Skip to main content
SpringerLink
Log in

Knowledge extraction and rule set compaction in XCS for non-Markov multi-step problems

  • Short Note
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

The application of the XCS family of classifier systems within multi-step problems has shown promise, but XCS classifier systems usually solve those problems with a large number of classifiers. If they can generate compact and readily interpretable solutions when used in multi-step problems, it will enhance their applicability and usefulness in a wide range of robot control and knowledge discovery tasks. In this paper, we describe some methods developed for XCS to compact the rule set and acquire explicit policy knowledge for multi-step problems, especially non-Markov problems. Experimental results show the ability to obtain a very compact rule set out of the final population of classifiers with little or no degradation of overall performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Butz M, Lanzi P, Llorà X, Goldberg D (2004) Knowledge extraction and problem structure identification in XCS. In: Yao X, Burke E, Lozano J, et al (eds) Parallel problem solving from nature—PPSN VIII, vol 3242. Springer, Berlin, pp 1051–1060

  2. Butz MV, Wilson SW (2002) An algorithmic description of XCS. Soft Comput 6(3–4):144–153

    Article  MATH  Google Scholar 

  3. Dixon P, Corne D, Oates M (2004) Encouraging compact rulesets from XCS for enhanced data mining. In: Bull L (ed) Applications of learning classifier systems, vol 150. Springer, Berlin, pp 92–109

  4. Dixon PW, Corne D, Oates MJ (2002) A ruleset reduction algorithm for the XCS learning classifier system. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, 5th international workshop (IWLCS 2002). Lecture notes in computer science, vol 2661. Springer, Berlin, pp 20–29

  5. Fu C, Davis L (2002) A modified classifier system compaction algorithm. In: Langdon WB, Paz ECU, Mathias K et al (eds) GECCO 2002: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann Publishers, pp 920–925

  6. Graphviz (2012) Graphviz-graph visualization software. http://www.graphviz.org/. Accessed 27 Sep 2012

  7. Kharbat F, Odeh M, Bull L (2007) New approach for extracting knowledge from the XCS learning classifier system. Int J Hybrid Intell Syst 4(2):49–62

    MATH  Google Scholar 

  8. Kovacs T (1997) XCS classifier system reliably evolves accurate, complete, and minimal representations for boolean functions. In: Roy R, Chawdhry P, Pant P (eds) Soft computing in engineering design and manufacturing. Springer-Verlag, London, pp 59–68

  9. Landau S, Sigaud O (2008) A comparison between ATNoSFERES and learning classifier systems on non-Markov problems. Inform Sci 178(23):4482–4500

    Article  Google Scholar 

  10. Lanzi P (2008) Learning classifier systems: then and now. Evol Intell 1(1):63–82

    Article  Google Scholar 

  11. Lanzi PL (1998) An analysis of the memory mechanism of XCSM. In: Koza JR, Banzhaf W, Chellapilla K, et al (eds) Genetic Programming 1998: Proceedings of the third annual conference. Morgan Kaufmann, University of Wisconsin, Madison, pp 643–651

  12. Lanzi PL (1999) An analysis of generalization in the XCS classifier system. Evol Comput 7(2):125–149

    Article  Google Scholar 

  13. Lanzi PL, Wilson SW (2000) Toward optimal classifier system performance in non-Markov environments. Evol Comput 8(4):393–418

    Article  Google Scholar 

  14. Nakata M, Lanzi PL, Takadama K (2012) Enhancing learning capabilities by XCS with best action mapping. In: Coello CC, Cutello V, Deb K, Forrest S, Nicosia G, Pavone M (eds) Parallel problem solving from nature—PPSN XII, vol 7491. Springer, Berlin, pp 256–265

  15. Nakata M, Sato F, Takadama K (2011) Towards generalization by identification-based XCS in multi-steps problem. 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp 389–394

  16. Wilson S (2002) Compact rulesets from XCSI. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems: fourth international workshop, IWLCS 2001 (LNAI 2321), vol 2321. Springer-Verlag, Berlin, pp 197–208

    Google Scholar 

  17. Wilson SW (1994) ZCS: a zeroth level classifier system. Evol Comput 2(1):1–18

    Article  Google Scholar 

  18. Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175

    Article  Google Scholar 

  19. Wilson SW (1998) Generalization in the XCS classifier system. In: Koza JR, Banzhaf W, Chellapilla K, et al (eds) Proceedings of the third annual genetic programming conference. Morgan Kaufmann, San Francisco, CA, pp 665–674

  20. Wilson SW (2000) Mining oblique data with XCS. University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory

    Google Scholar 

  21. Zatuchna Z, Bagnall A (2009) A learning classifier system for mazes with aliasing clones. Nat Comput 8(1):57–99

    Article  MathSciNet  MATH  Google Scholar 

  22. Zatuchna ZV (2005) AgentP: a learning classifier system with associative perception in maze environments. PhD, School of Computing Sciences, University of East Anglia (UEA)

Download references

Acknowledgments

The authors are grateful for the suggestions of the three anonymous reviewers and pioneers.

Author information

Authors and Affiliations

  1. Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Zhaoxiang Zang & Dehua Li

  2. College of Computer and Information Technology, China Three Gorges University, Yichang, 443000, Hubei, China

    Junying Wang

Authors
  1. Zhaoxiang Zang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dehua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaoxiang Zang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zang, Z., Li, D. & Wang, J. Knowledge extraction and rule set compaction in XCS for non-Markov multi-step problems. Evol. Intel. 6, 41–53 (2013). https://doi.org/10.1007/s12065-013-0087-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-013-0087-x

Keywords

Search

Navigation