Planning as search: a quantitative approach
Artificial Intelligence
A theoretical analysis of conjunctive-goal problems
Artificial Intelligence
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Explaining and repairing plans that fail
Artificial Intelligence
Issues in the design of operator composition systems
Proceedings of the seventh international conference (1990) on Machine learning
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Partial-order planning: evaluating possible efficiency gains
Artificial Intelligence
Derivation replay for partial-order planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Artificial Intelligence - Special volume on planning and scheduling
Production matching for large learning systems
Production matching for large learning systems
Failure driven dynamic search control for partial order planners: an explanation based approach
Artificial Intelligence
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Explanation-Based Learning: An Alternative View
Machine Learning
A Comparitive Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Learning to Refine Indexing by Introspective Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
A Case Study on the Mergeability of Cases with a Partial-Order Planner
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Building and refining abstract planning cases by change of representation language
Journal of Artificial Intelligence Research
A domain-independent algorithm for plan adaptation
Journal of Artificial Intelligence Research
Design and implementation of a replay framework based on a partial order planner
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Exploiting symmetry in the planning graph via explanation-guided search
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
A Survey on Case-Based Planning
Artificial Intelligence Review
A Case-Based Framework for Interactive Capture and Reuse of Design Knowledge
Applied Intelligence
Using genetic programming to learn and improve control knowledge
Artificial Intelligence
Towards a Unified Theory of Adaption in Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Learning Rewrite Rules versus Search Control Rules to Improve Plan Quality
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Using Reinforcement Learning for Similarity Assessment in Case-Based Systems
IEEE Intelligent Systems
Meta-case-based reasoning: self-improvement through self-understanding
Journal of Experimental & Theoretical Artificial Intelligence
Adaptation versus Retrieval Trade-Off Revisited: An Analysis of Boundary Conditions
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure.