Classifier systems are "discovery" production rule systems that utilize the genetic algorithm for discovery and allocate credit through the bucket brigade. For any given problem, the success of a classifier system depends on the choice of representation, the system's ability to attain reward or puni
โฆ LIBER โฆ
An approach to credit assignment in classifier systems
โ Scribed by T.H. Westerdale
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 88 KB
- Volume
- 4
- Category
- Article
- ISSN
- 1076-2787
No coin nor oath required. For personal study only.
โฆ Synopsis
We present a conceptual framework within which we can analyze simple reward schemes for classifier systems. The framework consists of a set of classifiers, a learning mechanism, and a finite automaton environment that outputs payoff. We find that many reward schemes have negative biases that degrade system performance. We analyze bucket brigade schemes, which are subgoal reward schemes, and profit sharing schemes, which aren't. By contrasting these schemes, we hope to better understand the place of subgoal reward in learning and evolution.
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