First-order learning systems (e.g., FOIL, FOCL, FORTE) generally rely on hill-climbing heuristics in order to avoid the combinatorial explosion inherent in learning first-order concepts. However, hill-climbing leaves these systems vulnerable to local maxima and local plateaus. We present a method, called relational pathfinding, which has proven highly effective in escaping local maxima and crossing local plateaus. We present our algorithm and provide learning results in two domains: family relationships and qualitative model building.
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In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 50-55, San Jose, CA, July 1992.
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Raymond J. Mooney Faculty mooney [at] cs utexas edu
Bradley Richards Ph.D. Alumni bradley [at] ai-lab fh-furtwangen de