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![Page 1: State Agnostic Planning Graphs William Cushing Daniel Bryce Arizona State University {william.cushing, dan.bryce}@asu.edu Special thanks to: Subbarao Kambhampati,](https://reader035.fdocuments.in/reader035/viewer/2022062714/56649d485503460f94a2365c/html5/thumbnails/1.jpg)
State Agnostic Planning Graphs
William CushingDaniel Bryce
Arizona State University{william.cushing, dan.bryce}@asu.edu
Special thanks to:Subbarao Kambhampati, David E. Smith,
Menkes van den Briel, Romeo Sanchez, J. Benton
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Motivation
Reachability analysis (via Planning Graphs) Sets of planning graphs are useful
Progression search Belief-space planning Replanning Robustification Local search …
…but highly redundant PGs overlap (duplicate information) PGs are inflexible (fixed source) Generalize PG to multiple sources
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Introduction
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Overview
Scratch
State Agnostic Graph
Planning GraphsBuildPG(A)
BuildSAG()
1. Labeled Uncertainty Graph [LUG]
2. (Belief) State Agnostic LUG [SALUG]
3. Optimized (Belief) State Agnostic LUG [SLUG]
ExtractH(A,B)
Reachability Queries
ExtractH(A,B)
Technique: Transform BuildPG(A) into BuildSAG()
Introduction
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Multiple Graphs
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Heuristics for belief-space
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D. Bryce and S. Kambhampati, “Heuristic Guidance Measures for Conformant Planning”, In ICAPS’04, 2004.
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Unioned Graphs
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Heuristics for belief-space
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Unioned Graphs
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Heuristics for belief-space
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h( )=5Labeled Graph [LUG]
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Heuristics for belief-space
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D. Bryce, S. Kambhampati, and D. Smith, “Planning in Belief Space with a Labelled Uncertainty Graph”, In AAAI Workshop on
Markov Processes, 2004.
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Labeled Graph [LUG]
1 ^ 3 ^ -5 ^ γ3 ^ 5 ^ -1 ^ γ1 ^ 5 ^ -3 ^ γ1 ^ 3v5 ^ -3v-5 ^ γ3 ^ 1v5 ^ -1v-5 ^ γ5 ^ 1v3 ^ -1v-3 ^ γ
1v3 ^ 3v5 ^ 1v5 ^ -1v-3v-5 ^ γ
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Binary Decision Diagrams
Initialize: and/projection
Operator: and/preconditions
Literal: or/supporters
Heuristics for belief-space
D. Bryce, S. Kambhampati, and D. Smith, “Planning in Belief Space with a Labelled Uncertainty Graph”, In AAAI Workshop on
Markov Processes, 2004.
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γ = “everything else false”
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Multiple (Labeled) Graphs
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Single graph for progression
D. Bryce, S. Kambhampati, and D. Smith, “Planning Graph Heuristics for Belief Space Search”, ASU CSE TR, 2005.
![Page 10: State Agnostic Planning Graphs William Cushing Daniel Bryce Arizona State University {william.cushing, dan.bryce}@asu.edu Special thanks to: Subbarao Kambhampati,](https://reader035.fdocuments.in/reader035/viewer/2022062714/56649d485503460f94a2365c/html5/thumbnails/10.jpg)
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Unioned (Labeled) Graph
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Single graph for progression
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Introduce labels for beliefs over labels for states
Labeled (Labeled) Graph [SALUG]
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Sr v Sb
Sb v Sg
Sr v Sg
Sr^Sb^Sg => 1v3 ^ 3v5 ^ 1v5 ^ -1v-3v-5 ^ γ-Sr => 5 ^ 1v3 ^ -1v-3 ^ γ-Sb => 1 ^ 3v5 ^ -3v-5 ^ γ-Sg => 3 ^ 1v5 ^ -1v-5 ^ γ
Single graph for progression
W. Cushing and D. Bryce, “State Agnostic Planning Graphs”, In AAAI, 2005.
![Page 12: State Agnostic Planning Graphs William Cushing Daniel Bryce Arizona State University {william.cushing, dan.bryce}@asu.edu Special thanks to: Subbarao Kambhampati,](https://reader035.fdocuments.in/reader035/viewer/2022062714/56649d485503460f94a2365c/html5/thumbnails/12.jpg)
Labeled (Labeled) Graph [SALUG]
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Single graph for progression
W. Cushing and D. Bryce, “State Agnostic Planning Graphs”, In AAAI, 2005.
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Filtered Unioned (Labeled) Graph [SLUG]
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Don’t let the name fool you!
Ignore irrelevant labels
Largest LUG == all LUGs
Optimized single graph
W. Cushing and D. Bryce, “State Agnostic Planning Graphs”, In AAAI, 2005.
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Belief Space Problems Classical Problems
Conformant Contingent
Empirical Results
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Conclusion
Developed general agnosticism (SAG) Removed dependence on world state (PG LUG) Removed dependence on belief state (LUG SALUG)
Dramatically improved performance ({LUG,SALUG} ~> SLUG)
Empirically demonstrated Internal performance boost Favorable external comparison
SAG has rich connections to: Constraint propagation (vs. branching) Lazy evaluation Memoization
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Further Details
Heuristics for belief space in the CAltAlt planner D. Bryce and S. Kambhampati, “Heuristic Guidance Measures for
Conformant Planning”, In ICAPS’04, 2004. Labeled Uncertainty Graph in the CAltAlt planner
D. Bryce, S. Kambhampati, and D. Smith, “Planning in Belief Space with a Labelled Uncertainty Graph”, In AAAI Workshop on Markov Processes, 2004.
Heuristics and LUG in the POND and CAltAlt planners D. Bryce, S. Kambhampati, and D. Smith, “Planning Graph
Heuristics for Belief Space Search”, ASU CSE TR, 2005. SLUG: Improvement to LUG for POND
W. Cushing and D. Bryce, “State Agnostic Planning Graphs”, In AAAI, 2005.
CLUG: propagating numeric information D. Bryce and S. Kambhampati, “Cost Sensitive Reachability
Heuristics for Handling State Uncertainty”, In UAI’05, 2005.
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