ALBERT

Description: A new efficient heuristic algorithm has been developed for the dynamic facility layout problem with budget constraint (DFLPB) using optimization via simulation technique. The heuristic integrates integer programming and discrete event simulation to address DFLPB. In the proposed algorithm, the nonlinear model of the DFLP has been changed to a pure integer programming (PIP) model. Then, the optimal solution of the PIP model has been used in a simulation model that has been designed in a similar manner as the DFLP for determining the probability of assigning a facility to a location. After a sufficient number of runs, the simulation model obtains near optimum solutions. Finally, to test the performance of the algorithm, several test problems have been taken from the literature and solved. The results show that the proposed algorithm is more efficient in terms of speed and accuracy than other heuristic algorithms presented in previous works.

Description: Flickermeter is a common name for a system that measures the obnoxiousness of flicker caused by voltage fluctuations. The output of flickermeter is a value of short-term flicker severity indicator, . This paper presents the results of the numerical simulations that reconstruct the processing of flickermeter in frequency domain. With the use of standard test signals, the characteristics of flickermeter were determined for the case of amplitude modulation of input signal, frequency modulation of input signal, and for input signal with interharmonic component. For the needs of simulative research, elements of standard IEC flickermeter signal chain as well as test signal source and tools for acquisition, archiving, and presentation of the obtained results were modeled. The results were presented with a set of charts, and the specific fragments of the charts were pointed out and commented on. Some examples of the influence of input signal’s bandwidth limitation on the flickermeter measurement result were presented for the case of AM and FM modulation. In addition, the diagrams that enable the evaluation of flickermeter’s linearity were also presented.

Description: Combining probability and first-order logic has been the subject of intensive research during the last 10 years. This artical introduces first-order probabilistic conditional logic (FO-PCL), a first-order extension of a propositional probabilistic logic formalism, which allows for the adequate representation of probabilistic if - then -rules. We demonstrate that our novel formalism allows to represent uncertain knowledge that cannot easily be represented by other formalisms combining first-order logic and probability. Furthermore, as the representation of the models of FO-PCL requires solving a complex entropy-optimization problem, we develop syntactic conditions for its simplification.

Description: This article presents KR eator , a versatile integrated development environment for probabilistic inductive logic programming currently under development. The area of probabilistic inductive logic programming (or statistical relational learning) aims at applying probabilistic methods of inference and learning in relational or first-order representations of knowledge. In the past ten years the community brought forth a lot of proposals to deal with problems in that area, which mostly extend existing propositional probabilistic methods like Bayes Nets and Markov Networks on relational settings. Only few developers provide prototypical implementations of their approaches and the existing applications are often difficult to install and to use. Furthermore, due to different languages and frameworks used for the development of different systems the task of comparing various approaches becomes hard and tedious. KR eator aims at providing a common and simple interface for representing, reasoning and learning with different relational probabilistic approaches. It is a general integrated development environment which enables the integration of various frameworks within the area of probabilistic inductive logic programming and statistical relational learning. Currently, KR eator implements Bayesian logic programs, Markov logic networks and relational maximum entropy under grounding semantics. More approaches will be implemented in the near future or can be implemented by researchers themselves as KR eator is open-source and available under public license. In this article, we provide some background on probabilistic inductive logic programming and statistical relational learning and illustrate the usage of KR eator on several examples using the three approaches currently implemented in KR eator . Furthermore, we give an overview on its system architecture.

Description: A special feature of programs in the action language Golog are non-deterministic constructs such as non-deterministic choice of actions or arguments. It has been shown that in the presence of stochastic actions and rewards reinforcement learning techniques can be applied to obtain optimal choices for those choice-points. In order to avoid an explosion of the state space, an abstraction mechanism is employed that computes first-order state descriptions for the given program. Intuitively, the idea is to generate abstract descriptions that group together states for which the expected reward of executing the program is the same. A current limitation is that a non-deterministic choice of arguments can be handled only if the possible candidates are known in advance. In this article we show how this restriction can be lifted. We also show how a first-order variant of binary decision diagrams can be used to efficiently compute first-order state abstractions. Moreover, we give a completely declarative specification of a learning Golog interpreter that incorporates the presented state-abstraction mechanisms.

Description: Log A B is a family of logics of belief. It holds a middle ground between the expressive, but prone to paradox, syntactical first-order theories and the often inconvenient, but safe, modal approaches. In this report, the syntax and semantics of Log A B are presented. Log A B is algebraic in the sense that it is a language of only terms; there is no notion of a formula, only proposition-denoting terms. The domain of propositions is taken to be a Boolean algebra, which renders classical truth conditions and definitions of consequence and validity theorems about Log A B structures. Log A B is shown to be sufficiently expressive to accommodate complex patterns of reasoning about belief while remaining paradox-free. A number of results are proved regarding paradoxical self-reference. They are shown to strengthen previous results, and to point to possible new approaches to circumventing paradoxes in syntactical theories of belief.

Description: The principle of maximum entropy has proven to be a powerful approach for commonsense reasoning in probabilistic conditional logics on propositional languages. Due to this principle, reasoning is performed based on the unique model of a knowledge base that has maximum entropy. This kind of model-based inference fulfils many desirable properties for inductive inference mechanisms and is usually the best choice for reasoning from an information theoretical point of view. However, the expressive power of propositional formalisms for probabilistic reasoning is limited and in the past few years many proposals have been given for probabilistic reasoning in relational settings. It seems to be a common view that in order to interpret probabilistic first-order sentences, either a statistical approach that counts (tuples of) individuals has to be used, or the knowledge base has to be grounded to make a possible worlds semantics applicable, for a subjective interpretation of probabilities. Most of these proposals of the second type rely on extensions of traditional probabilistic models like Bayes nets or Markov networks whereas there are only few works on first-order extensions of probabilistic conditional logic. Here, we take an approach of lifting maximum entropy methods to the relational case by employing a relational version of probabilistic conditional logic. First, we propose two different semantics and model theories for interpreting first-order probabilistic conditional logic. We address the problems of ambiguity that are raised by the difference between subjective and statistical views, and develop a comprehensive list of desirable properties for inductive model-based probabilistic inference in relational frameworks. Finally, by applying the principle of maximum entropy in the two different semantical frameworks, we obtain inference operators that fulfill these properties and turn out to be reasonable choices for reasoning in first-order probabilistic conditional logic.

Description: In this work, we present a framework based on the notion of dependencies. We use this framework to define semantics for inconsistent belief bases in a modular way to define general tools for handling inconsistency. We consider belief bases represented by non-monotonic formalisms, and in particular use extended logic programs and belief bases represented by sequences of these. We show the presented frameworks appliance with the answer set semantics for consistent belief bases. Moreover, we define various instantiations of the framework and show relations to other approaches. We present ways to improve the resulting semantics by means of changes to modules of the framework that lead to the definition of improved approaches to conflict handling in logic programming-based knowledge bases.

Description: The world production of crude oil is about 3 billion tons per year. The overall objective of the model in present study is supporting the decision makers in planning and conducting preventive and emergency interventions. The conservative equation for the slick dynamics was derived from layer-averaged Navier-Stokes (LNS) equations, averaged over the slick thickness. Eulerian approach is applied across the model, based on nonlinear shallow water Reynolds-averaged Navier-Stokes (RANS) equations. Depth-integrated standard k-ε turbulence schemes have been included in the model. Wetting and drying fronts of intertidal zone and moving boundary are treated within the numerical model. A highly accurate algorithm based on a fourth-degree accurate shape function has been used through an alternating-direction implicit (ADI) scheme which separates the operators into locally one-dimensional (LOD) components. The solution has been achieved by the application of KPENTA algorithm for the set of the flow equations which constitutes a pentadiagonal matrix. Hydrodynamic model was validated for a channel with a sudden expansion in width. For validation of oil spill model, predicted results are compared with experimental data from a physical modeling of oil spill in a laboratory wave basin under controlled conditions.