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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Makespan-Usefulness Example

Get all trucks to An optimal plan

Makespan-Usefulness Example

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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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Results Over Time

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Results Over Time

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At 30 Minutes

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Quality Change

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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