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 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
Conceptual Neighborhoods for Retrieval in Case-Based Reasoning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Constraint-Based Case-Based Planning Using Weighted MAX-SAT
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Case-based strategies in computer poker
AI Communications
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Traditional artificial intelligence techniques do not perform well in applications such as real-time strategy games because of the extensive search spaces which need to be explored. In addition, this exploration must be carried out on-line during performance time; it cannot be precomputed. We have developed on-line case-based planning techniques that are effective in such domains. In this paper, we extend our earlier work using ideas from traditional planning to inform the real-time adaptation of plans. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan. The plan 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. The approach is evaluated using WARGUS, a well-known real-time strategy game.