Learning sequential decision rules using simulation models and competition

A Coevolutionary Approach to Learning Sequential Decision Rules

We present a coevolutionary approach to learning sequential decision rules which appears to have a number of advantages over non-coevolutionary approaches. The coevolutionary approach encourages the formation of stable niches representing simpler subbehaviors. The evolutionary direction of each subbehavior can be controlled independently, providing an alternative to evolving complex behavior us...

Learning Decision Lists Using Homogeneous Rules

A decision list is an ordered list of conjunctive rules (Rivest 1987). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem | the classi cation accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in ...

Learning and Sequential Decision Making

Learning Sequential Decision Tasks

This paper presents a new approach called SANE for learning and performing sequential decision tasks. Compared to problem-general heuristics, SANE forms more e ective decision strategies because it learns to utilize domain-speci c information. SANE evolves neural networks through genetic algorithms and can learn in a wide range of domains with minimal reinforcement. SANE's evolution algorithm, ...

Learning Sequential Decision

This paper presents a new approach called SANE for learning and performing sequential decision tasks. Compared to problem-general heuristics, SANE forms more eeective decision strategies because it learns to utilize domain-speciic information. SANE evolves neural networks through genetic algorithms and can learn in a wide range of domains with minimal reinforcement. SANE's evolution algorithm, ...