In Proceedings of the Fifth International Conference on Machine Learning (ICML), pp. 270-283, Ann Arbor, MI: Morgan Kaufmann, June 1988.
Paper ID: 209
Category: Explanation-Based Learning, Learning for Planning and Problem Solving
A number of machine learning systems have been built which learn macro-operators or plan schemata, i.e. general compositions of actions which achieve a goal. However, previous research has not addressed the issue of generalizing the temporal order of operators and learning macro-operators with partially-ordered actions. This paper presents an algorithm for learning partially-ordered macro-operators which has been incorporated into the EGGS domain-independent explanation-based learning system. Examples from the domains of computer programming and narrative understanding are used to illustrate the performance of this system. These examples demonstrate that generalizing the order of operators can result in more general as well as more justified concepts. A theoretical analysis of the time complexity of the generalization algorithm is also presented.
