Enhancing Search for Satisficing Temporal Planning with Objective-driven Decisions
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Enhancing Search for Satisficing Temporal Planning with Objective- driven Decisions J. Benton Patrick Eyerich Subbarao Kambhampati
description
Enhancing Search for Satisficing Temporal Planning with Objective-driven Decisions. Patrick Eyerich. Subbarao Kambhampati. J. Benton. g-value plateaus in Temporal Planning. Common temporal planning objective function (:metric (minimize (total-time ))) - PowerPoint PPT Presentation
Transcript of Enhancing Search for Satisficing Temporal Planning with Objective-driven Decisions
Enhancing Search for Satisficing Temporal Planning
with Objective-driven DecisionsJ. Benton Patrick
EyerichSubbarao
Kambhampati
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g-value plateaus in Temporal Planning
Common temporal planning objective function (:metric (minimize (total-time)))
Makespan as the evaluation function is inefficient for satisificing search g-value plateaus Leads to worst case cost-variance between search
operations The usual approach: Use a Surrogate Search
Choose a surrogate evaluation function that allows for scalability, improving the cost-variance between search states
Objective Function ≠ Evaluation Function We want to improve “keeping track” of objective
function
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Temporal Fast Downward Temporal Fast Downward (TFD)
Objective Function
CorrespondingEvaluation Function
SurrogateEvaluation Function
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Temporal Fast Downward Search5
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Temporal Fast Downward Search5
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Find the Better Path Force consideration of better-makespan
path Should maintain surrogate evaluation
function’s scalability
Our idea: Determine whether operators are useful according to makespan and force their expansion
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Useful Operators Related to Wehrle et al.’s useless
actions At parent state s
Remove operator o from the domain Find heuristic value for , Apply operator o to generate Find heuristic value for , If then operator is possibly
useful Its degree of usefulness is
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Lookahead on Useful Operators Force expansion of most makespan-
useful state before other states Remove ‘best’ node from queue Analyze for child states for makespan-
usefulness Expand state given by most useful
operator Evaluate each resulting grandchild state
according to the surrogate evaluation function and push into queue
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Useful Operator Lookahead5
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Empirical Evaluation 4 Anytime search variations
TFD TFD with useful lookahead, TFD with lazy evaluation followed by TFD
with useful lookahead (and without lazy evaluation),
TFD with lazy evaluation followed by TFD without lazy evaluation,
Makespan heuristic using STN 30 minute timeout Compared on IPC score
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Summary Used notion of operator usefulness
Lookahead on most useful operator Use in combination with surrogate
search
Shown to improve plan quality in some domains
Continues to help when combined with a portfolio-like approach
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Future Work Lookahead more than one step
k-deep local lookaheads on most useful operators combined with best-first search
Use relaxed solutions YAHSP-style lookahead but stop when no
makespan-useful operators are applicable
