The University of Birmingham

Birmingham B15 2TT England

http://www.cs.bham.ac.uk/~mmk

Two basic questions to ask about any learning system are: to what kinds of problems is it well suited? To what kinds of problems is it poorly suited? Despite two decades of work, Learning Classifier Systems (LCS) researchers have had relatively little to say on the subject. This work attempts to answer questions like those above for Wilson's accuracy-based XCS [Wilson95a] applied to single step problems.

Wilson briefly investigated complexity in [Wilson98a] using a series of boolean multiplexer functions of string length 6, 11 and 20. Each of these functions has a minimal, complete, accurate, and non-overlapping representation using the standard ternary LCS alphabet, called its optimal population [O] [Kovacs2000z]. The size of the optimal populations (|[O]| for short) for these functions is 16, 32, and 64 respectively. By evaluating the number of inputs XCS needed to learn each function Wilson worked out an expression relating |[O]| and difficulty, and another relating string length and difficulty [Wilson98a]. Not surprisingly, functions of greater string length and |[O]| were more difficult. Our experience with multiplexer and parity functions indicates that larger |[O]| is more difficult even when string length is constant. Fig. 1 shows the difficulty of a series of 6 bit functions of increasing |[O]|, with performance measured as %[O] - the proportion of the optimal population present (see [Kovacs2000z] for details). In the following sections we discuss two additional, previously unknown, dimensions of problem complexity.

We have elsewhere [Kovacs2000z] outlined an approach to the study of problem complexity for XCS, an approach limited to an important subset of problems. We've outlined a number of dimensions of problem complexity, some previously unknown. We've illustrated how a significant dimension, |[O]|, structures the space of functions and defines complexity classes within it. Based on this we've created a single step test suite template that's simple to describe and implement, and which scales to any length input string. We suspect that XCS can reliably learn any function from the class studied, given enough resources. So far a great deal of empirical evidence has been gathered to support this hypothesis, but no proof is available.

- [Wilson95a]
Stewart W. Wilson.
Classifier fitness based on accuracy.
Evolutionary Computation, 3(2):149-175, 1995.
- [Wilson98a]
Stewart W. Wilson.
Generalization in the XCS classifier system.
In J. Koza et al., eds, Genetic Programming 1998:
Proceedings of the Third Annual Conference, pages 665-674. Morgan Kaufmann,
1998.
- [Kovacs2000z]
Tim Kovacs.
Accuracy-Based Classifier Systems. PhD Thesis. The University
of Birmingham, England. In preparation, 2000.

© Proceedings of the Third International Workshop on Learning Classifier Systems (IWLCS-2000), in the Joint Workshops of SAB 2000 and PPSN 2000, Tim Kovacs and Manfred Kerber

The URL of this page is http://www.cs.bham.ac.uk/~mmk/papers/00-IWLCS-WS.html.

Also available as pdf ps.gz bib .