Learning Relations by Pathfinding

Bradley L. Richards, Raymond J. Mooney

First-order learning systems (e.g., FOlL, 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.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.