Author: xavier

Eighth International Workshop on Learning Classifier Systems (IWLCS 2005) – CFP

Washington, D.C., USA, June 25, 2005. To be held during the Genetic and Evolutionary Computation Conference (GECCO-2005), June 25-29, 2005.

Since Learning Classifier Systems (LCSs) were introduced by Holland as a way of applying evolutionary computation to machine learning problems, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining to automated innovation to on-line control. Classifier systems are currently enjoying a renaissance, with newer approaches, in particular Wilson’s accuracy-based XCS, receiving a great deal of attention. LCS are also benefiting from advances in the field of reinforcement learning, and there is a trend toward developing connections between the two areas.

We invite submissions which discuss recent developments in all areas of research on, and applications of, Learning Classifier Systems.

IWLCS is the only event to bring together most of the core researchers in classifier systems. Two free tutorials on LCS will be presented at GECCO 2005: an introductory tutorial on LCS in general and an advanced tutorial on XCS.

Special Session on Challenges for the Field

What are the key challenges, issues, limitations, technologies and directions for LCS research? We plan a special session consisting of 5 minute presentations on challenges for the field and how to solve them. If interested, please send your name and title of your presentation to the organisers by April 10, 2005.

Submissions

Accepted papers will appear in the GECCO 2005 workshop volume distributed at the conference, and, as in previous years, we intend to publish selected papers in a post-workshop proceedings in Springer’s LNAI series.

There are two possibilities for paper submissions. Short papers of up to 4 pages may be submitted. Short papers will be peer reviewed and those accepted will be presented at the workshop. After the workshop the authors will have to submit full papers which are reviewed again for the post-workshop proceedings.

Alternatively, full papers of up to 20 pages may be submitted for peer review before the workshop. All accepted full papers will be presented at the workshop and will be published in the post-workshop proceedings.

All papers should be in LNAI format. A PDF file containing the paper should be e-mailed to iwlcs@cas.dis.titech.ac.jp by the 10th of April 2005.

Camera ready for GECCO’2005 workshop proceedings

Accepted short papers will be published in the GECCO workshop book. Authors of long papers have a choice: either i) prepare a short version for GECCO or ii) send them only your abstract for the GECCO book. If you prefer i) I would suggest an extended abstract of 1 or 2 pages, but anything up to 75% of the full paper is ok.

All papers need to be format following the ACM camera-ready directives of all accepted workshop papers should be submitted by April 26 for their inclusion in the GECCO 2005 workshop proceedings. It is important to note that those papers needs to be format following ACM directives. Further instructions for the preparation of the workshop papers can be found at http://www.sheridanprinting.com/typedept/gecco1.htm.

Remember to include the proper ACM conference information

\conferenceinfo{GECCO'05,} {June 25--29, 2005, Washington, DC, USA.}
\CopyrightYear{2005}
\crdata{1-59593-097-3/05/0006}

Important dates

  • Paper submission deadline: April 10, 2005 (Deadline extended)
  • Decisions: April 21, 2005
  • GECCO 2005 Workshop proceedings camerar-ready: April 26, 2005
  • Workshop: June 25, 2005

Organization

Organizing Commitee

  • Tim Kovacs, University of Bristol
  • Xavier Llorà, University of Illinois at Urbana-Champaign,
  • Keiki Takadama, Tokyo Institute of Technology

Advisory Committee

  • Pier Luca Lanzi, Politechnico de Milano
  • Wolfgang Stolzmann, DaimlerChrysler AG, Berlin
  • Stewart Wilson, Prediction Dynamics, Concord. MA

Program Committee

  • Alwyn Barry,University of Bath
  • Andrea Bonarini, Politecnico di MIlano
  • Lashon Booker, The MITRE Corporation
  • Will Browne, The University of Reading
  • Larry Bull, University of West England
  • Martin Butz, University of Wuerzburg
  • Rob Egginton, University of Bristol
  • Pierre Gérard, University of Paris 13
  • John Holmes, University of Pennsylvania School of Medicine
  • Jacob Hurst, University of West England
  • Sonia Schulenburg, Napier University
  • Olivier Sigaud, University of Paris 6
  • Jaume Bacardit, University of Nottingham
  • Ester Bernadó-Mansilla, Ramon Llull University
  • Luis Miramontes Hercog
  • Christopher Stone, University of the West of England
  • Atsushi Wada, ATR Network Informatic Laboratories

For further information please contact iwlcs@cas.dis.titech.ac.jp.

Evolutionary Computation in Data Mining

This carefully edited book by Ashish Ghosh and Lakhmi C. Jainreflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. Evolutionary Computation in Data Mining provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics.

Applications of Learning Classifier Systems

This carefully edited book by Larry Bull brings together a fascinating selection of applications of Learning Classifier Systems (LCS). The book demonstrates the utility of this machine learning technique in recent real-world applications in such domains as data mining, modelling and optimization, and control. It shows how the LCS technique combines and exploits many Soft Computing approaches into a single coherent framework to produce an improved performance over other approaches.

Learning Classifier Systems : 5th International Workshop (IWLCS 2002)

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.

Anticipatory Behavior in Adaptive Learning Systems

This book edited by Martin Butz, Olivier Sigaud, and Pierre Gérard addresses the interdisciplinary topic of anticipation, attracting attention from computer scientists, psychologists, philosophers, neuroscientists, and biologists is a rather new and often misunderstood matter of research. This book attempts to establish anticipation as a research topic and encourage further research and development work.

First, the book presents philosophical thoughts and concepts to stimulate the reader’s concern about the topic. Fundamental cognitive psychology experiments then confirm the existence of anticipatory behavior in animals and humans and outline a first framework of anticipatory learning and behavior. Next, several distinctions and frameworks of anticipatory processes are discussed, including first implementations of these concepts. Finally, several anticipatory systems and studies on anticipatory behavior are presented.

Data Mining and Knowledge Discovery with Evolutionary Algorithms

This book by Alex Freitas integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research.In general, data mining consists of extracting knowledge from data. In this book we particularly emphasize the importance of discovering comprehensible, interesting knowledge, which is potentially useful for the reader for intelligent decision making.In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.

Anticipatory Learning Classifier Systems

Anticipatory Learning Classifier Systems by Martin Butz describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior. Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning. Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system. It is an excellent reference for researchers interested in adaptive behavior and machine learning from a cognitive science perspective as well as those who are interested in combining evolutionary learning mechanisms for learning and optimization tasks.

Learning Classifier Systems : From Foundations to Applications

Learning Classifier Systems (LCS) 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 difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.

Classifier Fitness Based on Accuracy

In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier’s fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier’s fitness is given by a measure of the prediction’s accuracy. The system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its population tending to form a complete and accurate mapping X x A => P from inputs and actions to payoff predictions. Further, XCS tends to evolve classifiers that are maximally general subject to an accuracy criterion. Besides introducing a new direction for classifier system research, these properties of XCS make it suitable for a wide range of reinforcement learning situations where generalization over states is desirable.

The paper can be downloaded at: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.6508