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Foundations of Learning Classifier Systems
This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
Applications of Learning Classifier Systems
The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments? In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together? John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw a...
Web Engineering
Web engineering is a new discipline that addresses the pressing need for syst- atic and tool-supported approaches for the development, maintenance and te- ing of Web applications. Web engineering builds upon well-known and succe- ful software engineering principles and practices, adapting them to the special characteristics of Web applications. Even more relevant is the enrichment with methods and techniques stemming from related areas like hypertext authoring, human-computer interaction, content management, and usability engineering. The goal of the 4th International Conference on Web Engineering (ICWE 2004), inlinewiththepreviousICWEconferences,wastoworktowardsabetterund- standing of the i...
Genetic and Evolutionary Computation — GECCO 2003
The set LNCS 2723 and LNCS 2724 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2003, held in Chicago, IL, USA in July 2003. The 193 revised full papers and 93 poster papers presented were carefully reviewed and selected from a total of 417 submissions. The papers are organized in topical sections on a-life adaptive behavior, agents, and ant colony optimization; artificial immune systems; coevolution; DNA, molecular, and quantum computing; evolvable hardware; evolutionary robotics; evolution strategies and evolutionary programming; evolutionary sheduling routing; genetic algorithms; genetic programming; learning classifier systems; real-world applications; and search based software engineering.
Rule-Based Evolutionary Online Learning Systems
Rule-basedevolutionaryonlinelearningsystems,oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performanc...
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Classifier systems are an intriguing approach to a broad range of machine learning problems, based on automated generation and evaluation of condi tion/action rules. Inreinforcement learning tasks they simultaneously address the two major problems of learning a policy and generalising over it (and re lated objects, such as value functions). Despite over 20 years of research, however, classifier systems have met with mixed success, for reasons which were often unclear. Finally, in 1995 Stewart Wilson claimed a long-awaited breakthrough with his XCS system, which differs from earlier classifier sys tems in a number of respects, the most significant of which is the way in which it calculates th...
Learning Classifier Systems
This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Atlanta, GA, USA in July 2008, and in Montreal, Canada, in July 2009 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 12 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on LCS in general, function approximation, LCS in complex domains, and applications.
Artificial Intelligence and Games
This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading.
Advances in Neural Networks: Computational and Theoretical Issues
This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computational topics which are grouped together into chapters devoted to the discussion of novelties and innovations related to the field of Artificial Neural Networks as well as the use of neural networks for applications, pattern recognition, signal processing, and special topics such as the detection and recognition of multimodal emotional expressions and daily cognitive functions, and bio-inspired memristor-based networks. Providing insights into the latest research interest from a pool of international experts coming from different research fields, the volume becomes valuable to all those with any interest in a holistic approach to implement believable, autonomous, adaptive and context-aware Information Communication Technologies.
Artificial Intelligence & Games
As has been pointed out by several industrial game AI developers the lack of behavioral modularity across games and in-game tasks is detrimental for the development of high quality AI [605, 171]. An increasingly popular method for ad-hoc behavior authoring that eliminates the modularity limitations of FSMs and BTs is the utility-based AI approach which can be used for the design of control and decision making systems in games [425, 557]. Following this approach, instances in the game get assigned a particular utility function that gives a value for the importance of the particular instance [10, 169]. For instance, the importance of an enemy being present at a particular distance or the impor...
