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Dynamic Flexible Constraint Satisfaction and its Application to AI Planning / Edition 1

Dynamic Flexible Constraint Satisfaction and its Application to AI Planning / Edition 1

by Ian Miguel

ISBN-10: 1852337648

ISBN-13: 9781852337643

Pub. Date: 11/14/2003

Publisher: Springer London

The Distinguished Dissertation Series is published on behalf of the Conference of Professors and Heads of Computing and the British Computer Society, who annually select the best British PhD dissertations in computer science for publication. The dissertations are selected on behalf of the CPHC by a panel of eight academics. Each dissertation chosen makes a


The Distinguished Dissertation Series is published on behalf of the Conference of Professors and Heads of Computing and the British Computer Society, who annually select the best British PhD dissertations in computer science for publication. The dissertations are selected on behalf of the CPHC by a panel of eight academics. Each dissertation chosen makes a noteworthy contribution to the subject and reaches a high standard of exposition, placing all results clearly in the context of computer science as a whole. In this way computer scientists with significantly different interests are able to grasp the essentials - or even find a means of entry - to an unfamiliar research topic. Constraint satisfaction is a fundamental technique for knowledge representation and inference in Artificial Intelligence. This success is founded on simplicity and generality: a constraint simply expresses a set of admissible value combinations among a number of variables. However, the classical formulation of a static constraint satisfaction problem (CSP) with inflexible constraints, all of which a solution must satisfy, is insufficient to model many real problems. Recent work has addressed these shortcomings via two separate extensions, known as dynamic CSP and flexible CSP. Representing three years of PhD work by Dr. Ian Miguel, this book demonstrates how a range of instances of these two powerful extensions can be combined in order to solve more complex problems. As an application of this work, Artificial Intelligence Planning is extended to support compromise. Preferences are attached to plan goals and to the set of actions available to achieve these goals, allowing a systematic comparison of candidate plans. Although a plan may not completely satisfy all goals, nor perform the actions it uses in the most preferred situations, it may be significantly shorter than a compromise-free plan. Dr. Miguel has implemented Flexible Graphplan, a planning system based on dynamic flexible CSP, which generates a range of plans from an input problem, trading plan length against the number and severity of compromises made.

Product Details

Springer London
Publication date:
Distinguished Dissertations Series
Edition description:
Product dimensions:
0.81(w) x 6.14(h) x 9.21(d)

Table of Contents

1 Introduction.- 1.1 Solving Classical CSPs.- 1.2 Applications of Classical CSP.- 1.3 Limitations of Classical CSP.- 1.3.1 Flexible CSP.- 1.3.2 Dynamic CSP.- 1.4 Dynamic Flexible CSP.- 1.5 Flexible Planning: a DFCSP Application.- 1.6 Structure.- 1.7 Contributions and their Significance.- 2 The Constraint Satisfaction Problem.- 2.1 Constraints and Constraint Graphs.- 2.2 Tree Search Solution Techniques for Classical CSP.- 2.2.1 Backtrack.- 2.2.2 Backjumping.- 2.2.3 Conflict-Directed Backjumping.- 2.2.4 Backmarking.- 2.2.5 The Backmark Hybrids.- 2.2.6 Dynamic Backtracking.- 2.2.7 Relative Evaluation.- 2.3 Pre-Processing Techniques.- 2.3.1 Arc Consistency.- 2.3.2 Improving Efficiency in Enforcing Arc Consistency.- 2.3.3 Path Consistency.- 2.3.4 K-Consistency.- 2.3.5 Practical Consistency Enforcing.- 2.3.6 Directional Pre-Processing.- 2.4 Hybrid Tree-search Consistency-enforcing Algorithms.- 2.4.1 Partial Arc Consistency.- 2.4.2 Relative Evaluation.- 2.5 Heuristics.- 2.6 Conflict Recording.- 2.7 The Phase Transition in CSPs.- 2.8 Graph-Based Methods.- 2.8.1 The Invasion Procedure.- 2.8.2 The Cycle-Cutset Method.- 2.8.3 Non-separable Components.- 2.8.4 Tree-Clustering.- 2.9 Extending the CSP Framework.- 2.9.1 Extending Tree Search.- 2.9.2 Solution via Graph Decomposition.- 2.9.3 Additive Flexible CSP.- 2.9.4 Priority Maximisation Flexible CSP.- 2.10 Dynamic Constraint Satisfaction.- 2.10.1 Restriction/Relaxation-based Dynamic Constraint Satisfaction Problems.- 2.10.2 Recurrent Dynamic Constraint Satisfaction Problems.- 2.10.3 Activity-based Dynamic Constraint Satisfaction Problems.- 2.11 Summary.- 3 Dynamic Flexible Constraint Satisfaction.- 3.1 Towards Dynamic Flexible Constraint Satisfaction.- 3.1.1 Concepts of DFCSP.- 3.2 Examples from the Dynamic Perspective.- 3.2.1 Restriction/Relaxation-based DFCSP.- 3.2.2 Recurrent DFCSP.- 3.2.3 Activity-based DFCSP.- 3.3 A Specific Instance of DFCSP.- 3.3.1 The Flexible Component — a Recap.- 3.4 Fuzzy rrDFCSP Solution via Branch and Bound.- 3.5 Fuzzy rrDFCSP Solution via Local Repair.- 3.5.1 Local Changes.- 3.5.2 Flexible Local Changes: A Fuzzy rrDFCSP Algorithm.- 3.5.3 FLC Complexity Issues.- 3.6 Fuzzy Arc Consistency.- 3.6.1 The Complexity of Fuzzy Arc Consistency.- 3.6.2 Pre-processing with Fuzzy Arc Consistency.- 3.6.3 Hybrids.- 3.6.4 The Deletion Threshold.- 3.7 Solution Techniques for other DFCSP Instances.- 3.8 An Example.- 3.8.1 Solution of Initial Problem via Branch and Bound.- 3.8.2 Solution of Initial Problem via FLC.- 3.8.3 The Problem Changes.- 3.8.4 Solution of Updated Problem via Branch and Bound.- 3.8.5 Solution of Updated Problem via FLC.- 3.9 Summary.- 4 An Empirical Study of Fuzzy rrDFCSPs.- 4.1 The Problems.- 4.2 The Algorithms Studied.- 4.3 Evaluation Criteria.- 4.4 Heuristics Investigated.- 4.4.1 Variable Selection.- 4.4.2 Domain Element Selection.- 4.4.3 Constraint Check Selection.- 4.5 Results: 3-point Satisfaction Scale.- 4.6 Results: 4-point Satisfaction Scale.- 4.7 Results: 5-point Satisfaction Scale.- 4.8 The Utility of Dynamic Information.- 4.9 The Utility of the Deletion Threshold.- 4.10 The Utility of the Constraint Check Ordering Heuristic.- 4.11 The Utility of FLC Variable Selection Heuristics.- 4.12 The Utility of FLC Domain Element Selection Heuristics.- 4.13 Summary.- 5 Dynamic CSP in Domain-independent AI Planning.- 5.1 AI Planning.- 5.1.1 Constraint Satisfaction in Planning.- 5.2 An Overview of Graphplan.- 5.2.1 The Planning Graph.- 5.2.2 Basic Plan Extraction.- 5.2.3 Memoisation.- 5.3 Viewing the Planning Graph as a CSP.- 5.4 Plan Extraction via Dynamic Constraint Satisfaction.- 5.4.1 A Hierarchical Approach.- 5.4.2 Memoisation in the Hierarchical Context.- 5.5 The GP-rrDCSP Algorithm.- 5.5.1 The Top-level Procedure.- 5.5.2 The extract() Procedure.- 5.5.3 The propagateMS() Procedure.- 5.6 Complexity Issues.- 5.7 Avoiding Irrelevant Variables in Memosets Created by Propagation.- 5.8 Focusing the Search.- 5.8.1 Variable Selection.- 5.8.2 Value Selection.- 5.8.3 Constraint Check Selection.- 5.9 Summary.- 6 GP-rrDCSP: Experimental Results.- 6.1 The Logistics Domain.- 6.1.1 The RocketA Problem.- 6.1.2 The RocketB Problem.- 6.1.3 The LogisticsA Problem.- 6.1.4 The LogisticsB and LogisticsC Problems.- 6.1.5 The LogisticsD Problem.- 6.2 The Blocks-world Domain.- 6.2.1 The 12-step Problem.- 6.2.2 The Blocks-world LargeA Problem.- 6.2.3 The Blocks-world LargeB Problem.- 6.3 The Gripper Domain.- 6.4 The Movie Domain.- 6.5 The Grid Domain.- 6.6 Summary.- 7 Flexible Planning Problems & Flexible Graphplan.- 7.1 Background.- 7.2 Flexible Planning Problems.- 7.2.1 Representation.- 7.2.2 The Valuable Package Problem (Continued).- 7.3 Flexible Graph Expansion.- 7.3.1 Mutual Exclusivity.- 7.3.2 Basic Flexible Graph Expansion.- 7.3.3 Limited Graph Expansion.- 7.3.4 Satisfaction Propagation During Graph Expansion.- 7.4 Flexible Plan Extraction via rrDFCSP.- 7.4.1 The FCSP Viewpoint.- 7.4.2 The Hierarchical Approach Revisited.- 7.4.3 Memoisation for Flexible Plan Synthesis.- 7.4.4 The Valuable Package Problem - Synthesised Plans.- 7.5 The FGP Algorithm.- 7.5.1 The Top-level Procedure.- 7.5.2 The FExtract() Procedure.- 7.5.3 Complexity Issues.- 7.6 Summary.- 8 FGP: Experimental Results.- 8.1 The Test Suite.- 8.2 The Test Suite: Plan Synthesis Results.- 8.2.1 The Utility of Limited Graph Expansion and Satisfaction Propagation.- 8.3 The Rescue Problem.- 8.3.1 The Shortest Plan.- 8.3.2 A Five Step Plan.- 8.3.3 A Nine Step Plan: The People are Evacuated.- 8.3.4 An Eleven Step Plan: All People and Possessions Evacuated.- 8.3.5 A Plan with No Compromises.- 8.4 Summary.- 9 Conclusion.- 9.1 A Summary.- 9.1.1 Dynamic Flexible Constraint Satisfaction.- 9.1.2 The Application of Dynamic and Dynamic Flexible CSP to AI Planning.- 9.2 Future Work.- 9.2.1 Enrichment of the DFCSP Matrix.- 9.2.2 Improvement of GP-rrDCSP.- 9.2.3 Further Development of Flexible Graphplan.- 9.2.4 Further Applications.- 9.3 And Finally.- References.- A Pseudo-code.- A.1 Backtrack.- A.2 Backjump.- A.3 Conflict-directed Backjump.- A.4 Backmark.- A.5 Revise().- A.6 AC-1().- A.7 AC-3().- A.8 AC-1/4().- A.9 Branch and Bound.- B Proofs.- B.1 Soundness and Completeness of FLC.- B.3 Soundness and Completeness of Flexible Graphplan.- D Planning Problems.- D.1 The Test Suite.- D.1.1 Domain Operators.- D.1.2 Problem 1.- D.1.3 Problem 2.- D.1.4 Problem 3.- D.1.5 Problem 4.- D.1.6 Problem 5.- D.1.7 Problem 6.- D.1.8 Problem 7.- D.1.9 Problem 8.- D.1.10 Problem 9.- D.1.11 Problem 10.- D.1.12 Problem 11.- D.1.13 Problem 12.- D.2 The Rescue Problem.- D.2.1 Domain Operators.- D.2.2 Problem Specification.

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