A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
A Survey on Case-Based Planning
Artificial Intelligence Review
Applications of SHOP and SHOP2
IEEE Intelligent Systems
Case-Based Planning and Execution for Real-Time Strategy Games
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
A domain-independent algorithm for plan adaptation
Journal of Artificial Intelligence Research
Learning to win: case-based plan selection in a real-time strategy game
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Abstraction in Knowledge-Rich Models for Case-Based Planning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Implementing high-resolution adaptivity in game-based learning
Edutainment'12/GameDays'12 Proceedings of the 7th international conference on Edutainment, and Proceedings of the 3rd international conference on E-Learning and Games for Training, Education, Health and Sports
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Case-based planning (CBP) is based on reusing past successful plans for solving new problems. CBP is particularly useful in environments where the large amount of time required to traverse extensive search spaces makes traditional planning techniques unsuitable. In particular, in real-time domains, past plans need to be retrieved and adapted in real time and efficient plan adaptation techniques are required. We have developed real time adaptation techniques for case based planning and specifically applied them to the domain of real time strategy games. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan suggested by that case. The case is then adapted in real-time using its plan dependency graph. This allows the system to create and adapt plans in an efficient and effective manner while performing the task. Our techniques have been implemented in the Darmok system (see [8]), designed to play WARGUS, a well-known real-time strategy game. We analyze our approach and prove that the complexity of the plan adaptation stage is polynomial in the size of the plan. We also provide bounds on the final size of the adapted plan under certain assumptions.