Formalizing Dependency Directed Backtracking and Explanation Based Learning in Refinement Search

No. 1: Agents, AI in Art and Entertainment, Knowledge Representation, and Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Enhancing Efficiency

The ideas of dependency directed backtracking (DDB) and explanation based learning (EBL) have developed independently in constraint satisfaction, planning and problem solving communities. In this paper, I formalize and unify these ideas under the task-independent framework of refinement search, which can model the search strategies used in both planning and constraint satisfaction. I show that both DDB and EBL depend upon the common theory ofexplaining search failuresand regressing them to higher levels of the search tree. The relevant issues of importance include (a) how the failures are explained and (b) how many failure explanations are remembered. This task-independent understanding of DDB and EBL helps support cross-fertilization of ideas among Constraint Satisfaction, Planning and Explanation-Based Learning communities.