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Learning Classifier Systems
This book constitutes the refereed proceedings of the 5th International Workshop on Learning Classifier Systems, IWLCS 2003, held in Granada, Spain in September 2003 in conjunction with PPSN VII. The 10 revised full papers presented together with a comprehensive bibliography on learning classifier systems were carefully reviewed and selected during two rounds of refereeing and improvement. All relevant issues in the area are addressed.
IUTAM Symposium on Dynamics Modeling and Interaction Control in Virtual and Real Environments
This volume contains the invited papers presented at the IUTAM Symposium on Multibody Dynamics and Interaction Control in Virtual and Real Environments held in Budapest, Hungary, June 7−11 2010. The symposium aimed to bring together specialists in the fields of multibody system modeling, contact/collision mechanics and control of mechanical systems. The offered topics included modeling aspects, mechanical and mathematical models, the question of neglections and simplifications, reduction of large systems, interaction with environment like air, water and obstacles, contact of all types, control concepts, control stability and optimization. Discussions between experts in these fields made it...
Recent Advances in Reinforcement Learning
This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008. The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to the field of and current researches in reinforcement learning.
Reinforcement Learning
Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to product...
Advances in Learning Classifier Systems
Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.
Computing Anticipatory Systems
This book constitutes the proceedings of the Seventh International Conference on Computing Anticipatory Systems, CASYS'05. The volume includes all invited papers as well as those papers that received a best paper award. Also included is a lecture on "General Principles for Brain Design," presented by Brian D. Josephson, Nobel Laureate in Physics. The papers discuss recent research and development in the modeling and computing of anticipatory systems in fields of natural and artificial systems.
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