Domain-independent planning: representation and plan generation
Artificial Intelligence
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Planning as search: a quantitative approach
Artificial Intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Automatically generating abstractions for problem solving
Automatically generating abstractions for problem solving
Steps toward artificial intelligence
Computers & thought
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Human Problem Solving
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
A new representation and associated algorithms for generalized planning
Artificial Intelligence
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It has long been recognized that hierarchical problem solving can be used to reduce search. Yet, there has been little analysis of the problem-solving method and few experimental results. This paper provides the first comprehensive analytical and empirical demonstrations of the effectiveness of hierarchical problem solving. First, the paper shows analytically that hierarchical problem solving can reduce the size of the search space from exponential to linear in the solution length and identifies a sufficient set of assumptions for such reductions in search. Second, it presents empirical results both in a domain that meets all of these assumptions as well as in domains in which these assumptions do not strictly hold. Third, the paper explores the conditions under which hierarchical problem solving will be effective in practice.