Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Alternative essences of intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Conditional, probabilistic planning: a unifying algorithm and effective search control mechanisms
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
An analogy ontology for integrating analogical processing and first-principles reasoning
Eighteenth national conference on Artificial intelligence
OBDD-based universal planning for synchronized agents in non-deterministic domains
Journal of Artificial Intelligence Research
Conditional progressive planning under uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
An analogy ontology for integrating analogical processing and first-principles reasoning
IAAI'02 Proceedings of the 14th conference on Innovative applications of artificial intelligence - Volume 1
The cog project: building a humanoid robot
Computation for metaphors, analogy, and agents
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Recently, several planners have been designed that can create conditionally branching plans to solve problems which involve uncertainty. These planners represent an important step in broadening the applicability of AI planning techniques, but they typically must search a larger space than non-branching planners, since they must produce valid plans for each branch considered. In the worst case this can produce an exponential increase in the complexity of planning. If conditional planners are to become usable in real-world domains, this complexity must be controlled by sharing planning effort among branches. Analogical plan reuse should playa fundamental role in this process. We have implemented a conditional probabilistic planner that uses analogical plan replay to derive the maximum benefit from previously solved branches of the plan. This approach provides valuable guidance for when and how to merge different branches of the plan and exploits the high similarity between the different branches in a conditional plan, which have the same goal and typically a very similar state. We present experimental data in which analogical plan replay significantly reduces the complexity of conditional planning. Analogical replay can be applied to a variety of conditional planners, complementing the plan sharing that they may perform naturally.