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Learning Classifier Systems: From Foundations to Applications

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Learning classi er systems are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal di erence learning.

From the beginning, classi er systems have attracted the interest of researchers in many di erent areas, ranging from the design of gas pipelines to personal internet agents, and including cognitive science, data mining, economic trading agents, and autonomous robotics. In 1989 Stewart Wilson and David Goldberg presented a review of the rst decade of classi er system research, discussing some of the milestones that characterized the eld's early development. In 1992 the First International Workshop on Learning Classi er Systems (IWLCS92) was held in Houston, Texas.

The 1990s saw an increasing interest in the eld: many successful applications to real-world problems were presented as well as new classi er system models. With seven years since the rst workshop and more than 400 papers published, it was time in 1999 to examine again the state of the art of learning classi er system research. For this purpose the Second International Workshop on Learning Classi er Systems (IWLCS99) was organized and the idea of this volume conceived. The workshop, held in Orlando, Florida, July 13, 1999, attracted a vital community of researchers from many di erent areas who share a common interest in this machine learning paradigm. Some of the most interesting work presented at the workshop appears in this volume.

Our book provides an overview of the current state of the art of learning classi er systems and highlights some of the most promising research directions. The rst paper of Part I asks the fundamental question: \What Is a Learning Classi er System?". Answers are given by John Holland, originator of classi er systems, and by other long-time researchers in the eld: Lashon Booker, Marco Colombetti, Marco Dorigo, Stephanie Forrest, David Goldberg, Rick Riolo, Robert E. Smith, and Stewart Wilson. Three following papers summarize, from di erent perspectives, developments in the eld since Wilson and Goldberg's 1989 review. Part II contains papers on advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classi er system models. Part III is dedicated to promising applications of classi er systems such as: data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. A classi er systems bibliography with more than 400 references completes the book.

We hope that this volume will be a key reference for researchers working with learning classi er systems over the next decade; the constantly increasing interest in this paradigm suggests that there will be many of them!

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