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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Journal ArticleDOI
VHPOP: versatile heuristic partial order planner
Håkan L. S. Younes,Reid Simmons +1 more
TL;DR: VHPOP as mentioned in this paper is a partial order causal link (POCL) planner loosely based on UCPOP, which combines the experience gained in the early to mid 1990's on flaw selection strategies for POCL planning, and combines this with recent developments in the field of domain independent planning such as distance based heuristics and reachability analysis.
Journal ArticleDOI
Using regression-match graphs to control search in planning
TL;DR: An algorithm is presented, which searches a space of plan prefixes, trying to extend one of them to a complete sequence of actions, guided by a heuristic estimator based on regression-match graphs, which attempt to characterize the entire subgoal structure of the remaining part of the problem.
Journal ArticleDOI
Learning heuristic functions for large state spaces
TL;DR: To make the process effective when only a single problem instance needs to be solved, this work presents a variation in which the bootstrap learning of new heuristics is interleaved with problem-solving using the initial heuristic and whatever heuristic have been learned so far.
Proceedings Article
Learning inadmissible heuristics during search
TL;DR: This paper demonstrates that heuristics learned on-line result in both faster search and better solutions while relying only on information readily available in any best-first search.
Proceedings Article
Least-cost flaw repair: a plan refinement strategy for partial-order planning
TL;DR: Experimental results demonstrate that the power of DUnf does not come from delaying threat repairs per se, but rather from the fact that this delay has the effect of imposing a partial preference for least-cost flaw selection.