state agnostic planning graphs

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

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

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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h( )=5Labeled Graph [LUG]

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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.

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


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”

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.

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Unioned (Labeled) Graph

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Introduce labels for beliefs over labels for states

Labeled (Labeled) Graph [SALUG]

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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


W. Cushing and D. Bryce, “State Agnostic Planning Graphs”, In AAAI, 2005.

Labeled (Labeled) Graph [SALUG]

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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


Belief Space Problems Classical Problems

Conformant Contingent

Empirical Results

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

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


CLUG: propagating numeric information D. Bryce and S. Kambhampati, “Cost Sensitive Reachability

Heuristics for Handling State Uncertainty”, In UAI’05, 2005.

state agnostic planning graphs

Documents

planning graph heuristics

graphsingle graph

graphssingle graph

labelled uncertainty

conformant planning

belief space search

aaai workshop

subbarao kambhampati