Proceedings of the Annual Meeting of the Cognitive Science Society

The problem addressed In this paper is heuristically-guided learning of finite automata from examples. Given positive sample strings and negative sample strings, a finite automaton is generated and incrementally refined to accept all positive samples but no negative samples. This paper describes some experiments in applying hillcllmblng to modify finite automata to accept a desired regular language. We show that many problems can be solved by this simple method.