Case-based planning: viewing planning as a memory task
Case-based planning: viewing planning as a memory task
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Explaining and repairing plans that fail
Artificial Intelligence
The roles of associational and causal reasoning in problem solving
Artificial Intelligence
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Flexible strategy learning: analogical replay of problem solving episodes
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Plan reuse versus plan generation: a theoretical and empirical analysis
Artificial Intelligence - Special volume on planning and scheduling
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
A domain-independent algorithm for plan adaptation
Journal of Artificial Intelligence Research
PlanMine: Predicting Plan Failures Using Sequence Mining
Artificial Intelligence Review - Issues on the application of data mining
Using simulation and critical points to define states in continuous search spaces
Proceedings of the 32nd conference on Winter simulation
SPADE: An Efficient Algorithm for Mining Frequent Sequences
Machine Learning
Sequence Mining in Categorical Domains: Algorithms and Applications
Sequence Learning - Paradigms, Algorithms, and Applications
Plan-based control of robotic agents: improving the capabilities of autonomous robots
Plan-based control of robotic agents: improving the capabilities of autonomous robots
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Past research on assessing and improving plans in domains that contain uncertainty has focused on analytic techniques that are exponential in the length of the plan. Little work has been done on choosing from among the many ways in which a plan can be improved. We present the IMPROVE algorithm which simulates the execution of large, probabilistic plans. IMPROVE runs a data mining algorithm on the execution traces to pinpoint defects in the plan that most often lead to plan failure. Finally, IMPROVE applies qualitative reasoning and plan adaptation algorithms to modify the plan to correct these defects. We have tested IMPROVE on plans containing over 250 steps in an evacuation domain, produced by a domain-specific scheduling routine. In these experiments, the modified plans have over a 15% higher probability of achieving their goal than the original plan.