Learning and Applying Competitive Strategies

Learning reusable sequences can support the development of expertise in many domains, either by improving decisionmaking quality or decreasing execution speed. This paper introduces and evaluates a method to learn action sequences for generalized states from prior problem experience. From experienced sequences, the method induces the context that underlies a sequence of actions. Empirical results indicate that the sequences and contexts learned for a class of problems are actually those deemed important by experts for that particular class, and can be used to select appropriate action sequences when solving problems there. Repeated problem solving can provide salient, reusable data to a learner. This paper focuses on programs that acquire expertise in a particular domain. The thesis of our work is that previously experienced sequences are an important knowledge source for such programs, and that appropriate organization of those sequences can support reuse of that knowledge, in novel ...

• Type: Conference
• Year: 2004
• Where: AAAI
• Authors: Esther Lock, Susan L. Epstein
• Keywords: AAAI 2004, Action Sequence, Experienced Sequences, Intelligent Agents, Sequences