Category: IWLCS

Preliminary IWLCS 2007 CFP

London, UK, July 7-9, 2007. To be held during the Genetic and Evolutionary Computation Conference (GECCO-2007), July 7-11, 2007.

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 a very active area of research, 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. A free introductory tutorial on LCS will be presented at GECCO 2007.

The final call for papers can be found here.

Advances at the frontier of LCS: LNCS 4399

“Advances at the frontier of Learning Classifier Systems” has been shipped to Springer for the final stages of editing and printing. The volume is going to be printed as Springer’s LNCS 4399 volume. When we started editing this volume, we faced the choice of organizing the contents in a purely chronological fashion or as a sequence of related topics that help walk the reader across the different areas. In the end we decided to organize the contents by area, breaking a little the time-line. This was not a simple endeavor as we could organize the material using multiple criteria. The taxonomy below is our humble effort to provide a coherent grouping. Needless to say, some works may fall in more than one category. Below, you may find the tentative table of contents of the volume. It may change a little bit, but we will keep you posted as soon as we learn from Springer.

Part I. Knowledge representation

  • 1. Analyzing Parameter Sensitivity and Classifier Representations for Real-valued XCS
    by Atsushi Wada, Keiki Takadama, Katsunori Shimohara, and Osamu Katai
    4399 – 001
  • 2. Use of Learning Classifier System for Inferring Natural Language Grammar
    by Olgierd Unold and Grzegorz Dabrowski
    4399 – 018
  • 3. Backpropagation in Accuracy-based Neural Learning Classifier Systems
    by Toby O’Hara and Larry Bull
    4399 – 026
  • 4. Binary Rule Encoding Schemes: A Study Using The Compact Classifier System
    by Xavier Llorà, Kumara Sastry , and David E. Goldberg
    4399 – 041

Part II. Mechanisms

  • 5. Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach Learning Classifier System
    by Jaume Bacardit and Josep Maria Garrell
    4399 – 061
  • 6. Post-processing Clustering to Decrease Variability in XCS Induced Rulesets
    by Flavio Baronti, Alessandro Passaro, and Antonina Starita
    4399 – 081
  • 7. LCSE: Learning Classifier System Ensemble for Incremental Medical Instances
    by Yang Gao, Joshua Zhexue Huang, Hongqiang Rong, and Da-qian Gu
    4399 – 094
  • 8. Effect of Pure Error-Based Fitness in XCS
    by Martin V. Butz , David E. Goldberg, and Pier Luca Lanzi
    4399 – 105
  • 9. A Fuzzy System to Control Exploration Rate in XCS
    by Ali Hamzeh and Adel Rahmani
    4399 – 116
  • 10. Counter Example for Q-bucket-brigade under Prediction Problema
    by Atsushi Wada, Keiki Takadama, and Katsunori Shimohara
    4399 – 130
  • 11. An Experimental Comparison between ATNoSFERES and ACS
    by Samuel Landau, Olivier Sigaud, Sébastien Picault, and Pierre Gérard
    4399 – 146
  • 12. The Class Imbalance Problem in UCS Classifier System: A Preliminary Study
    by Albert Orriols-Puig and Ester Bernadó-Mansilla
    4399 – 164
  • 13. Three Methods for Covering Missing Input Data in XCS
    by John H. Holmes, Jennifer A. Sager, and Warren B. Bilker
    4399 – 184

Part III. New Directions

  • 14. A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients
    by Javier G. Marín-Blázquez and Sonia Schulenburg
    4399 – 197
  • 15. Adaptive value function approximations in classifier systems
    by Lashon B. Booker
    4399 – 224
  • 16. Three Architectures for Continuous Action
    by Stewart W. Wilson
    4399 – 244
  • 17. A Formal Relationship Between Ant Colony Optimizers and Classifier Systems
    by Lawrence Davis
    4399 – 263
  • 18. Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis
    by John H. Holmes
    4399 – 276

Part IV. Application-oriented research and tools

  • 19. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
    by Jaume Bacardit and Martin V. Butz
    4399 – 290
  • 20. Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule
    by Jaume Bacardit, David E. Goldberg, and Martin V. Butz
    4399 – 299
  • 21. Using XCS to Describe Continuous-Valued Problem Spaces
    by David Wyatt, Larry Bull, and Ian Parmee
    4399 – 318
  • 22. The EpiXCS Workbench: A Tool for Experimentation and Visualization
    by John H. Holmes and Jennifer A. Sager
    4399 – 343

IWLCS 2007 organizers

Finally, we finished the pool process to get the organizers for IWLCS 2007. The next three volunteers that will be forming the organizing committee are:

Please join us welcoming them and encouraging them while taking the challenge of keeping IWLCS moving forward and expanding its reach. The advisory committee is now formed by

IWLCS 2007: Call for organizers

Dear colleagues,

The term for Tim Kovacs, Xavier Llorà, and Keiki Takadama as organizers of the International Workshop on Learning Classifier Systems is over. For the last two years we have organized the workshop and we have put together a book that covers 2003, 2004, and 2005 workshops (soon to enter the printing stage).

We have greatly enjoyed working with you all. Your friendship and help has motivated and guide us maintain alive the great effort that Pier Luca Lanzi, Stewart Wilson, and Wolfgang Stolzman started in 1999.

We would humbly put out this call for organizer for IWLCS 2007. For those of you that have been with us in the workshops, please, do not be afraid and volunteer. We would encourage you to get in touch with us before August 1st. Just sent and email to any of us (Tim Kovacs, Xavier Llorà, and Keiki Takadama). We would announce the next organizing committee by September 1st.

Thank you all for all the support during this two-year period!
Tim, Xavier, Keiki.

Slides of the presentations of IWLCS 2006

Here you can find the slides of the presentations done at the Ninth International Workshop on Learning Classifier Systems (IWLCS 2006).

  • Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System, Jaume Bacardit, Natalio Krasnogor. [Download PDF]
  • The Χ-ary Extended Compact Classifier System: Linkage Learning in Pittsburgh LCS, Xavier Llorà, Kumara Sastry, David E. Goldberg, Luis delaOssa. [Download PDF]
  • An Initial Analysis of Parameter Sensitivity for XCS with Computed Prediction, Pier Luca Lanzi, Matteo Zanini.
  • Improving Classifier Error Estimate in XCSF, Daniele Loiacono, Jan Drugowitsch, Alwyn Barry, Pier Luca Lanzi. [Download PDF]
  • Dual-structured Classifier System Mediating XCS and Gradient Descent based Update, Atsushi Wada, Keiki Takadama, Katsunori Shimohara. [Download PDF]
  • A Further Look at UCS Classifier System, Albert Orriols-Puig, Ester Bernadó-Mansilla. [Download PPT]
  • Using XCS for Action Selection in RoboCup Rescue Simulation League, Ivette C. Martínez, David Ojeda, Ezequiel Zamora. [Download PDF]
  • Community of Practice under Learning Classifier Systems, Yutaka I. Leon Suematsu, Keiki Takadama, Katsunori Shimohara, Osamu Katai. [Download PDF]
  • Technology Extraction for Future Generations from Process Time Series Data Reflecting Expert Operator Skills, Setsuya Kurahashi, Takao Terano. [Download PDF]
  • An Artificial Life Classifier System for Real-Valued Inputs, Julian Bishop. [Download PDF]
  • Developing Conversational Interfaces with XCS, Dave Toney, Johanna Moore, Oliver Lemon. [Download PDF]

The Ninth International Workshop on Learning Classifier Systems (IWLCS 2006) is here!

The schedule for the Ninth International Workshop on Learning Classifier Systems (IWLCS’2006) is ready. We are getting together on July 9 during the GECCO 2006 Workshops. Here is the timetable. Each presentation may last for 25 minutes for the oral presentation plus five minutes for question. We are looking forward to meet you all there!

Timetable

  • 9:10 -9:20: Opening remarks. Chair: Tim Kovacs.
  • 9:20-10:20:Pittsburgh Approach Session. Chair: Tim Kovacs.

    • 9:20- 9:50: Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System, Jaume Bacardit, Natalio Krasnogor.
    • 9:50-10:20: The Chi-ary Extended Compact Classifier System: Linkage Learning in Pittsburgh LCS, Xavier Llorà, Kumara Sastry, David E. Goldberg, Luis delaOssa.
  • 10:20-10:40: Break 20 minutes.
  • 10:40-12:40: Method and Analysis. Chair: Xavier Llorà.

    • 10:40-11:10: An Initial Analysis of Parameter Sensitivity for XCS with Computed Prediction, Pier Luca Lanzi, Matteo Zanini.
    • 11:10-11:40: Improving Classifier Error Estimate in XCSF, Daniele Loiacono, Jan Drugowitsch, Alwyn Barry, Pier Luca Lanzi.
    • 11:40-12:10: Dual-structured Classifier System Mediating XCS and Gradient Descent based Update, Atsushi Wada, Keiki Takadama, Katsunori Shimohara.
    • 12:10-12:40: A Further Look at UCS Classifier System, Albert Orriols-Puig, Ester Bernado-Mansilla.
  • 12:40-14:20: Lunch break [NOTE: 10 minutes later + 10 minutes addition in comparison with the workshop schedule posted at the GECCO WEB page]
  • 14:20-15:50: Distributed and Multiagent System. Chair: Keiki Takadama.

    • 14:20-14:50: Using XCS for Action Selection in RoboCup Rescue Simulation League, Ivette C. Martinez, David Ojeda, Ezequiel Zamora.
    • 14:50-15:20: Community of Practice under Learning Classifier Systems, Yutaka I. Leon Suematsu, Keiki Takadama, Katsunori Shimohara, Osamu Katai.
    • 15:20-15:50: Technology Extraction for Future Generations from Process Time Series Data Reflecting Expert Operator Skills, Setsuya Kurahashi, Takao Terano.
  • 15:50-16:10: Break 20 minutes
  • 16:10-17:10: Advanced Architecture. Chair: Tim Kovacs.

    • 16:10-16:40: An Artificial Life Classifier System for Real-Valued Inputs, Julian Bishop.
    • 16:40-17:10: Developing Conversational Interfaces with XCS, Dave Toney, Johanna Moore, Oliver Lemon.
  • 17:10-17:50: Open Discussion.
  • 17:50-18:00: Closing remarks.

List of papers to be presented at IWLCS 2006

This is the list of papers accepted for presentation at IWLCS 2006 that will take place during GECCO 2006.

  • Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System
    Bacardit, J. and Krasnogor, N.
  • An Artificial Life Classifier System for Real-Valued Inputs
    Bishop, J.
  • Technology Extraction for Future Generations from Process Time Series Data Reflecting Expert Operator Skills
    Kurahashi, S. and Terano, T.
  • An Initial Analysis of Parameter Sensitivity for XCS with Computed Prediction
    Lanzi, P.L., and Zanini, M.
  • The χ-ary Extended Compact Classifier System: Linkage Learning in Pittsburgh LCS
    Llorà, X., Sastry, K., Goldberg, D.E., and delaOssa, L.
  • Using XCS for Action Selection in RoboCup Rescue Simulation League
    Martínez, I. C., Ojeda, D., and Zamora, E.
  • Extending XCS with Representation in First-Order Logic
    Mellor, D.
  • A Further Look at UCS Classifier System
    Orriols-Puig, A., Bernad&oaccute;-Mansilla, E.
  • Agent-Based Learning Classifier Systems for Grid Data Mining
    Santos, M.F, Quintela, H., and Neves, J.
  • Community of Practice under Learning Classifier Systems
    Suematsu, Y.I.L., Takadama, K., Shimohara, K., and Katai, O.
  • Developing Conversational Interfaces with XCS
    Toney, D., Moore, J., and Lemon, O.
  • Dual-structured Classifier System Mediating XCS and Gradient Descent based Update
    Wada, A., Takadama, K., and Shimohara, K.

Advances at the frontier of LCS: First step completed

The first step of the volume Advances at the frontier of LCS is almost done. Below there is a list of the camera readies collected so far. These book chapters cover the contributions to IWLCS on 2003 and 2004.

  • Data Mining in Learning Classifier Systems: Comparing XCS with GAssist.
    Bacardit, J. and Butz, M.
  • Bloat Control and Generalization Pressure using the Minimum Description Length Principle for a Pittsburgh approach Learning Classifier System.
    Bacardit, J. and Garrell, J.M.
  • Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule.
    Bacardit, J., Goldberg, D.E., and Butz, M.
  • Effect of Pure Error-Based Fitness in XCS.
    Butz, M., Goldberg, D.E., and Lanzi, P.L.
  • A Formal Relationship Between Ant Colony Optimizers and Classifier Systems.
    Davis, D.
  • An Experimental Comparison between ATNoSFERES and ACS.
    Landau, S., Sigaud, O., Picault, S., and Gérard, P.
  • Where to Go Once You Have Evolved a Bunch of Promising Hypotheses?.
    Llorà, X., Bernadó, B., Bacardit, J., and Traus, I.
  • A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients.
    Martín-Blazquez, J. and Schulenburg, S.
  • Backpropagation in Accuracy-based Neural Learning Classifier Systems .
    O’Hara, T. and Bull, L.
  • Use of Learning Classifier System for Inferring Natural Language Grammar .
    Unold, O. and Dabrowski, G.
  • Analyzing Parameter Sensitivity and Classifier Representations for Real-valued XCS .
    Wada, A., Takadama, K., Shimohara, K., and Katai, O.
  • Three Architectures for Continuos Action.
    Wilson, S.W.
  • Using XCS to Describe Continuous-Valued Problem Spaces.
    Wyatt, D., Bull, L., and Parmee, I.

Ninth International Workshop on Learning Classifier Systems (IWLCS 2006) – CFP

Seattle, WA, USA, July 8-9, 2006. To be held during the Genetic and Evolutionary Computation Conference (GECCO-2006), July 8-12, 2006.

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 a very active area of research, 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. A free introductory tutorial on LCS will be presented at GECCO 2006.

Submissions

There are two possibilities for paper submissions. Both will be peer reviewed, but reviews of short papers will be mainly to provide feedback to authors – we expect most or all will be accepted.

1) Short papers of up to 4 pages may be submitted. Accepted short papers will be presented at the workshop and published in the GECCO workshop volume. The format of the GECCO workshop volume is to be confirmed but we expect it will be the ACM format used in 2005. After the workshop authors will be invited to submit full papers which are reviewed again for the post-workshop proceedings, which we plan to publish in Springer’s LNAI series as in past years.

2) Full papers of up to 20 pages (in Springer format) may be submitted for peer review before the workshop. Accepted full papers will be presented at the workshop and will be published in the post-workshop proceedings. Authors of full papers have a choice of how to contribute to the GECCO workshop volume: either i) prepare a short version for GECCO or ii) publish only your abstract in the GECCO book. If you prefer i) we would suggest an extended abstract of 1 or 2 pages, but anything up to 50% of the full paper is ok.

Papers should be submitted as PDF files e-mailed to iwlcs@cas.dis.titech.ac.jp.

Important dates

Please note: all dates are to be confirmed.

  • Paper submission deadline: March 24, 2006 (extended)
  • Decisions: April 12, 2006 (extended)
  • GECCO 2006 Workshop proceedings camerar-ready: April 26, 2006 (extended)
  • Workshop: July 8-9, 2006

Camera Ready for GECCO 2006 Workshop Proceedings

The camera-ready papers should be formated following the instructions provided by GECCO. Failing to comply will result in exclusion from the proceedings. The proceedings will only be published on CD-ROM. Camera-ready papers must be submitted using the GECCO-2006 Submission & Review site at https://ssl.linklings.net/conferences/gecco2006/.

Organization

Organizing Commitee

  • Tim Kovacs, University of Bristol (UK)
  • Xavier Llorà, University of Illinois at Urbana-Champaign (USA)
  • Keiki Takadama, Tokyo Institute of Technology (Japan)

Advisory Committee

  • Pier Luca Lanzi, Politechnico de Milano (Italy)
  • Wolfgang Stolzmann, Daimler Chrysler AG (Germany)
  • Stewart Wilson, Prediction Dynamics (USA)

Program Committee

  • Bacardit, Jaume. University of Nottingham (UK)
  • Bagnall, Tony. Univesity of East Anglia (UK)
  • Barry, Alwyn. University of Bath (UK)
  • Bernadó Mansilla, Ester. Universitat Ramon Llull (Spain)
  • Bonarini, Andrea. Politecnico di Milano (Italy)
  • Booker, Lashon. The Mitre Corporation (USA)
  • Browne, Will. University of Reading (UK)
  • Bull, Larry. University of West England (UK)
  • Butz, Martin. Universitat Wurzburg (Germany)
  • Carse, Brian. University of West England (UK)
  • Davis, David. NuTech Solutions (USA)
  • Drugowitsch, Jan. University of Bath (UK)
  • Egginton, RobUniversity of Bristol (UK)
  • Herrera, Francisco. Universidad de Granada (Spain)
  • Holmes, John. University of Pennsylvania (USA)
  • Homaifar, Abdollah. North Carolina A&T State University (USA)
  • Kovacs, Tim. University of Bristol (UK)
  • Lanzi, Pier Luca. Politecnico di Milano (Italy)
  • Llorà, Xavier. University of Illlinois at Urbana-Champaign (USA)
  • Marin-Blazquez, Javier. Universidad de Murcia (Spain)
  • Miramontes-Hercog, Luis. Instituto Tecnológico y de Estudios Superiores de Monterrey (Mexico)
  • Muruzabal, Jorge. Universidad Rey Juan Carlos (Spain)
  • Schulenburg, Sonia. University of Edinburgh (UK)
  • Sigaud, Olivier. Laboratoire d’Informatique de Paris 6 (France)
  • Stolzman, Wolfgang. Daimler Chrysler AG (Germany)
  • Takadama, Keiki. Tokyo Institute of Technology (Japan)
  • Wada, Atsushi. Advanced Telecomunications Research Institute (Japan)
  • Wilson, Stewart. Prediction Dynamics (USA)
  • Zatuchna, Z. V. Univesity of East Anglia (UK)

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