Control Knowledge to Improve Plan Quality

Generating production-quality plans is an essential element in transforming planners from research tools into real-world applications. However most of the work to date on learning planning control knowledgehas beenaimed at improving the efficiency of planning; this work has been termed "speed-up learning". This paper focuses on learning control knowledge to guide a planner towards better solutions, i.e. to improve the quality of the plans produced by the planner, as its problem solving experience increases. We motivate the use of quality-enhancing search control knowledge and its automated acquisition from problem solving experience. We introduce an implemented mechanism for learning such control knowledge and some of our preliminary results in a process planning domain.

• Type: Conference
• Year: 1994
• Where: AIPS
• Authors: M. Alicia Pérez, Jaime G. Carbonell
• Keywords: AIPS 1994, Artificial Intelligence, Control Knowledge, Problem Solving Experience, Search Control Knowledge