is e-commerce where products are usually described by technical specifications (structured data) and textual user reviews (unstructured data). Both sources of information complement each other, covering quantifiable as well as perceived aspects of each product. In fact, for most searches users will have more or less abstract concepts in mind, as opposed to clear cut categorical information. In this paper, we develop a novel approach to reveal implicit product features for querying, by combining structured product data with natural-language product reviews. Using a self-supervised learning technique we progressively build a query-aware representation of the product domain under consideration. This representation can then effectively be used for intuitive querying. We performed extensive experiments confirming the effectiveness of our approach over real world product data. In particular, our evaluations show vastly improved precision and recall over the respective IR techniques. Content Type Journal Article Pages 1-14 DOI 10.3233/WIA-140282 Authors Silviu Homoceanu, Institute for Information Systems, Technische Universität Braunschweig, Braunschweig, Germany. E-mail: {silviu,balke}@ifis.cs.tu-bs.de Wolf-Tilo Balke, Institute for Information Systems, Technische Universität Braunschweig, Braunschweig, Germany. E-mail: {silviu,balke}@ifis.cs.tu-bs.de Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2014

Description: In the past years various methods have been developed which require semantic annotations of Web services as an input. Such methods typically leverage discovery, match-making, composition and execution of Web services in dynamic settings. At the same time a number of automated Web service annotation approaches have been proposed for enabling application of these methods in settings where it is not feasible to provide the annotations manually. However, lack of effective automated evaluation frameworks has seriously limited proper assessment of the constructed annotations in settings where the overall annotation quality of large quantities of Web services needs to be evaluated. This paper describes an evaluation framework for measuring the quality of semantic annotations for a large number of real-world Web services from heterogeneous application domains. The evaluation framework is generally based on analyzing properties of Web service networks constructed from semantic annotations of the Web services. More specifically, we measure scale-free, small-world and correlation degree properties of the networks to evaluate the overall quality of annotations. The evaluation is demonstrated using annotations constructed semi-automatically for a set of publicly available WSDL documents containing descriptions of about 200 000 Web service operations. Content Type Journal Article Pages 15-34 DOI 10.3233/WIA-140283 Authors Shahab Mokarizadeh, Software and Computer System Department, ICT School, Forum 105, 164 40 Kista, Sweden. E-mail: info@ict.kth.se Peep Küngas, Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia. E-mail: peep.kungas@ut.ee Mihhail Matskin, Software and Computer System Department, ICT School, Forum 105, 164 40 Kista, Sweden. E-mail: info@ict.kth.se Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2014

Description: A topic model capable of assigning word pairs to associated topics is developed to explore people's activities. Considering that the form of word pairs led by verbs is a more effective way to express people's activities than separate words, we incorporate the word-connection model into the smoothed Latent Dirichlet Allocation (LDA) to ensure that the words are well paired and assigned to the associated topics. To quantitatively and qualitatively evaluate the proposed model, two datasets were built using Twitter posts as data sources: the wish-related and the geographical information-related datasets. The experiment results using the wish-related dataset indicate that the relatedness of words plays a key role in forming reasonable pairs, and the proposed model, word-pair generative Latent Dirichlet Allocation (wpLDA), performs well in clustering. Results obtained using the geographical information-related dataset demonstrate that the proposed model works well for discovering people's activities, in which the activities are understandably represented with an intuitive character. Content Type Journal Article Pages 193-209 DOI 10.3233/WIA-140292 Authors Dandan Zhu, Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan Yusuke Fukazawa, Services and Solution Development Department, NTT DoCoMo, Inc., NTT DoCoMo R&D Center 3-5 Hikari-no-oka, Yokosuka, Kanagawa 239-8536, Japan Eleftheerios Karapetsas, Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan Jun Ota, Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 2 / 2014

Description: We propose a syntactic possibilistic belief-change operator, which operates on a belief base of necessity-valued formulas. Such a base may be regarded as a finite and compact encoding of a possibility distribution over a possibly infinite set of interpretations. The proposed operator is designed so that it behaves like a semantic possibilistic belief-change operator for BDI agents recently proposed in the literature. The equivalence of the semantic and syntactic operators is then proved. Experimental results are presented. The aim of these experiments is to demonstrate that the cost of belief revision (expressed in terms of the number of entailment checks required) as well as the size of the belief base do not explode as the number of new pieces of information (formulas) supplied increases. Content Type Journal Article Pages 155-169 DOI 10.3233/WIA-140290 Authors Célia da Costa Pereira, Université Nice Sophia Antipolis, I3S, UMR 7271, 06900 Sophia Antipolis, France. E-mail: {celia.pereira,andrea.tettamanzi}@unice.fr Andrea G.B. Tettamanzi, Université Nice Sophia Antipolis, I3S, UMR 7271, 06900 Sophia Antipolis, France. E-mail: {celia.pereira,andrea.tettamanzi}@unice.fr Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 2 / 2014

Description: Multi-agent systems research has been drawing innovative insights from other disciplines such as philosophy and cognitive science. While cognitive studies have shown that over 95% of human decisions conform to the recognition-primed decision making (RPD) model, there still lacks formal descriptions of RPD from agent perspective. The objective of this study is to establish a formal connection between agent mental attitudes and the meta-cognition concepts as used in RPD. We adopt the intentional attitudes as introduced in the SharedPlans theory to model the RPD process and its cognitive activities. In particular, situation recognition is framed as the formation of potential intentions, mental simulation and expectancy monitoring are modeled as two processes for intention evolution, and recognition diagnosis is linked to the notion of intention reconsideration. Such a mentalistic modeling of recognition suggests that the RPD process offers an ideal framework that can systematically integrate intention formation, intention evaluation, and intention reconsideration—three key activities that BDI agents typically implement. This study also indicates that agent-based modeling is a viable approach for formally studying meta-cognition concepts and team cognition concepts. Content Type Journal Article Pages 139-154 DOI 10.3233/WIA-140289 Authors Xiaocong Fan, School of Engineering, Behrend College, The Pennsylvania State University, 5101 Jordan Road, Erie, PA 16563, USA. E-mail: xfan@psu.edu Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 2 / 2014

Description: Previously, we constructed the Japanese Wikipedia Ontology (JWO) via a semi-automatic process using the Japanese Wikipedia, but it had problems due to a lack of upper classes and appropriate definitions of properties. Thus, the aim of the current study was to complement the upper classes in JWO by refining and integrating JWO and Japanese WordNet (JWN) to build a class hierarchy with defined properties based on the considerations of property inheritance. To achieve this, we developed tools that help users to refine the class-instance relationships, to identify the JWO classes that need to be aligned with JWN synsets, and to align the JWO classes with the JWN synsets via user interaction. We also integrated JWO and JWN using a domain ontology development environment, DODDLE-OWL. We also propose a method for building a class hierarchy with defined properties by elevating the common properties defined in sibling classes to higher classes in JWO. Content Type Journal Article Pages 211-233 DOI 10.3233/WIA-140293 Authors Takeshi Morita, School of Social Informatics, Aoyama Gakuin University, Sagamihara-shi, Japan Yuka Sekimoto, Faculty of Science and Technology, Keio University, Yokohama-shi, Japan. E-mail: {s_tamagawa,yamaguti}@ae.keio.ac.jp Susumu Tamagawa, Faculty of Science and Technology, Keio University, Yokohama-shi, Japan. E-mail: {s_tamagawa,yamaguti}@ae.keio.ac.jp Takahira Yamaguchi, Faculty of Science and Technology, Keio University, Yokohama-shi, Japan. E-mail: {s_tamagawa,yamaguti}@ae.keio.ac.jp Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 2 / 2014

Description: Resource management is a key challenge in multiagent systems. It is especially important in dynamic environments where decisions need to be made quickly and when decisions can get obsolete quickly. In wireless local area networks (WLANs), resource management includes dynamic channel assignment, dynamic transmit power control and load balancing of WLANs traffic. In this work, we present a novel decentralized framework and a distributed optimization algorithm DLB-SDPOP for load balancing in complex WLANs. Our self-stabilizing algorithm focuses on repairing the network structure instead of reconstructing it when network perturbations occur. It controls the complexity of problem solving by utilizing efficient search and leverages uncertainty to reduce the possibility of reaching myopic solutions. The size and number of inter-agent communication messages are significantly reduced using a communication filtering mechanism. We categorize different scenarios based on key characteristics (i.e., load, dynamics and uncertainty) in WLANs and compare our algorithm with other state of the art distributed constraint optimization algorithms in each scenario. Our empirical results show that our distributed approach improves WLANs load balancing performance significantly. Content Type Journal Article Pages 111-138 DOI 10.3233/WIA-140288 Authors Shanjun Cheng, AltiSource, Greensboro, NC, USA. E-mail: chengshanjun@gmail.com Anita Raja, Department of Software and Information Systems, The University of North Carolina at Charlotte, Charlotte, NC, 28223, USA. E-mail: anraja@uncc.edu Jiang Xie, Department of Electrical and Computer Engineering, The University of North Carolina at Charlotte, Charlotte, NC, 28223, USA. E-mail: jxie1@uncc.edu Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 2 / 2014

Description: In this paper we empirically investigate the feasibility of using peer-designed agents (PDAs) instead of people for the purpose of mechanism evaluation. This approach has been increasingly advocated in agent research in recent years, mainly due to its many benefits in terms of time and cost. Our experiments compare the behavior of 31 PDAs and 150 people in a legacy eCommerce-based price-exploration setting, using different price-setting mechanisms and diverse performance measures. The results show a varying level of similarity between the aggregate behavior obtained when using people and when using PDAs. In some settings similar results were obtained, in others the use of PDAs rather than people yielded substantial differences. This suggests that the ability to generalize results from one successful implementation of PDA-based systems to another, regarding the use of PDAs as a substitute for people in system evaluations, is quite limited. The decision to prefer PDAs for mechanism evaluation is therefore setting dependent and the applicability of the approach must be re-evaluated when switching to a new setting or using a different measure. Furthermore, we show that even in settings where the aggregate behavior is found to be similar, the individual strategies used by agents in each group highly vary. Finally, we report the results of an extensive comparative analysis of the level of optimality reflected in people's and PDAs' individual decisions in our decision making setting. The results show that the decisions of both groups are far from optimal, however the use of PDAs results in strategies that are more than twice as close to the optimal ones. Content Type Journal Article Pages 171-191 DOI 10.3233/WIA-140291 Authors Avshalom Elmalech, Computer Science Department, Bar-Ilan University, Ramat-Gan, Israel. E-mail: {elmalea,sarned}@cs.biu.ac.il David Sarne, Computer Science Department, Bar-Ilan University, Ramat-Gan, Israel. E-mail: {elmalea,sarned}@cs.biu.ac.il Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 2 / 2014

Description: Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the web users i.e. data collected from web logs or cookies without knowing users preferences. Developing such a recommender system is complex due to the huge amount of anonymous noisy data. In this paper we present a Particle Swarm Optimization (PSO) based clustering approach called Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) for building a recommender system based on implicit web usage data. The approach mimics multi-agent properties of the particles of a swarm and divide the problem space into smaller sub-spaces i.e. clusters. Each cluster represents a particular group of user with similar interests. Later, the K-nearest neighbours of the most relevant cluster are generated as recommendations for a web user and ranked based on their distance. We performed different experiments for preprocessing, to assess the quality of clusters, and for the accuracy of recommendations. An overall accuracy of 65% to 95% was achieved for different scenarios, while in some cases the accuracy touched 100 precent when the selection was made from the top-5 recommendations. Content Type Journal Article Pages 389-409 DOI 10.3233/WIA-140302 Authors Shafiq Alam, 38 Princes Street, Auckland 1010, New Zealand. E-mail: {gill,ykoh,pat}@cs.auckland.ac.nz Gillian Dobbie, 38 Princes Street, Auckland 1010, New Zealand. E-mail: {gill,ykoh,pat}@cs.auckland.ac.nz Yun Sing Koh, 38 Princes Street, Auckland 1010, New Zealand. E-mail: {gill,ykoh,pat}@cs.auckland.ac.nz Patricia Riddle, 38 Princes Street, Auckland 1010, New Zealand. E-mail: {gill,ykoh,pat}@cs.auckland.ac.nz Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: State-of-the-art studies on cyberbullying detection, using text classification, predominantly take it for granted that streaming text can be completely labelled. However, the rapid growth of unlabelled data generated in real time from online content renders this virtually impossible. In this paper, we propose a session-based framework for automatic detection of cyberbullying within the large volume of unlabelled streaming text. Given that the streaming data from Social Networks arrives in large volume at the server system, we incorporate an ensemble of one-class classifiers in the session-based framework. System uses Multi-Agent distributed environment to process streaming data from multiple social network sources. The proposed strategy tackles real world situations, where only a few positive instances of cyberbullying are available for initial training. Our main contribution in this paper is to automatically detect cyberbullying in real world situations, where labelled data is not readily available. Initial results indicate the suggested approach is reasonably effective for detecting cyberbullying automatically on social networks. The experiments indicate that the ensemble learner outperforms the single window and fixed window approaches, while the learning process is based on positive and unlabelled data only, no negative data is available for training. Content Type Journal Article Pages 375-388 DOI 10.3233/WIA-140301 Authors Vinita Nahar, School of Information Technology and Electrical Engineering, The University of Queensland, Australia. E-mail: v.nahar@uq.edu.au/vinita.nahar.uq@gmail.com, xueli@itee.uq.edu.au Xue Li, School of Information Technology and Electrical Engineering, The University of Queensland, Australia. E-mail: v.nahar@uq.edu.au/vinita.nahar.uq@gmail.com, xueli@itee.uq.edu.au Hao Lan Zhang, NIT, Zhejiang University, Ningbo, Zhejiang Province, China. E-mail: haolan.zhang@nit.zju.edu.cn Chaoyi Pang, NIT, Zhejiang University, Ningbo, Zhejiang Province, China. E-mail: haolan.zhang@nit.zju.edu.cn Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: This special issue particularly focuses on using agent-based methods to solve the complex computational problems arising in Big Data environments. It covers the recent advances in the areas of distributed problem solving, agent-based data mining, as well as recommendation systems, working with data extracted from both physical and online environments. Content Type Journal Article Category Guest editorial Pages 343-345 DOI 10.3233/WIA-140300 Authors Hao Lan Zhang, Center for SCDM, NIT, Zhejiang University, Ningbo City, China. E-mail: haolan.zhang@nit.zju.edu.cn Hoong Chuin Lau, School of Information Systems, Singapore Management University, Singapore. E-mail: hclau@smu.edu.sg Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: In this paper, we formulate and study the Multi-agent Orienteering Problem with Time-dependent Capacity Constraints (MOPTCC). MOPTCC is similar to the classical orienteering problem at the single-agent level: given a limited time budget, an agent travels around the network and collects rewards by visiting different nodes, with the objective of maximizing the sum of his collected rewards. The most important feature we introduce in MOPTCC is the inclusion of multiple competing and interacting agents. All agents in MOPTCC are assumed to be self-interested, and they interact with each other when arrive at the same nodes simultaneously. As all nodes are capacitated, if a particular node receives more agents than its capacity, all agents at that node will be made to wait and agents suffer collectively as a result (in terms of extra time needed for queueing). Due to the decentralized nature of the problem, MOPTCC cannot be solved in a centralized manner; instead, we need to seek out equilibrium solutions; and if this is not possible, at least approximated equilibrium solutions. The major contribution of this paper is the formulation of the problem, and our first attempt in identifying an efficient and effective equilibrium-seeking procedure for MOPTCC. Content Type Journal Article Pages 347-358 DOI 10.3233/WIA-140304 Authors Cen Chen, School of Information Systems, Singapore Management University, Singapore. E-mail: {cenchen.2012,sfcheng,hclau}@smu.edu.sg Shih-Fen Cheng, School of Information Systems, Singapore Management University, Singapore. E-mail: {cenchen.2012,sfcheng,hclau}@smu.edu.sg Hoong Chuin Lau, School of Information Systems, Singapore Management University, Singapore. E-mail: {cenchen.2012,sfcheng,hclau}@smu.edu.sg Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: With the rapid development of Internet, the amount of information on the Web grows explosively, people often feel puzzled and helpless in finding and getting the information they really need. For overcoming this problem, recommender systems such as singular value decomposition (SVD) method help users finding relevant information, products or services by providing personalized recommendations based on their profiles. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data. Thus, to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm which is called ApproSVD algorithm based on approximating SVD in this paper. The trick behind our algorithm is to sample some rows of a user-item matrix, rescale each row by an appropriate factor to form a relatively smaller matrix, and then reduce the dimensionality of the smaller matrix. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on MovieLens dataset and Flixster dataset, and show that our method has the best prediction quality. Furthermore, in order to show the superiority of the ApproSVD algorithm, we also conduct an empirical study to compare the prediction accuracy and running time between ApproSVD algorithm and incremental SVD algorithm on MovieLens dataset and Flixster dataset, and demonstrate that our proposed method has better performance overall. Content Type Journal Article Pages 359-373 DOI 10.3233/WIA-140303 Authors Xun Zhou, UCAS-VU Joint Lab for Social Computing and E-Health Research, University of Chinese Academy of Sciences, Beijing, China. E-mail: zhouxuntthappy@163.com Jing He, Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia. E-mail: {Jing.He,Yanchun.Zhang}@vu.edu.au Guangyan Huang, School of Information Technology, Deakin University, Melbourne, Australia. E-mail: guangyan.huang@deakin.edu.au Yanchun Zhang, Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia. E-mail: {Jing.He,Yanchun.Zhang}@vu.edu.au Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: Fervent and agile communication on social networking sites and in virtual organizations provides opportunities for potential issues to trigger individuals into individual action as well as the attraction and mobilization of like-minded individuals into an organization that is both physically and virtually emergent. Examples are the rapid pace of Arab Spring and the diffusion rate of the occupy movement. Previous organizational models lack the representational power to model spontaneous exigencies of a network organization that accounts for rapid rates of dissemination in impromptu networks. This model, therefore, is conceived in a life cycle for a prototypical, emergent networked organization and description of operations therein from formation to dissolution. After describing the life cycle, this article offers insights for a model of a successful emergent organization and an implemented example of a spacecraft organization of satellites. Content Type Journal Article Pages 325-339 DOI 10.3233/WIA-140299 Authors Saad Alqithami, Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States Henry Hexmoor, Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: In task environments with large state and action spaces, the use of temporal and state abstraction can potentially improve the decision making performance of agents. However, existing approaches within a reinforcement learning framework typically identify possible subgoal states and instantly learn stochastic subpolicies to reach them from other states. In these circumstances, exploration of the reinforcement learner is unfavorably biased towards local behavior around these subgoals; temporal abstractions are not exploited to reduce required deliberation; and the benefit of employing temporal abstractions is conflated with the benefit of additional learning done to define subpolicies. In this paper, we consider a cognitive agent architecture that allows for the extraction and reuse of temporal abstractions in the form of experience trajectories from a bottom-level reinforcement learning module and a top-level module based on the BDI (Belief-Desire-Intention) model. Here, the reuse of trajectories depends on the situation in which their recording was started. We investigate the efficacy of our approach using two well-known domains – the pursuit and the taxi domains. Detailed simulation experiments demonstrate that the use of experience trajectories as plans acquired at runtime can reduce the amount of decision making without significantly affecting asymptotic performance. The combination of temporal and state abstraction leads to improved performance during the initial learning of the reinforcement learner. Our approach can significantly reduce the number of deliberations required. Content Type Journal Article Pages 267-287 DOI 10.3233/WIA-140296 Authors Jens Pfau, CGI Space, 64295 Darmstadt, Germany. E-mail: jens.pfau@cgi.com Samin Karim, Department of Computing and Information Systems, University of Melbourne, 3010, Victoria, Australia. E-mail: {karims,mkirley,l.sonenberg}@unimelb.edu.au Michael Kirley, Department of Computing and Information Systems, University of Melbourne, 3010, Victoria, Australia. E-mail: {karims,mkirley,l.sonenberg}@unimelb.edu.au Liz Sonenberg, Department of Computing and Information Systems, University of Melbourne, 3010, Victoria, Australia. E-mail: {karims,mkirley,l.sonenberg}@unimelb.edu.au Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: Taste in music is of highly subjective nature, making the recommending of music tracks a challenging research task. With TRECS, our live prototype system, we present a weighted hybrid recommender approach that amalgamates three diverse recommender techniques into one comprehensive score. Moreover, our prototype system peppers the generated result list by some simple serendipity heuristic. This way, users can benefit from recommendations aligned with their current taste in music while gaining some exploratory diversification. Empirical evaluations of the live TRECS system, based on an online evaluation, assess the overall recommendation quality as well as the impact of each of the three sub-recommenders. In addition, to better understand the nature and impact of serendipity in isolation, we conducted another study with another recommender prototype of ours, named SONG STUMBLER. The latter assesses three different serendipity metrics in an online evaluation. Content Type Journal Article Pages 235-248 DOI 10.3233/WIA-140294 Authors Cai-Nicolas Ziegler, XING EVENTS GmbH, Sandstraße 33, D-80335 München, Germany Thomas Hornung, REWE Information Systems GmbH, Humboldtstraße 140-144, D-51149 Köln, Germany. E-mail: thomas.hornung@rewe-group.com Martin Przyjaciel-Zablocki, Dept. of Computer Science, University of Freiburg, Georges-Köhler-Allee 51, D-79110 Freiburg, Germany. E-mail: {zablocki,gausss,lausen}@informatik.uni-freiburg.de Sven Gauß, Dept. of Computer Science, University of Freiburg, Georges-Köhler-Allee 51, D-79110 Freiburg, Germany. E-mail: {zablocki,gausss,lausen}@informatik.uni-freiburg.de Georg Lausen, REWE Information Systems GmbH, Humboldtstraße 140-144, D-51149 Köln, Germany. E-mail: thomas.hornung@rewe-group.com Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy. Content Type Journal Article Pages 289-307 DOI 10.3233/WIA-140297 Authors Yongli Ren, School of Information Technology, Deakin University, 221 Burwood Highway, Vic 3125, Australia. E-mail: yongli@deakin.edu.au, wanlei@deakin.edu.au Gang Li, School of Information Technology, Deakin University, 221 Burwood Highway, Vic 3125, Australia. E-mail: yongli@deakin.edu.au, wanlei@deakin.edu.au Wanlei Zhou, School of Information Technology, Deakin University, 221 Burwood Highway, Vic 3125, Australia. E-mail: yongli@deakin.edu.au, wanlei@deakin.edu.au Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: We present an agent architecture and a hybrid behavior learning method for it that allows the use of communicated intentions of other agents to create agents that are able to cooperate with various configurations of other agents in fulfilling a task. Our shout-ahead architecture is based on two rule sets, one making decisions without communicated intentions and one with these intentions, and reinforcement learning is used to determine in a particular situation which set is responsible for the final decision. Evolutionary learning is used to learn these rules. Our application of this approach to learning behaviors for units in a computer game shows that the use of shout-ahead using only communicated intentions in the second rule set substantially improves the quality of the learned behavior compared to agents not using shout-ahead. Also, allowing for additional conditions in the second rule set can either improve the quality or worsen it, based on what type of conditions are used. Content Type Journal Article Pages 309-324 DOI 10.3233/WIA-140298 Authors Sanjeev Paskaradevan, Department of Computer Science, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4. E-mail: sanjeev.com@gmail.com, {denzinge,dkwehr}@ucalgary.ca Jörg Denzinger, Department of Computer Science, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4. E-mail: sanjeev.com@gmail.com, {denzinge,dkwehr}@ucalgary.ca Daniel Wehr, Department of Computer Science, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4. E-mail: sanjeev.com@gmail.com, {denzinge,dkwehr}@ucalgary.ca Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: Personality can be defined as a set of characteristics which makes a person unique. Psychological theory suggests that people's behavior is a reflection of personality. Therefore, it is feasible to predict personality through behavior. Conventional personality assessment is performed by self-report inventory. Participants need to fill in a tedious inventory to get their personality scores. In the large-scale investigation, every returned inventory needs manual computation, which costs much manual efforts and cannot be done in real time. In order to avoid these shortages, this research aims to objectively predict the Big-Five personality from the usage records of Sina Microblog. Since its initial launch in December, 2005, Sina Microblog has been the leading microblogging service provider in China. Millions of users upload and download resources via microblogging status everyday. Therefore, by conducting an online user survey of 444 active users, this paper analyzes the relation modes between personality and online behavior. Furthermore, this research proposes multi-task regression and incremental regression to predict the Big-Five personality from online behaviors. The results indicate that correlation factors are significant between different personality dimensions. Besides, our training data set is reliable enough and multi-task regression performs better than other modeling algorithms. Content Type Journal Article Pages 249-265 DOI 10.3233/WIA-140295 Authors Shuotian Bai, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China. E-mail: baishutian10@ucas.ac.cn Sha Yuan, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China. E-mail: baishutian10@ucas.ac.cn Bibo Hao, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China. E-mail: baishutian10@ucas.ac.cn Tingshao Zhu, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road Chaoyang District, Beijing, China Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: In this paper we present CooL-AgentSpeak, an extension of AgentSpeak-DL with plan exchange and ontology services. In CooL-AgentSpeak, the search for an ontologically relevant plan is no longer limited to the agent's local plan library but is carried out in the other agents' libraries too, according to a cooperation strategy, and it is not based solely on unification and on the subsumption relation between concepts, but also on ontology matching. Belief querying and updating also take advantage of ontological reasoning and matching. Content Type Journal Article Pages 83-107 DOI 10.3233/WIA-140287 Authors Viviana Mascardi, DIBRIS, University of Genova, Via Dodecaneso 35, 16146, Genova, Italy. E-mail: davide.ancona@unige.it, matteo.barbieri@oniriclabs.com Davide Ancona, DIBRIS, University of Genova, Via Dodecaneso 35, 16146, Genova, Italy. E-mail: davide.ancona@unige.it, matteo.barbieri@oniriclabs.com Matteo Barbieri, DIBRIS, University of Genova, Via Dodecaneso 35, 16146, Genova, Italy. E-mail: davide.ancona@unige.it, matteo.barbieri@oniriclabs.com Rafael H. Bordini, FACIN-PUCRS, Av. Ipiranga 6681, 90619-900, Porto Alegre, RS, Brazil. E-mail: r.bordini@pucrs.br Alessandro Ricci, DEIS, University of Bologna, Via Venezia 52, 47023, Cesena (FC), Italy. E-mail: a.ricci@unibo.it Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2014

Description: During deep brain stimulation (DBS) treatment of Parkinson disease, the target of the surgery is the subthalamic nucleus (STN). This anatomical structure is small (9×7×4 mm) and poorly visible using Computer Tomography (CT) or Magnetic Resonance Imaging (MRI) scans. Because of that, a multi-electrode micro recording system is used intra surgically for better localization of the target nucleus. This paper presents five different analytical methods, that can be used to construct an autonomic system which can aid neurosurgeons in precise localization of the Subthalamic    Nucleus (STN). Such system could be used during surgery in the environment of the operation theater. Described methods take as input signals recorded from the micro electrodes. Their result in turn allows one to tell which from the recorded signals comes from the STN. First method bases on the recorded action potentials, i.e. on electrical activity of neurons that are near electrode's recording tip. Second utilizes root mean square of recorded signals. Third takes into account amplitude of the background noise present in the recorded signal. The last two methods examine Low Frequency Background (LFB) and High Frequency Background (HFB). Content Type Journal Article Pages 63-82 DOI 10.3233/WIA-140286 Authors Konrad A. Ciecierski, Warsaw Univ. of Technology, Institute of Comp. Science, 00-655 Warsaw, Poland Zbigniew W. Raś, Warsaw Univ. of Technology, Institute of Comp. Science, 00-655 Warsaw, Poland Andrzej W. Przybyszewski, UMass Medical School, Dept. of Neurology, Worcester, MA 01655, USA Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2014

Description: Ant colony optimization (ACO), an intelligential optimization algorithm, has been widely used to solve combinational optimization problems. One of the obstacles in applying ACO is that its search process is sometimes biased by algorithm features such as the pheromone model and the method of constructing the solutions. Due to such searching bias, ant colony optimization cannot converge to the optimal solutions of deceptive problems. The goal of our study is to find an effective method to avoid such searching bias and to achieve high performance of ACO on deceptive problems. In this paper, we present a method for avoiding the searching bias in the first order deceptive problem of ACO taking the n-bit trap problem as an instance. Convergence analysis of our method is also given. Our experimental results confirm the correctness of our theoretical analysis and show that our method can effectively avoid the searching bias and can ensure both the convergence in value and the convergence in solution for the first order deceptive problems. Content Type Journal Article Pages 51-62 DOI 10.3233/WIA-140285 Authors Bolun Chen, Department of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China Ling Chen, Department of Computer Science and Engineering, Yangzhou University, Yangzhou, 225127, China Haiying Sun, Department of Computer Science and Engineering, Yangzhou University, Yangzhou, 225127, China Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2014

Description: Autocompletion systems support users in the formulation of queries in different situations, from development environments to the web. In this paper we describe Composite Match Autocompletion (COMMA), a lightweight approach to the introduction of semantics in the realization of a semi-structured data autocompletion matching algorithm. The approach is formally described, then it is applied and evaluated with specific reference to the e-commerce context. The semantic extension to the matching algorithm exploits available information about product categories and distinguishing features of products to enhance the elaboration of exploratory queries. COMMA supports a seamless management of both targeted/precise queries and exploratory/vague ones, combining different filtering and scoring techniques. The algorithm is evaluated with respect both to effectiveness and efficiency in a real-world scenario: the achieved improvement is significant and it is not associated to a sensible increase of computational costs. Content Type Journal Article Pages 35-49 DOI 10.3233/WIA-140284 Authors Riccardo Porrini, DISCo, University of Milano-Bicocca, Viale Sarca 336/14, 20126, Milan, Italy. E-mail: {matteo.palmonari,giuseppe.vizzari,riccardo.porrini}@disco.unimib.it Matteo Palmonari, DISCo, University of Milano-Bicocca, Viale Sarca 336/14, 20126, Milan, Italy. E-mail: {matteo.palmonari,giuseppe.vizzari,riccardo.porrini}@disco.unimib.it Giuseppe Vizzari, DISCo, University of Milano-Bicocca, Viale Sarca 336/14, 20126, Milan, Italy. E-mail: {matteo.palmonari,giuseppe.vizzari,riccardo.porrini}@disco.unimib.it Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 1 / 2014

Description: Motivated by applications in Dynamic Spectrum Access Networks, we focus on a system in which a few agents are engaged in a costly individual exploration process where each agent's benefit is determined according to the minimum obtained value. Such an exploration pattern is applicable to many systems, including shipment and travel planning. This paper formally introduces and analyzes a sequential variant of the general model. According to that variant, only a single agent engages in exploration at any given time, and when an agent initiates its exploration, it has complete information about the minimum value obtained by the other agents so far. We prove that the exploration strategy of each agent, according to the equilibrium of the resulting Stackelberg game, is reservation-value based, and show how the reservation values can be calculated. We also analyze the agents' expected-benefit maximizing exploration strategies when they are fully cooperative (i.e., when they aim to maximize the expected joint benefit). The equilibrium strategies and the expected benefit of each agent are illustrated using a synthetic homogeneous environment, thereby demonstrating the properties of this new exploration scheme and the benefits of cooperation. Content Type Journal Article Pages 221-244 DOI 10.3233/WIA-130272 Authors Igor Rochlin, Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel. E-mail: {igor.rochlin,david.sarne}@gmail.com David Sarne, Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel. E-mail: {igor.rochlin,david.sarne}@gmail.com Gil Zussman, Department of Electrical Engineering, Columbia University, New York, NY, USA. E-mail: gil@ee.columbia.edu Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2013

Description: Service selection is one of the important problems in applications of multi-agent systems. A qualitative way is desirable for service selection according to agents' preferences on non-functional Quality of Service (QoS) attributes of services. However, it is challenging when the decision has to be made for multiple agents with preferences on attributes that may be incomplete. In this paper, first use a qualitative graphical representation tool called CP-nets to describe preference relations in a relatively compact, intuitive and structured manner. We then propose a preference reasoning algorithm to construct a derivation tree from a CP-net for each agent and then generate all service patterns for this agent. The Rank semantic is used together with the Lex semantic to provide the ordering of different service patterns. After that, we merge ranked service patterns for all agents and select a set of services that satisfy these agents the most. Finally, we also propose a semantic validation algorithm to show the satisfactory degree of the best service patterns according to other voting semantics. Experimental results indicate that this method can always obtain optimal outcomes which closely satisfy all agents, within acceptable execution time. Content Type Journal Article Pages 263-282 DOI 10.3233/WIA-130274 Authors Hongbing Wang, School of Science and Engineering, Southeast University, China Jie Zhang, School of Computer Engineering, Nanyang Technological University, Singapore. E-mail: zhangj@ntu.edu.sg Cheng Wan, School of Science and Engineering, Southeast University, China Shizhi Shao, School of Science and Engineering, Southeast University, China Robin Cohen, School of Computer Science, University of Waterloo, Canada. E-mail: rcohen@cs.uwaterloo.ca Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2013

Description: We are concerned with normative systems for communities of human and artificial agents that cooperate for improving their overall goals. Unlike open artificial multi-agent systems, we need to be more flexible in judging the behavior of the agents, and still thrive for efficiency. To attain the overall system goals norms are used for guiding the behavior of the agents, whether human or artificial. To provide flexibility in adaptation the power of ontology expressed in description logic is used. The problem consists then in conceiving some mechanism that can support flexibility for the system, without imposing a too heavy burden for such applications. Some cases are presented to show how our proposal satisfies the needs for adapting and evolving normative systems for communities of practice. The interplay of the norms and the concepts in the ontology provides a period of grace for minor offenders, wether artificial or human. Content Type Journal Article Pages 339-350 DOI 10.3233/WIA-130279 Authors Ioan Alfred Letia, Department of Computer Science, Technical University of Cluj-Napoca, Romania. E-mail: pop_oct@yahoo.com Octavian Pop, Department of Computer Science, Technical University of Cluj-Napoca, Romania. E-mail: pop_oct@yahoo.com Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2013

Description: The success of virtual communities (VCs) relies on collaboration and resource sharing principles, making trust a priority for each member. The work presented in this paper addresses the problem of trust management in open and decentralised virtual communities. We address this problem by proposing an Adaptive and Socially-Compliant Trust Management System (ASC-TMS) adopting a multi-agent based approach. Our proposal relies on the joint use of trust policies and meta-policies, which allow VC members to exhibit adaptive (i.e., in accordance with environment changes) and socially-compliant (i.e., in accordance with the collective policies specified by the community members) behaviour. The experiments reported in this paper focus on the socially-compliant aspect of ASC-TMS. They aim at evaluating different policy integration algorithms. Content Type Journal Article Pages 315-338 DOI 10.3233/WIA-130278 Authors Reda Yaich, Ecole Nationale Supérieure des Mines de Saint-Etienne, Saint-Etienne, France. E-mail: {yaich,boissier,picard,jaillon}@emse.fr Olivier Boissier, Ecole Nationale Supérieure des Mines de Saint-Etienne, Saint-Etienne, France. E-mail: {yaich,boissier,picard,jaillon}@emse.fr Gauthier Picard, Ecole Nationale Supérieure des Mines de Saint-Etienne, Saint-Etienne, France. E-mail: {yaich,boissier,picard,jaillon}@emse.fr Philippe Jaillon, Ecole Nationale Supérieure des Mines de Saint-Etienne, Saint-Etienne, France. E-mail: {yaich,boissier,picard,jaillon}@emse.fr Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2013

Description: Online Social Networks (OSN) and Virtual Communities (VC) software are vital tools useful to connect organizations with customers or community members. As these tools become more ubiquitous with general population, different managment problems start to arise. As these members' interactions become large, it is impossible for manual handling of moderation tasks without automatic or semi-automatic techniques. Of course, web mining techniques are very useful for understanding text patterns or browsing patterns in Websites, opening an opportunity to develop new algorithms to discover community members' patterns which have to be moderated. There have been previous work done on text patterns discovery for moderation that have reduced the moderation task to finding spammers. However, the moderation problem is much more complex: it involves not only text but also behavior patterns (from bulling of other members to fights between them). We present a dissimilarity measure which includes human interaction aspects combined with these interactions' contents semantics (not just free words of text). We show how we successfully applied our method into a real Virtual Community of Practice to detect users that should be moderated. Content Type Journal Article Pages 351-364 DOI 10.3233/WIA-130280 Authors Sebastián A. Ríos, Industrial Engineering Department, University of Chile, AV. República 701, Santiago, Chile. E-mail: srios@dii.uchile.cl; robsilva@ing.uchile.cl Roberto A. Silva, Industrial Engineering Department, University of Chile, AV. República 701, Santiago, Chile. E-mail: srios@dii.uchile.cl; robsilva@ing.uchile.cl Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2013

Description: The search for information on the Web of Data is becoming increasingly difficult due to its considerable growth. Especially novice users need to acquire both knowledge about the underlying ontology structure and proficiency in formulating formal queries (e.g. SPARQL queries) to retrieve information from Linked Data sources. So as to simplify and automate the querying and retrieval of information from such sources, this paper presents an approach for constructing SPARQL queries based on user-supplied keywords. Our approach utilizes a set of predefined basic graph pattern templates for generating adequate interpretations of user queries. This is achieved by obtaining ranked lists of candidate resource identifiers for the supplied keywords and then injecting these identifiers into suitable positions in the graph pattern templates. The main advantages of our approach are that it is completely agnostic of the underlying knowledge base and ontology schema, that it scales to large knowledge bases and is simple to use. We evaluate all 17 possible valid graph pattern templates by measuring their precision and recall on 53 queries against DBpedia. Our results show that 8 of these basic graph pattern templates return results with a precision above 70%. Our approach is implemented as a Web search interface and performs sufficiently fast to provide answers within an acceptable time frame even when used on large knowledge bases. Content Type Journal Article Pages 283-295 DOI 10.3233/WIA-130275 Authors Saeedeh Shekarpour, Universität Leipzig, Institut für Informatik, D-04103 Leipzig, Germany. E-mail: {shekarpour,auer,ngonga,gerber,hellmann,stadler}@informatik.uni-leipzig.de Sören Auer, Universität Leipzig, Institut für Informatik, D-04103 Leipzig, Germany. E-mail: {shekarpour,auer,ngonga,gerber,hellmann,stadler}@informatik.uni-leipzig.de Axel-Cyrille Ngonga Ngomo, Universität Leipzig, Institut für Informatik, D-04103 Leipzig, Germany. E-mail: {shekarpour,auer,ngonga,gerber,hellmann,stadler}@informatik.uni-leipzig.de Daniel Gerber, Universität Leipzig, Institut für Informatik, D-04103 Leipzig, Germany. E-mail: {shekarpour,auer,ngonga,gerber,hellmann,stadler}@informatik.uni-leipzig.de Sebastian Hellmann, Universität Leipzig, Institut für Informatik, D-04103 Leipzig, Germany. E-mail: {shekarpour,auer,ngonga,gerber,hellmann,stadler}@informatik.uni-leipzig.de Claus Stadler, Universität Leipzig, Institut für Informatik, D-04103 Leipzig, Germany. E-mail: {shekarpour,auer,ngonga,gerber,hellmann,stadler}@informatik.uni-leipzig.de Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2013

Description: Distributed data collections are now more and more common due to the emergence of cloud computing, to spatially decentralized businesses, or to the availability of various data sharing web services. Obtain knowledge in such a collection raises then the need of new data mining methods to apply in a decentralized architecture. In this paper, we explore a machine learning side of this work direction. We propose a novel technique for decentralized estimation of probabilistic mixture models, which are among the most versatile generative models for understanding data sets. More precisely, we demonstrate how to estimate a global mixture model from a set of local models. Our approach accommodates dynamic topology and data sources and is statistically robust, i.e. resilient to the presence of unreliable local models. Such outlier models may arise from local data which are outliers, compared to the global trend, or poor mixture estimation. We report experiments on synthetic data and real geo-location data from Flickr. Content Type Journal Article Pages 245-262 DOI 10.3233/WIA-130273 Authors Ali El-Attar, LINA (UMR CNRS 6241), Université de Nantes, Nantes, France. {ali.el-attar,marc.gelgon}@univ-nantes.fr Antoine Pigeau, LINA (UMR CNRS 6241), Université de Nantes, Nantes, France. {ali.el-attar,marc.gelgon}@univ-nantes.fr Marc Gelgon, LINA (UMR CNRS 6241), Université de Nantes, Nantes, France. {ali.el-attar,marc.gelgon}@univ-nantes.fr Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2013

Description: In this paper, we present an approach based on the principle that psychological capacities, especially personality traits, influence the decision making process of rational agents. Using a three-level (trait, facet, scheme) extension of the FFM/NEO PI-R taxonomy facilitating its computational implementation, we propose a model for the expression of personality traits in terms of so-called influence operators that add meta control rules to the cycle of rational BDI agents. We distinguish eight different classes of influence operators, depending on the step of the deliberation cycle that they influence and on the operand to which they are applied (goals, actions, plans or functions). Concrete examples are provided through a complete definition of the operators necessary to express one of the personality traits of the Five Factor Model, Conscientiousness, resulting in a set of 30 operators. Finally, we discuss the way to design a specific character through the use of an activation matrix, providing values for each scheme and operator. Content Type Journal Article Pages 203-220 DOI 10.3233/WIA-130271 Authors François Bouchet, SMART Laboratory, McGill University, 3700 McTavish, Montreal, Canada Jean Paul Sansonnet, LIMSI-CNRS, BP 133, 91403 Orsay Cedex, France. E-mail: jps@limsi.fr Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 3 / 2013

Description: Web Intelligence deals with intelligent methods and information & communication technologies that are integrated to enhance different web-based applications. Communities are popular, particularly on the World Wide Web, as a means for like-minded individuals to pursue common goals. Communities appear as a first-class object in the areas of web intelligence and agent technologies, as well as a crucial crossroads of several sub-domains (i.e. user modeling, protocols, data management, data mining, content modeling, etc.). These sub-domains impact the nature of the communities and the applications which are related to them. The use of Web Intelligence and communities is discussed together with ways in which a wide range of research is benefiting this area for the long-term. Content Type Journal Article Pages 299-302 DOI 10.3233/WIA-130276 Authors Rajendra Akerkar, Western Norway Research Institute (Vestlandsforsking) Sogndal, NO-6851, Norway. E-mail: rak@vestforsk.no Pierre Maret, Université de Lyon, CNRS, UMR5516, Laboratoire Hubert Curien, F-42000, Saint-Etienne, France Laurent Vercouter, Institut National de Sciences Appliquées de Rouen, 76801 Saint-Etienne-du-Rouvray Cedex, France. E-mail: laurent.vercouter@insa-rouen.fr Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2013

Description: Research in the Semantic Web, especially in modeling virtual communities, has provided models useful to represent the richness of these social network interactions. The SIOC (Semantically-Interlinked Online Communities) vocabulary provides concepts and properties that can be used to describe information from online communities (e.g., message boards, wikis, weblogs, etc.). However, the SIOC ontology does not consider social aspects nor the higher order semantics hidden in linkages between community members. This paper describes SIOC-SNA-DM, an extension of the SIOC vocabulary. SIOC-SNA-DM's model is tri-partite, consisting of People, Policies, and Purposes which are social aspects observable in most social communities. A challenge to using our model is how to populate these aspects, since higher order semantics from interactions need to be extracted. Thus, we explain how this population is done with advanced text mining using a latent semantic technique over a large virtual community called Plexilandia.cl with more than 2500 musicians working on the site. Our previous work, in this area, has shown how including these social aspects help to outperform results generated by state-of-the-art techniques. One of the novelties of this present work is the introduction and the elucidation of SIOC-SNA-DM, and how to populate the ontology in order to support the social aspects needed to enhance results of Social Network Mining techniques. Content Type Journal Article Pages 303-314 DOI 10.3233/WIA-130277 Authors Sebastián A. Ríos, Industrial Engineering Department, University of Chile, AV. República 701, Santiago, Chile Felipe Aguilera, Computer Science Department, University of Chile, AV. República 701, Santiago, Chile. E-mail: {faguiler,fbustos}@dcc.uchile.cl Francisco Bustos, Computer Science Department, University of Chile, AV. República 701, Santiago, Chile. E-mail: {faguiler,fbustos}@dcc.uchile.cl Tope Omitola, WAIS, School of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK. E-mail: {tobo,nrs}@ecs.soton.ac.uk Nigel Shadbolt, WAIS, School of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK. E-mail: {tobo,nrs}@ecs.soton.ac.uk Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 4 / 2013

Description: Recently, numerous successful approaches have been developed for instrument recognition in monophonic sounds. Unfortunately, none of them can be successfully applied to polyphonic sounds. Identification of music instruments in polyphonic sounds is still difficult and challenging. This has stimulated a number of research projects on music sound separation, new features development, and more recently on hierarchically structured classifiers used in content-based music recommender systems. This paper introduces a hierarchically structured cascade classification system to estimate multiple timbre information from the polyphonic sound by classification which is based on acoustic features and short-term power spectrum matching. This cascade system makes a first estimate on the higher level decision attribute which stands for the musical instrument family. Then, the further estimation is done within that specific family range. Experiments showed better performance of a hierarchical system than the traditional flat classification method which directly estimates the instrument without higher level of family information analysis. Traditional hierarchical structures were constructed in human semantics, which are meaningful from human perspective but not appropriate for a cascade system. We introduce a new hierarchical instrument schema according to the clustering results of the acoustic features. This new schema better describes the similarity among different instruments or among different playing techniques of the same instrument. The classification results show the higher accuracy of cascade system with the new schema compared to the traditional schemas. Content Type Journal Article Pages 149-170 DOI 10.3233/WIA-130268 Authors Wenxin Jiang, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. E-mail: wjiang2@fhcrc.org Zbigniew W. Ras, Department of Computer Science, University of North Carolina, Charlotte, NC 28223, USA Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2013

Description: A human's performance in a complex task is highly dependent on the demands of the task, in the sense that highly demanding situations will often cause a degradation of performance. To maintain performance quality usually extra effort has to be contributed. However, the resources for such extra effort available to the human are limited. In this paper an agent model is proposed in which different types of relations between effort, task demands and performance quality can be used to analyse the human's performance quality. It is illustrated how a support agent incorporating this model can support a human based on different performance criteria. The agent model thus allows to build agent applications that provide optimal support depending on a specific situation and goal of the task. Content Type Journal Article Pages 171-184 DOI 10.3233/WIA-130269 Authors Michel C.A. Klein, Department of Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands Rianne M. van Lambalgen, Department of Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands Jan Treur, Department of Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2013

Description: With the tremendous development of information technology, the volume of data in digital libraries is increasing enormously, and the magnitude is putting users at risk of information overload. Personalized search aims at solving the problem by tailoring search results for individual demands. We propose a novel approach to provide personalized search by learning users' interests from historic behaviors and re-ranking search results by a Spreading Activation (SA) model. In our approach, users' interests are categorized by the level of recency to form user profiles, which in turn serve as the input of the SA model. Then, SA runs on the domain ontology incorporated with a newly defined relationship borrowIntent derived from the assumption of collaborative filtering. We further demonstrate the effectiveness of the proposed methodology through experiments on real data from a university library. The presented approach can also be applied in other contexts such as electronic commerce. Content Type Journal Article Pages 137-147 DOI 10.3233/WIA-130267 Authors Tao Sun, School of Electronics Engineering and Computer Science, Peking University, NO.5 Yiheyuan Road, Haidian District, Beijing, 100871, P.R. China. E-mail: {suntao,mzhang,yanfei}@net.pku.edu.cn, zhdeng@cis.pku.edu.cn Ming Zhang, School of Electronics Engineering and Computer Science, Peking University, NO.5 Yiheyuan Road, Haidian District, Beijing, 100871, P.R. China. E-mail: {suntao,mzhang,yanfei}@net.pku.edu.cn, zhdeng@cis.pku.edu.cn Fei Yan, School of Electronics Engineering and Computer Science, Peking University, NO.5 Yiheyuan Road, Haidian District, Beijing, 100871, P.R. China. E-mail: {suntao,mzhang,yanfei}@net.pku.edu.cn, zhdeng@cis.pku.edu.cn Zhihong Deng, School of Electronics Engineering and Computer Science, Peking University, NO.5 Yiheyuan Road, Haidian District, Beijing, 100871, P.R. China. E-mail: {suntao,mzhang,yanfei}@net.pku.edu.cn, zhdeng@cis.pku.edu.cn Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2013

Description: The Internet has become for many the most important medium for staying informed about current news events. Some events cause heightened interest on a topic, which in turn yields a higher frequency of the search queries related to it. These queries are going through a “query burst”. In this paper we examine the behavior of search engine users during a query burst, compared to before and after the burst. We are interested in how this behavior changes, and how it affects other stake-holders in web search. We analyze one year of web-search and news-search logs, looking at query bursts from multiple perspectives. First, we adopt the perspective of search engine users, describing changes in their effort and interest while searching. Second, we adopt the perspective of news providers by comparing web search and news search query bursts. Third, we look at the burst from the perspective of content providers. We study the conditions under which content providers can “ride” a wave of increased interest on a topic, and obtain a share of the user's increased attention. We do so by identifying the class of queries that can be considered as an opportunity for content providers that are “late-comers” for a query, in the sense of not being among the first to write about its topic. We also present a simple model for predicting the click share content providers could obtain if they decide to provide content about these queries. Content Type Journal Article Pages 107-124 DOI 10.3233/WIA-130265 Authors Ilija Subašić, Department of Computer Science, K.U. Leuven, Celestijenlaan 200a, 3001, Leuven-Heverlee, Belgium. E-mail: ilija.subasic@cs.kuleuven.be Carlos Castillo, Yahoo! Research, Diagonal 177 8th floor, 08018 Barcelona, Catalonia, Spain. E-mail: chato@acm.org Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2013

Description: It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems. In this paper, we argue that multiagent meta-level control is an effective way to determine when this adaptation process should be done and how much effort should be invested in adaptation as opposed to continuing with the current action plan. We use a reinforcement learning based local optimization algorithm within each agent to learn multiagent meta-level control agent policies in a decentralized fashion. These policies will allow each agent to adapt to changes in environmental conditions while reorganizing the underlying multiagent network when needed. We then augment the agent with a heuristic rule-based algorithm that uses information provided by the reinforcement learning algorithm in order to resolve conflicts among agent policies from a local perspective at both learning and execution stages. We evaluate this mechanism in the context of a multiagent tornado tracking application called NetRads. Empirical results show that adaptive multiagent meta-level control significantly improves the performance of the tornado tracking network for a variety of weather scenarios. Content Type Journal Article Pages 81-105 DOI 10.3233/WIA-130264 Authors Shanjun Cheng, Department of Software and Information Systems, The University of North Carolina at Charlotte, Charlotte, NC, 28223, USA. E-mail: chengshanjun@gmail.com, anraja@uncc.edu Anita Raja, Department of Software and Information Systems, The University of North Carolina at Charlotte, Charlotte, NC, 28223, USA. E-mail: chengshanjun@gmail.com, anraja@uncc.edu Victor Lesser, Department of Computer Science, University of Massachusetts Amherst, Amherst, MA, 01003, USA. E-mail: lesser@cs.umass.edu Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2013

Description: Large-scale systems are part of a growing trend in distributed computing, and coordinating control of them is an increasing challenge. This paper presents a cooperative agent system that scales to one million or more nodes in which agents form coalitions to complete global task objectives. This approach uses the large-scale Command and Control (C2) capabilities of the Resource Clustered Chord (RC-Chord) Hierarchical Peer-to-Peer (HP2P) design. Tasks are submitted that require access to processing, data, or hardware resources, and a distributed agent search is performed to recruit agents to satisfy the distributed task. This approach differs from others by incorporating design elements to accommodate large-scale systems into the resource location algorithm. Peersim simulations demonstrate that the distributed coalition formation algorithm is as effective as an omnipotent central algorithm in a one million agent system. Content Type Journal Article Pages 67-79 DOI 10.3233/WIA-130263 Authors Daniel R. Karrels, Air Force Research Laboratory, Directed Energy Directorate, 3550 Aberdeen Ave SE, Kirtland AFB, Albuquerque, NM, USA Gilbert L. Peterson, Department of Electrical and Computer Engineering, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH, USA Barry E. Mullins, Department of Electrical and Computer Engineering, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH, USA Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2013

Description: Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource, such as web-page. In this paper we investigate the influence of tag suggestions on the emergence of power-law distributions as a result of collaborative tag behavior. Although previous research has already shown that power-laws emerge in tagging systems, the cause of why power-law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power-laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. This imitation behavior leads to a feedback loop in which some tags are reinforced, which is also known as the ‘rich get richer’ or a preferential attachment model. We present experimental results showing that the power-law distribution forms when tag suggestions are not presented to the users, and the power-law distribution does not hold when tag suggestions are presented to the user. Furthermore, we show that the real effect of tag suggestions is rather subtle; the power-law distribution that would naturally occur without tag suggestions is ‘compressed’ if tag suggestions are given to the user, resulting in a shorter long tail and a ‘compressed’ top of the power-law distribution. We show that tag suggestions by themselves do not account for the formation of power-law distributions in tagging systems. Content Type Journal Article Pages 41-53 DOI 10.3233/WIA-130261 Authors Dirk Bollen, Faculty of Innovation Sciences and Industrial Engineering, Eindhoven University of Technology, Den Dolech 2, Postbus 513, 5600 MB Eindhoven, The Netherlands. E-mail: d.g.f.m.bollen@tue.nl Harry Halpin, School of Informatics, University of Edinburgh, United Kingdom. E-mail: H.Halpin@ed.ac.uk Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2013

Description: The real-world continuous double auction (CDA) market is a dynamic environment. However, most of the existing agent bidding strategies are simply designed for static markets. A new detecting method for bidding strategy is necessary for more practical simulations and applications. In this paper, we present a novel agent-based computing approach called the GDX Plus (GDXP) model. In the proposed model, trades are decided according to the market events in history combined with the forecast of market trends. The GDXP model employs a dynamic adjustment mechanism to make the bidding strategy adapt to the shocks in a dynamic environment. The experimental results of the comparison between GDXP and other typical models, with respect to both static and dynamic CDA markets, demonstrate the performance of the GDXP model. Content Type Journal Article Pages 55-65 DOI 10.3233/WIA-130262 Authors Zhongyi Zhang, College of Computer Science and Technology, Jilin University, No. 2699, Qianjin street, Changchun, China Yanbin Liu, College of Computer Science and Technology, Jilin University, No. 2699, Qianjin street, Changchun, China Jiguang Li, College of Computer Science and Technology, Jilin University, No. 2699, Qianjin street, Changchun, China Bo Yang, College of Computer Science and Technology, Jilin University, No. 2699, Qianjin street, Changchun, China Fanhua Yu, College of Computer Science and Technology, Changchun Normal University, No. 677, Changji street, Changchun, China Dayou Liu, College of Computer Science and Technology, Jilin University, No. 2699, Qianjin street, Changchun, China Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2013

Description: Graph clustering, or community detection, is an important task of discovering the underlying structure in a network by clustering vertices in a graph into communities. In the past decades, non-overlapping methods such as normalized cuts and modularity-based methods, which assume that each vertex belongs to a single community, are proposed to discover disjoint communities. On the other hand, overlapping methods such as CPM, which assume that each vertex can belong to multiple communities, have been drawing increasing attention as the assumption fits the reality. In this paper, we show that existing non-overlapping and overlapping methods lack consideration to edges that link a vertex to its neighbors belonging to different communities, which often leads to counter-intuitive results of vertices located near borders of communities. Therefore, we propose a new graph clustering methods named RoClust, which uses three roles, bridges, gateways and hubs to discover communities. Each of the three roles represents a kind of vertices that connect communities. Experimental results show that RoClust outperforms state-of-the-art methods of graph clustering including non-overlapping and overlapping methods. Content Type Journal Article Pages 1-20 DOI 10.3233/WIA-130259 Authors Bin-Hui Chou, Department of Informatics, ISEE, Kyushu University, Fukuoka 8190395, Japan. E-mail: {chou,suzuki}@i.kyushu-u.ac.jp Einoshin Suzuki, Department of Informatics, ISEE, Kyushu University, Fukuoka 8190395, Japan. E-mail: {chou,suzuki}@i.kyushu-u.ac.jp Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2013

Description: Within human trust related behaviour, according to the literature from the domains of Psychology and Social Sciences often non-rational behaviour can be observed. Current trust models that have been developed typically do not incorporate non-rational elements in the trust formation dynamics. In order to enable agents that interact with humans to have a good estimation of human trust, and take this into account in their behaviour, trust models that incorporate such human aspects are a necessity. A specific non-rational element in humans is that they are often biased in their behaviour. In this paper, models for human trust dynamics are presented incorporating human biases. In order to show that they more accurately describe human behaviour, they have been evaluated against empirical data, which shows that the models perform significantly better. Content Type Journal Article Pages 21-40 DOI 10.3233/WIA-130260 Authors Mark Hoogendoorn, Agent Systems Research Group, VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands. E-mail: {mhoogen,swjaffry,treur}@few.vu.nl Syed Waqar Jaffry, Agent Systems Research Group, VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands. E-mail: {mhoogen,swjaffry,treur}@few.vu.nl Peter-Paul van Maanen, Agent Systems Research Group, VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands. E-mail: {mhoogen,swjaffry,treur}@few.vu.nl Jan Treur, Agent Systems Research Group, VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands. E-mail: {mhoogen,swjaffry,treur}@few.vu.nl Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 1 / 2013

Description: Online auctions have provided an alternative trading method to exchange items without the geographical and time constraints. However, buyers would face difficulties in searching, monitoring, and selecting an auction to participate in. As a consequence, agent technology is introduced to overcome these pitfalls. In this paper, heterogeneous intelligent agents and heterogeneous standard bidders are generated in a simulated auction market and their performances are measured. By doing so, it would further simulate the real online auction marketplace where bidders may have different bidding behaviors or implement different bidder agents. From the simulated results, the average winner's utility, the average number of winning auctions, the average closing price and the average median consumer surplus ratio are used to evaluate the winners' performances. From the results obtained, it is generalized that by using intelligent bidder agents to participate in online auctions, it benefits the bidders. Besides that, market economy is reviewed based on the results obtained. Content Type Journal Article Pages 185-199 DOI 10.3233/WIA-130270 Authors Jacob Sow, School of Engineering and Information Technology, Universiti Malaysia Sabah, 88400, Kota Kinabalu, Sabah, Malaysia Patricia Anthony, School of Engineering and Information Technology, Universiti Malaysia Sabah, 88400, Kota Kinabalu, Sabah, Malaysia Chong Mun Ho, School of Science and Technology, Universiti Malaysia Sabah, 88400, Kota Kinabalu, Sabah, Malaysia Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2013

Description: Description Logic possesses strong knowledge representation and reasoning capabilities and offers logical foundation for Semantic Web ontology languages such as OWL and OWL-S. However, the present implementations of OWL and OWL-S are deficient in semantic modeling for dynamic service composition. They also do not consider user preferences. AI planning possesses a better modeling capability of action state transformations in planning and provides an effective method for solving planning problem and task decomposition. However, it is limited in knowledge representation and reasoning capabilities. Based on merits of Description Logic, OWL-S and AI planning, this paper extends OWL-S model, proposes a service composition mechanism and testifies its feasibility in Description Logic. The results show that this composition mechanism is not only feasible but also helpful for semantic modeling of service composition in Semantic Web. Content Type Journal Article Pages 125-135 DOI 10.3233/WIA-130266 Authors Yingjie Li, College of Computer and Software, Taiyuan University of Technology, P.R. China Xueli Yu, College of Computer and Software, Taiyuan University of Technology, P.R. China Rui Wang, College of Computer and Software, Taiyuan University of Technology, P.R. China Lili Geng, College of Computer and Software, Taiyuan University of Technology, P.R. China Li Wang, College of Computer and Software, Taiyuan University of Technology, P.R. China Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 11 Journal Issue Volume 11, Number 2 / 2013

Description: Today, enterprises have to be customer and service oriented in order to adapt themselves to a more and more competitive and evolving context. Their innovators (actors of the innovation) have to collaborate in order to anticipate the customers' needs. To do so, they work as a virtual team (a remote and inter-professional network) and try to describe services situations, to detect lacks and opportunities, and to propose the design of adapted solutions. Based on the service literature and the discussions of the Service Science Management and Engineering (SSME) discipline, we conceive the service as a dynamic configuration of heterogeneous entities: a “Service System”. We then suggest formalizing it with a mereological ontology (collective classes instead of distributive classes) of processual entities (processes instead of substances) in order to highlight its dynamic dimension. The Service System acts as an intermediary object aiming at the innovators' cooperation and creativity during a co-design phase. Its modelling reveals all the possible experiences within a situation through the identification of the relationships between processual entities. A co-modelling process and a web-based tool are currently tested upstream of the services design process of a French telecom operator. This operator's process and experiments are good examples. Content Type Journal Article Pages 247-260 DOI 10.3233/WIA-2012-0244 Authors Florie Bugeaud Eddie Soulier Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2012

Description: This paper proposes an approach which aims at improving information access (skills, services) in large networks. It proposes a self-organized multiagent analysis of the problem allowing to reduce the number of messages transmitted for a skill search. The MWAC (Multi-Wireless-Agent Communication) model is extended to take some specificities of social networks into account, like the information held by the social network members and their connections with the others. The network of agents may represent a social network. Each agent holds information about members (agents) and is linked with other agents which represent its neighborhood. Each member builds a local network representation according to its needs. A global view of a social network is not dynamically available and furthermore not desirable. Our approach is thus based on a decentralized interrogation of the network and on organizational structures detection. With this method, a search does not need to flood all the network with the query because it uses the self-organized structure that emerges so, it can avoid a saturation in the network and it reduces the amount of transmitted messages. In order to show the interest of the approach, our proposition is validated by several simulations. The applications of this work are related to skill searching in a virtual social network. Content Type Journal Article Pages 235-246 DOI 10.3233/WIA-2012-0243 Authors Annabelle Mercier Michel Occello Jean-Paul Jamont Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2012

Description: Existing works about user profile modeling always model the user as an independent entity. However, in social sciences, many works show the user's behavior as strongly influenced by his social ties and/or social interactions. These results were difficult to integrate in user profile modeling because of the problem of capturing social data about users in infor-mation systems. With the advent of the social web for example, this barrier can be broken on the Web, and we can reasonably think about enriching users' profiles from social data. In this paper we propose a technique to develop users' profiles with social data. We built profiles from textual users' activities data, and we show through visualization of temporal graphs the relevance of social ties on built profiles (with an experimentation carried out on 7, 081 Facebook profiles). These results motivated the potential implementation of new profiling techniques based on users' social networks, to enrich users' profiles and solve pending problems such as ‘cold start problem’ in personalized or recommender systems. Content Type Journal Article Pages 261-274 DOI 10.3233/WIA-2012-0245 Authors Dieudonné Tchuente Marie-Françoise Canut Nadine Baptiste Jessel André Péninou Florence Sèdes Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2012

Description: It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance. Content Type Journal Article Pages 151-163 DOI 10.3233/WIA-2012-0238 Authors Abdulmohsen Algarni, School of Electrical Engineering and Computer Science, Faculty of Science and Engineering, Queensland University of Technology, Brisbane Qld 4001, Australia. E-mail: {a1.algarni,y2.li,yue.xu}@qut.edu.au Yuefeng Li, School of Electrical Engineering and Computer Science, Faculty of Science and Engineering, Queensland University of Technology, Brisbane Qld 4001, Australia. E-mail: {a1.algarni,y2.li,yue.xu}@qut.edu.au Sheng-Tang Wu, Dept. of Applied Informatics and Multimedia, Asia University, Taichung, Taiwan. E-mail: swu@asia.edu.tw Yue Xu, School of Electrical Engineering and Computer Science, Faculty of Science and Engineering, Queensland University of Technology, Brisbane Qld 4001, Australia. E-mail: {a1.algarni,y2.li,yue.xu}@qut.edu.au Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2012

Description: Special Section. Introducing Web Intelligence for CommunitiesPart 2 of 2 Content Type Journal Article Pages 233-233 Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 2 / 2012

Description: This paper considers the problem of learning task specific web-service descriptions from traces of users successfully completing a task. Unlike prior approaches, we take a traditional machine-learning perspective to the construction of web-service models from data. Our representation models both syntactic features of web-service schemas (including lists and optional elements), as well as semantic relations between objects in the task. Together, these learned models form a full schematic model of the dataflow. Our theoretical results, which are the main novelty in the paper, show that this structure can be learned efficiently: the number of traces required for learning grows polynomially with the size of the task. We also present real-world task descriptions mined from tasks using online services from Amazon and Google. Content Type Journal Article Pages 397-421 DOI 10.3233/WIA-2012-0254 Authors Thomas J. Walsh, Accenture Technology Labs, 161 N. Clark St., Chicago, IL 60601, USA. E-mail: thomasjwalsh@gmail.com Michael L. Littman, Department of Computer Science, Rutgers University, 110 Frelinghuysen Rd., Piscataway, NJ 08854, USA. E-mail: {mlittman,borgida}@cs.rutgers.edu Alexander Borgida, Department of Computer Science, Rutgers University, 110 Frelinghuysen Rd., Piscataway, NJ 08854, USA. E-mail: {mlittman,borgida}@cs.rutgers.edu Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 4 / 2012

Description: Virtual training systems with intelligent agents provide an effective means to train people for complex, dynamic tasks like crisis management or firefighting. For successful training, intelligent virtual agents should be able to show believable behavior, adapt their behavior to the trainee's performance and give useful explanations about their behavior. Agents can provide more believable behavior and explanations if they, besides their own, take the assumed knowledge and intentions of other players in the scenario into account. This paper proposes two ways to model agents with a theory of mind, i.e. equip them with the ability to ascribe mental concepts such as knowledge and intentions to others. The first theory of mind model is based on theory–theory (TT) and the second on simulation theory (ST). In a simulation study, agents with no theory of mind, a TT-based theory of mind, and an ST-based theory of mind are compared. The results show that agents with a theory of mind are preferred over agents with no theory of mind, and that, regarding agent development, the ST model has advantages over the TT model. Content Type Journal Article Pages 331-343 DOI 10.3233/WIA-2012-0250 Authors Maaike Harbers, Information and Computing Sciences, Utrecht University, P.O.Box 80.089, 3508 TB, Utrecht, The Netherlands. E-mail: {maaike,jj}@cs.uu.nl Karel Van den Bosch, Training Innovations, TNO Human Factors, P.O.Box 23, 3769 ZG, Soesterberg, The Netherlands. E-mail: karel.vandenbosch@tno.nl John-Jules Meyer, Information and Computing Sciences, Utrecht University, P.O.Box 80.089, 3508 TB, Utrecht, The Netherlands. E-mail: {maaike,jj}@cs.uu.nl Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2012

Description: In this paper, we evaluate whether the robustness of a market mechanism that allocates complementary resources could be improved through the aggregation of time periods in which resources are consumed. In particular, we study a multi-round combinatorial auction that is built on a general equilibrium framework. We adopt the general equilibrium framework and the particular combinatorial auction design from the literature, and we investigate the benefits and the limitation of time-period aggregation when demand-side uncertainties are introduced. By using simulation experiments on a real-life resource allocation problem from a container port, we show that, under stochastic conditions, the performance variation of the process decreases as the time frame length (time frames are obtained by aggregating time periods) increases. This is achieved without causing substantial deterioration in the mean performance. The main driver for the increase in robustness is that longer time frames result in allocations where resources are assigned in longer contiguous time blocks. The resulting resource continuity allows bidders to shift schedules upon realization of stochasticity. To demonstrate the generality of the notion that resource continuity increases allocation robustness, we perform further experiments on a decentralized variant of the classical job shop scheduling problem. The experiment results demonstrate similar benefits. Content Type Journal Article Pages 305-318 DOI 10.3233/WIA-2012-0248 Authors Shih-Fen Cheng, School of Information Systems, Singapore Management University, 80 Stamford Road, Singapore 178902, Singapore. E-mail: {sfcheng,jtajan,hclau}@smu.edu.sg John Tajan, School of Information Systems, Singapore Management University, 80 Stamford Road, Singapore 178902, Singapore. E-mail: {sfcheng,jtajan,hclau}@smu.edu.sg Hoong Chuin Lau, School of Information Systems, Singapore Management University, 80 Stamford Road, Singapore 178902, Singapore. E-mail: {sfcheng,jtajan,hclau}@smu.edu.sg Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2012

Description: Semantic Wikipedia aims to enhance Wikipedia by adding explicit semantics to links between Wikipedia entities. However, we have observed that it currently suffers the following limitations: lack of semantic annotations and lack of semantic annotators. In this paper, we resort to relation population to automatically extract relations between any entity pair to enrich semantic data, and predicate suggestion to recommend proper relation labels to facilitate semantic annotating. Both tasks leverage relation classification which tries to classify extracted relation instances into predefined relations. However, due to the lack of labeled data and the excessiveness of noise in Semantic Wikipedia, existing approaches cannot be directly applied to these tasks to obtain high-quality annotations. In this paper, to tackle the above problems brought by Semantic Wikipedia, we use a label propagation algorithm and exploit semantic features like domain and range constraints on categories as well as linguistic features such as dependency trees of context sentences in Wikipedia articles. The experimental results on 7 typical relation types show the effectiveness and efficiency of our approach in dealing with both tasks. Content Type Journal Article Pages 319-330 DOI 10.3233/WIA-2012-0249 Authors Haofen Wang, Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China. E-mail: {whfcarter,fulinyun,yyu}@apex.sjtu.edu.cn Linyun Fu, Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China. E-mail: {whfcarter,fulinyun,yyu}@apex.sjtu.edu.cn Yong Yu, Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China. E-mail: {whfcarter,fulinyun,yyu}@apex.sjtu.edu.cn Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 10 Journal Issue Volume 10, Number 3 / 2012