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  • bic Book Industry Communication::M Medicine
  • machine learning
  • Springer  (59)
  • Taylor & Francis
  • 1995-1999  (59)
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  • Springer  (59)
  • Taylor & Francis
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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    OR spectrum 17 (1995), S. 55-66 
    ISSN: 1436-6304
    Keywords: Local search ; simulated annealing ; tabu search ; genetic algorithms ; machine learning ; knowledge based information systems ; Lokale Suche ; Simulated Annealing ; Tabu Search ; Genetische Algorithmen ; Maschinelles Lernen ; Wissensbasierte Informationssysteme
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Description / Table of Contents: Zusammenfassung Aus drei Gründen stellen wir diesem Sonderheft „Applied Local Search“ ein erweitertes Vorwort voran. Erstens sollen die mittlerweile bereits als klassisch zu bezeichnenden Strukturen und Ideen von dem, was derzeit unter lokaler Suche verstanden wird, vorgestellt werden. Simulated Annealing, Tabu Search and Genetische Algorithmen werden somit in ihren Grandelementen beschrieben, wobei der Schwerpunkt bewußt auf Tabu Search liegt, das sich derzeit als beste Strategie zur Lösung kombinatorischer Optimierungsprobleme etabliert hat. Neuere und mittlerweile sehr erfolgreiche aber immer noch wenig bekannte Ideen, wie die Reverse Elimination Methode und Ejection Chains, werden ebenfalls im Rahmen von Tabu Search vorgestellt. Zweites Anliegen ist, die Einbettung von lokalen Suchverfahren in einem allgemeineren Kontext wissensbasierter Informationssysteme zu beschreiben. Lokale Suche wird dabei als ein Paradigma maschinellen Lernens betrachtet. Schließlich soll dieses Vorwort ebenfalls einen kurzen Überblick der in diesem Heft enthaltenen Arbeiten geben und sie aufgrund der Verfahren und Modelle gruppieren.
    Notes: Abstract The idea of this extended foreword to the special issue on applied local search is threefold. Firstly, we provide a brief and fundamental description of what is nowadays called local search. Components which have meanwhile become an integral part of the classical aspects on simulated annealing, tabu search and genetic algorithms are reviewed. Furthermore, today tabu search can be considered as the major pillar of local search. Hence, attention is drawn to a couple of tabu search issues more recently developed such as the reverse elimination method and ejection chains. Secondly, local search based knowledge engineering is developed to constitute a substantial part of knowledge based information systems. Within this general setting local search will be considered as one particular paradigm of machine learning. Thirdly, we are going to introduce what is considered to be the main subject of this issue, local search applications. We briefly embed the contents of the subsequent papers and group them with respect to their particular methods and models within the above mentioned framework.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 161-166 
    ISSN: 1572-8145
    Keywords: Autonomous agents ; behaviours ; self-organization ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract During the last few years, in an attempt to provide an efficient alternative to classical methods to designing robot control structures, the behaviour-based approach has emerged. Its success has largely been a result of the bottom-up development of a number of fast, tightly coupled control processes. This new approach, however, has some important limitations because of its lack of goal directedness and flexibility. This paper describes a self-improving control system that would deal with some of these problems. The system is based on two levels of arbitration, a local level which enables the robot to survive in a particular real-world situation, and a global level which ensures that the robot reactions be consistent with the required goal. Emphasis is put on the local arbitration level: it is shown how the local priorities can be computed and learnt and some simulation results are presented.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1572-8145
    Keywords: Neural networks ; quality engineering ; sensor integration ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Artificial neural networks have been shown to have a lot of potential as a means of integrating multi-sensor signals for real-time monitoring of machining processes. However, many questions still remain to be answered on how to optimize the training parameters during the training phase to optimize their subsequent performance, especially in view of the fact that the few published articles have made conflicting recommendations. This paper presents a systematic evaluation of the individual effects of training parameters — learning rate, momentum rate, number of hidden layer nodes, transfer function and learning rule-on the performance of back-propagation networks used for predicting quality characteristics of end-milled parts. Multi-sensor signatures (acoustic emission, spindle vibration, cutting force components and machining time) acquired during circular end-milling of 4140 steel and the corresponding measured quality characteristics (surface roughness and bore tolerance) were used to train the networks. The network is part of a proposed intelligent machining monitoring and diagnostic system for quality assurance of machined parts. The network performances were evaluated using four different criteria: maximum error, rms error, mean error and number of training cycles. One of the results obtained shows that the hyperbolic tangent transfer function gives a better performance than the sigmoid and sine functions respectively. Optimum combinations of training parameters have been observed. The effects of various combinations of training parameters are presented.
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 323-329 
    ISSN: 1572-8145
    Keywords: Intelligent manufacturing ; machine learning ; neuro–fuzzy systems ; genetic algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Intelligence is strongly connected with learning adapting abilities, therefore such capabilities are considered as indispensable features of intelligent manufacturing systems (IMSs). A number of approaches have been described to apply different machine learning (ML) techniques for manufacturing problems, starting with rule induction in symbolic domains and pattern recognition techniques in numerical, subsymbolic domains. In recent years, artificial neural network (ANN) based learning is the dominant ML technique in manufacturing. However, mainly because of the ‘black box’ nature of ANNs, these solutions have limited industrial acceptance. In the paper, the integration of neural and fuzzy techniques is treated and former solutions are analysed. A genetic algorithm (GA) based approach is introduced to overcome problems that are experienced during manufacturing applications with other algorithms.
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 331-338 
    ISSN: 1572-8145
    Keywords: Simulation ; modelling ; machine learning ; evolutionary algorithms ; artificial neural network
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The use of simulation technology as a tool for planning and control is of increasing significance in most fields of production. The main part of the expenditure concerning simulation analyses is the modelling of the considered production. Despite the use of modern building-block-oriented modelling technology, this modelling can often not be done by the user, but only by external experts. Against this backdrop, an adaptive simulation system is being developed by the Institute for Industrial Manufacturing and Management (IFF) at the University of Stuttgart. It independently adapts to real production processes, i.e. it learns about the interdependencies of production processes, and, in this way, supports the user in constructing and maintaining the model. In terms of information technology, the research in the field of artificial intelligence, especially in the subdomain of machine learning, is the basis for the realization of such adaptive systems.
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Constraints 3 (1998), S. 239-253 
    ISSN: 1572-9354
    Keywords: machine learning ; game playing ; spatial cognition ; extensible architectures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes an architecture that begins with enough general knowledge to play any board game as a novice, and then shifts its decision-making emphasis to learned, game-specific, spatially-oriented heuristics. From its playing experience, it acquires game-specific knowledge about both patterns and spatial concepts. The latter are proceduralized as learned, spatially-oriented heuristics. These heuristics represent a new level of feature aggregation that effectively focuses the program's attention. While training against an external expert, the program integrates these heuristics robustly. After training it exhibits both a new emphasis on spatially-oriented play and the ability to respond to novel situations in a spatially-oriented manner. This significantly improves performance against a variety of opponents. In addition, we address the issue of context on pattern learning. The procedures described here move toward learning spatially-oriented heuristics for autonomous programs in other spatial domains.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Annals of operations research 55 (1995), S. 179-193 
    ISSN: 1572-9338
    Keywords: Genetic algorithms ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract Theories are collections of large bodies of data in the real world. We describe autonomous systems, which observe the outside world and try to generate programs which reproduce the observed data. Methods for generation of new programs are enumeration as well as mutation and combination of old programs. We describe two criteria for judging the quality of a program. We can judge a program to be good if it is short and describes a large body of input data. With this criterion we show that a system can learn to evaluate arithmetic expressions in polish notation. But we can also judge a program to be good if it allows to compress the total length of descriptions ofall observations so far. By the latter criterion a system can createtests which can be used e.g. to partition the programs found so far into directories.
    Type of Medium: Electronic Resource
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Annals of operations research 65 (1996), S. 21-34 
    ISSN: 1572-9338
    Keywords: Quality improvement ; heuristic optimization ; machine learning ; service industry ; customer service measures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract Redesigning and improving business processes to better serve customer needs has become a priority in service industries as they scramble to become more competitive. This paper describes an approach to process improvement that is being developed collaboratively by applied researchers at US WEST, a major telecommunications company, and the University of Colorado. Motivated by the need to streamline and to add more quantitative power to traditional quality improvement processes, the new approach uses an artificial intelligence (AI) statistical tree growing method that uses customer survey data to identify operations areas where improvements are expected to affect customers most. This AI/statistical method also identifies realistic quantitative targets for improvement and suggests specific strategies (recommended combinations of actions) that are predicted to have high impact. This research, funded in part by the Colorado Advanced Software Institute (CASI) in an effort to stimulate profitable innovations, has resulted in a practical methodology that has been used successfully at US WEST to help set process improvement priorities and to guide resource allocation decisions throughout the company.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Constraints 1 (1996), S. 7-43 
    ISSN: 1572-9354
    Keywords: constraint satisfaction algorithms ; machine learning ; configurable systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Multi-tac is a learning system that synthesizes heuristic constraint satisfaction programs. The system takes a library of generic algorithms and heuristics and specializes them for a particular application. We present a detailed case study with three different distributions of a single combinatorial problem, “Minimum Maximal Matching”, and show that Muti-tac can synthesize programs for these different distributions that perform on par with hand-coded programs and that exceed the performance of some well-known satisfiability algorithms. In synthesizing a program, Multi-tac bases its choice of heuristics on an instance distribution, and we demonstrate that this capability has a significant impact on the results.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    Machine learning 23 (1996), S. 121-161 
    ISSN: 0885-6125
    Keywords: machine learning ; robotics ; uncertainty ; planning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, proves that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.
    Type of Medium: Electronic Resource
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