The FERMI System: Inducing Iterative Macro-Operators from Experience

Proceedings of the AAAI Conference on Artificial Intelligence, 5

Volume

Science

Learning

Automated methods of exploiting past experience to reduce search vary from analogical transfer to chunking control knowledge. In the latter category, various forms of composing problem-solving operators into larger units have been explored. However, the automated formulation of effective macro-operators requires more than the storage and parametrization of individual linear operator sequences. This paper addresses the issue of acquiring conditional and iterative operators, presenting a concrete example implemented in the FERMI problem-solving system. In essence, the process combines empirical recognition of cyclic patterns in the problem-solving trace with analytic validation and subsequent formulation of general iterative rules. Such rules can prove extremely effective in reducing search beyond linear macro-operators produced by past techniques.