Book ChapterDOI
Improving Heuristics On-the-fly for Effective Search in Plan Space
Shashank Shekhar,Deepak Khemani +1 more
- pp 302-308
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TLDR
This work extends single-step-error adaptation to heuristic functions from Partial Order Causal Link (POCL) planning to allow a partial order planner to observe the effective average- step-error during search.Abstract:
The design of domain independent heuristic functions often brings up experimental evidence that different heuristics perform well in different domains. A promising approach is to monitor and reduce the error associated with a given heuristic function even as the planner solves a problem. We extend this single-step-error adaptation to heuristic functions from Partial Order Causal Link (POCL) planning. The goal is to allow a partial order planner to observe the effective average-step-error during search. The preliminary evaluation shows that our approach improves the informativeness of the state-of-the-art heuristics. Our planner solves more problems by using the improved heuristics as compared to when it uses current heuristics in the selected domains.read more
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Learning and Tuning Meta-heuristics in Plan Space Planning
Shashank Shekhar,Deepak Khemani +1 more
TL;DR: An online error minimization approach in POCL framework is discussed to minimize the step-error associated with the offline learned models thus enhancing their informativeness and scale up the performance of the planner over standard benchmarks, specially for larger problems.
References
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Proceedings Article
Fast planning through planning graph analysis
Avrim Blum,Merrick L. Furst +1 more
TL;DR: A new approach to planning in STRIPS-like domains based on constructing and analyzing a compact structure the authors call a Planning Graph is introduced, and a new planner, Graphplan, is described that uses this paradigm.
Journal ArticleDOI
Fast planning through planning graph analysis
Avrim Blum,Merrick L. Furst +1 more
TL;DR: Graphplan as mentioned in this paper is a partial-order planner based on constructing and analyzing a compact structure called a planning graph, which can be used to find the shortest possible partial order plan or state that no valid plan exists.
Journal ArticleDOI
Planning as heuristic search
Blai Bonet,Hector Geffner +1 more
TL;DR: A family of heuristic search planners are studied based on a simple and general heuristic that assumes that action preconditions are independent, which is used in the context of best-first and hill-climbing search algorithms, and tested over a large collection of domains.
Proceedings Article
UCPOP: a sound, complete, partial order planner for ADL
TL;DR: It is proved ucpop is both sound and complete for this representation and a practical implementation that succeeds on all of Pednault's and McDermott's examples, including the infamous "Yale Stacking Problem".
Proceedings Article
Systematic nonlinear planning
TL;DR: A simple, sound, complete, and systematic algorithm for domain independent STRIPS planning by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation.