Learning Opposite Concept for Machine Planning

May 1999

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)

Volume

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)

All Papers

An incomplete planning domain theory can cause an inconsistency problem in a noisy domain. To solve the problem of applying two opposite operators to the same state, we present a novel method to learn a negative precondition as control knowledge. Even though the control knowledge is unknown to a machine, it is implicitly known as opposite concept to a human. To learn the human concept, we propose a new technique to mechanically generate a graph composed of opposite operators from a domain theory and extract opposite literals. We show that the opposite concept is a special type of mutex used in Graphplan. A learned concept can simplify the operator by removing a redundant precondition while preventing inconsistencies.