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Planning techniques recorded a significant progress during recent years. However, many planning problems remain still hard even for modern planners. One of the most promising approaches is gathering additional knowledge by using learning techniques. Well known sort of knowledge - macro-operators, formalized like 'normal' planning operators, represent a sequence of primitive planning operators. The other sort of knowledge consists of pruning unnecessary operators' instances (actions) by investigating connections (entanglements) between operators and initial or goal predicates. Advantageously, macro-operators and entanglements can be encoded directly in planning domains (or problems) and common planning systems can be applied on them. In this paper, we will show how we can put these approaches together. We will provide an experimental evaluation showing that combining these learning techniques can improve the planning process.
Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on (Volume:2 )
Date of Conference: 27-29 Oct. 2010