Incremental learning of control knowledge for nonlinear problem solving

Abstract

In this paper we advocate a learning method where a deductive and an inductive strategies are combined to efficiently learn control knowledge. The approach consists of initially bounding the explanation to a predetermined set of problem solving features. Since there is no proof that the set is sufficient to capture the correct and complete explanation for the decisions, the control rules acquired are then refined, if and when applied incorrectly to new examples. The method is especially significant as it applies directly to nonlinear problem solving, where the search space is complete. We present hamlet, a system where we implemented this learning method, within the context of the prodigy architecture. hamlet learns control rules for individual decisions corresponding to new learning opportunities offered by the nonlinear problem solver that go beyond the linear one. These opportunities involve, among other issues, completeness, quality of plans, and opportunistic decision making. Finally, we show empirical results illustrating hamlet's learning performance.