목표들간 상호간섭의 분석을 통한 탐색제어 지식의 학습

Learning Search Control Knowledge From the analysis of Goal Interactions

This paper presents methodology which enables the derivation of goal ordering rules from the analysis of problem failures. We examine all the possible ways of taking actions that lead to failures. If there are restrictions imposed by a problem state on possible actions to be taken, the restrictions manifest themselves in the form of a restricted set of possible operator bindings. Our method makes use of this observation to derive general control rules which are guaranteed to be correct. The overhead involved in learning is very low because this methodology needs only small amount of data to learn from namely, the goal stacks from the leaf nodes of a failure search tree. Empirical tests show that the rules derived by our system PAL couperform those derived by other systems such as PRODIGY and STATIC.