Planning for conjunctive goals
Artificial Intelligence
Case-based planning: viewing planning as a memory task
Case-based planning: viewing planning as a memory task
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Intelligent planning: a decomposition and abstraction based approach
Intelligent planning: a decomposition and abstraction based approach
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
Using Cases Utility for Heuristic Planning Improvement
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Intelligent Guidance and Suggestions Using Case-Based Planning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Case-Based Plan Adaptation: An Analysis and Review
IEEE Intelligent Systems
On-line case-based plan adaptation for real-time strategy games
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
An analysis on transformational analogy: general framework and complexity
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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Previous approaches to case-based planning often finds a similar plan case to a new planning problem to adapt to solve the new problem. However, in the case base, there may be some other cases that provide helpful knowledge in building the new solution plan. Likewise, from each existing case there may be only certain parts that can be adapted for solving the new problem. In this paper, we propose a novel constraint-based case-based planning framework that can consider all similar plans in a case base to the current problem, and take only portions of their solutions in adaptation. Our solution is to convert all similar plan cases to constraints, and use them to solve the current problem by maximally exploiting the reusable knowledge from all the similar plan cases using a weighted MAX-SAT solver. We first encode a new planning problem as a satisfiability problem, and then extract constraints from plan cases. After that, we solve the SAT problem, including the extracted constraints, using a weighted MAX-SAT solver and convert the solution to a plan to solve the new planning problem. In our experiments, we test our algorithm in three different domains from International Planning Competition (IPC) to demonstrate the efficiency and effectiveness of our approach.