Solution reuse in dynamic constraint satisfaction problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
In search of the best constraint satisfaction search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Flexible strategy learning: analogical replay of problem solving episodes
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Understanding and Extending Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Extending Planning Graphs to an ADL Subset
Extending Planning Graphs to an ADL Subset
Storing and indexing plan derivations through explanation-based analysis of retrieval failures
Journal of Artificial Intelligence Research
Domain filtering can degrade intelligent backtracking search
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Improving Graphplan's search with EBL & DDB techniques
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Domain-independent temporal planning in a planning-graph-based approach
AI Communications
Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in Graphplan
Journal of Artificial Intelligence Research
Using memory to transform search on the planning graph
Journal of Artificial Intelligence Research
Improving Graphplan's search with EBL & DDB techniques
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
On the application of least-commitment and heuristic search in temporal planning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Using available memory to transform Graphplan's search
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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We present a method for exploiting the symmetry in the planning graph structure and certain redundancies inherent in the Graphplan algonthm, so as to improve its backward search. The main insight underlying our method is that due to these features the backward search conducted at level k + 1 of the graph is essentially a replay of the search conducted at the previous level k with certam well-defined extensions. Our method consists of maintaining a pilot explanation structure capturing the failures encountered at previous levels of the search, and using it in an intelligent way to guide the search at the newer levels. The standard EBL and DDB techniques can be employed to control the size of the pilot explanation. The technique has been implemented in the EGBG system, and we present a preliminary empirical study.