Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Explaining and repairing plans that fail
Artificial Intelligence
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
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)
The design and implementation of a case-based planning framework within a partial-order planner
The design and implementation of a case-based planning framework within a partial-order planner
Failure driven dynamic search control for partial order planners: an explanation based approach
Artificial Intelligence
Failsafe: a floor planner that uses EBG to learn from its failures
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
A Survey on Case-Based Planning
Artificial Intelligence Review
Using genetic programming to learn and improve control knowledge
Artificial Intelligence
A Case Retention Policy Based on Detrimental Retrieval
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
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
Storing and indexing plan derivations through explanation-based analysis of retrieval failures
Journal of Artificial Intelligence Research
CBM-Gen+: an algorithm for reducing case base inconsistencies in hierarchical and incomplete domains
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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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In this paper we describe the design and implementation of the derivation replay framework, DERSNLP+EBL (Derivational SNLP+EBL), which is based within a partial order planner. DERSNLP+EBL replays previous plan derivations by first repeating its earlier decisions in the context of the new problem situation, then extending the replayed path to obtain a complete solution for the new problem. When the replayed path cannot be extended into a new solution, explanation-based learning (EBL) techniques are employed to identify the features of the new problem which prevent this extension. These features are then added as censors on the retrieval of the stored case. To keep retrieval costs low, DERSNLP+EBL normally stores plan derivations for individual goals, and replays one or more of these derivations in solving multi-goal problems. Cases covering multiple goals are stored only when subplans for individual goals cannot be successfully merged. The aim in constructing the case library is to predict these goal interactions and to store a multi-goal case for each set of negatively interacting goals. We provide empirical results demonstrating the effectiveness of DERSNLP+EBL in improving planning performance on randomly-generated problems drawn from a complex domain.