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  • Articles  (99)
  • machine learning  (99)
  • Springer  (99)
  • Periodicals Archive Online (PAO)
  • Computer Science  (95)
  • Economics  (4)
  • 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
    Annals of operations research 16 (1988), S. 33-52 
    ISSN: 1572-9338
    Keywords: Artificial intelligence ; collective learning systems theory ; machine learning ; massively parallel architectures ; parallel distributed processing ; data flow architectures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract The classical approach to the acquisition of knowledge in artificial intelligence has been to program the intelligence into the machine in the form of specific rules for the application of the knowledge: expert systems. Unfortunately, the amount of time and resources required to program an expert system with sufficient knowledge for non-trivial problem-solving is prohibitively large. An alternative approach is to allow the machine tolearn the rules based upon trial-and-error interaction with the environment, much as humans do. This will require endowing the machine with a sophisticated set of sensors for the perception of the external world, the ability to generate trial actions based upon this perceived information, and a dynamic evaluation policy to allow it to measure the effectiveness of its trial actions and modify its repertoire accordingly. The principles underlying this paradigm, known ascollective learning systems theory, have already been applied to sophisticated gaming problems, demonstrating robust learning and dynamic adaptivity. The fundamental building block of a collective learning system is thelearning cell, which may be embedded in a massively parallel, hierarchical data communications network. Such a network comprising 100 million learning cells will approach the intelligence capacity of the human cortex. In the not-too-distant future, it may be possible to build a race of robotic slaves to perform a wide variety of tasks in our culture. This goal, while irresistibly attractive, is most certainly fraught with severe social, political, moral, and economic difficulties.
    Type of Medium: Electronic Resource
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  • 3
    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.
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  • 4
    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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  • 5
    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.
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  • 6
    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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  • 7
    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.
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Machine learning 26 (1997), S. 147-176 
    ISSN: 0885-6125
    Keywords: machine learning ; inductive logic programming ; regression ; real-valued variables ; first-order logic ; applications of machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a new approach, called First Order Regression (FOR), to handling numerical information in Inductive Logic Programming (ILP). FOR is a combination of ILP and numerical regression. First-order logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program FORS is an implementation of this idea, where numerical regression is focused on a distinguished continuous argument of the target predicate. We show that this can be viewed as a generalisation of the usual ILP problem. Applications of FORS on several real-world data sets are described: the prediction of mutagenicity of chemicals, the modelling of liquid dynamics in a surge tank, predicting the roughness in steel grinding, finite element mesh design, and operator's skill reconstruction in electric discharge machining. A comparison of FORS' performance with previous results in these domains indicates that FORS is an effective tool for ILP applications that involve numerical data.
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  • 9
    ISSN: 1572-8412
    Keywords: archaeological typology ; ceramics ; knowledge acquisition ; machine learning ; Sudan
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Media Resources and Communication Sciences, Journalism
    Notes: Abstract The authors here show that machine learning techniques can be used for designing an archaeological typology, at an early stage when the classes are not yet well defined. The program (LEGAL, LEarning with GAlois Lattice) is a machine learning system which uses a set of examples and counter-examples in order to discriminate between classes. Results show a good compatibility between the classes such as the yare defined by the system and the archaeological hypotheses.
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 3 (1993), S. 31-51 
    ISSN: 1572-8641
    Keywords: Induction ; machine learning ; uniform convergence ; prior probability ; inductive logic
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow confirmed by the data — how are we to restrict the space of inductive hypotheses and choose effectively some rules that will probably perform well on future examples? We analyze if and how this problem is approached in standard accounts of induction and show the difficulties that are present. Finally, we suggest that the explanation-based learning approach and related methods of knowledge intensive induction could be, if not a solution, at least a tool for solving some of these problems.
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