Refinement Planning as a Unifying Framework for Plan Synthesis

Planning -- the ability to synthesize a course of action to achieve desired goals -- is an important part of intelligent agency and has thus received significant attention within AI for more than 30 years. Work on efficient planning algorithms still continues to be a hot topic for research in AI and has led to several exciting developments i the past few years. This article provides a tutorial introduction to all the algorithms and approaches to the planning problem in AI. To fulfill this ambitious objective, I introduce a generalized approach to plan synthesis called refinement planning and show that in its various guises, refinement planning subsumes most of the algorithms that have been, or are being, developed. It is hoped that this unifying overview provides the reader with a brand-name-free appreciation of the essential issues in planning.

[1]  David Chapman,et al.  Planning for Conjunctive Goals , 1987, Artif. Intell..

[2]  Nicholas Kushmerick,et al.  An Algorithm for Probabilistic Least-Commitment Planning , 1994, AAAI.

[3]  A. Haas.  The Case for Domain-Specific Frame Axioms , 1987 .

[4]  Daniel S. Weld,et al.  Temporal Planning with Continuous Change , 1994, AAAI.

[5]  V. S. Subrahmanian,et al.  Complexity, Decidability and Undecidability Results for Domain-Independent Planning , 1995, Artif. Intell..

[6]  Subbarao Kambhampati,et al.  On the Nature and Role of Modal Truth Criteria in Planning , 1996, Artif. Intell..

[7]  Avrim Blum,et al.  Fast Planning Through Planning Graph Analysis , 1995, IJCAI.

[8]  Daniel S. Weld.  An Introduction to Least Commitment Planning , 1994, AI Mag..

[9]  Daniel S. Weld,et al.  Xii: Planning with Universal Quantification and Incomplete Information , 1994 .

[10]  S. Kambhampati,et al.  Universal classical planner: an algorithm for unifying state-space and plan-space planning , 1996 .

[11]  Mark Drummond,et al.  Situated Control Rules , 1989, KR.

[12]  Hector J. Levesque,et al.  A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.

[13]  Fahiem Bacchus,et al.  Using temporal logic to control search in a forward chaining planner , 1996 .

[14]  Subbarao Kambhampati,et al.  Synthesizing Customized Planners from Speci cationsBiplav Srivastava biplav , 2008 .

[15]  Anthony Barrett,et al.  Partial-Order Planning: Evaluating Possible Efficiency Gains , 1994, Artificial Intelligence.

[16]  Austin Tate,et al.  Generating Project Networks , 1977, IJCAI.

[17]  Subbarao Kambhampati,et al.  Design and Implementation of a Replay Framework Based on a Partial Order Planner , 1996, AAAI/IAAI, Vol. 1.

[18]  李幼升,et al.  Ph , 1989 .

[19]  Steven Minton,et al.  Total-Order and Partial-Order Planning: A Comparative Analysis , 1994, J. Artif. Intell. Res..

[20]  Oren Etzioni,et al.  Explanation-Based Learning: A Problem Solving Perspective , 1989, Artif. Intell..

[21]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Steve A. Chien.  Static and Completion Analysis for Planning Knowledge Base Development and Verification , 1996, AIPS.

[23]  Austin Tate,et al.  Interacting Goals And Their Use , 1975, IJCAI.

[24]  Bart Selman,et al.  Encoding Plans in Propositional Logic , 1996, KR.

[25]  Matthew L. Ginsberg.  A New Algorithm for Generative Planning , 1996, KR.

[26]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[27]  Martha E. Pollack,et al.  Is "Early Commitment" in Plan Generation Ever a Good Idea? , 1996, AAAI/IAAI, Vol. 2.

[28]  Subbarao Kambhampati,et al.  Planning and Scheduling , 1997, The Computer Science and Engineering Handbook.

[29]  James M. Crawford,et al.  Experimental Results on the Crossover Point in Random 3-SAT , 1996, Artif. Intell..

[30]  Bart Selman,et al.  Pushing the Envelope: Planning, Propositional Logic and Stochastic Search , 1996, AAAI/IAAI, Vol. 2.

[31]  Edwin P. D. Pednault,et al.  ADL and the State-Transition Model of Action , 1994, J. Log. Comput..

[32]  Subbarao Kambhampati,et al.  Planning as Refinement Search: A Unified Framework for Evaluating Design Tradeoffs in Partial-Order Planning , 1995, Artif. Intell..

[33]  Michael P. Wellman.  Dominance and Subsumption in Constraint-Posting Planning , 1987, IJCAI.

[34]  David A. McAllester,et al.  Systematic Nonlinear Planning , 1991, AAAI.

[35]  Drew McDermott,et al.  A Heuristic Estimator for Means-Ends Analysis in Planning , 1996, AIPS.

[36]  Daniel S. Weld,et al.  UCPOP: A Sound, Complete, Partial Order Planner for ADL , 1992, KR.

[37]  Jean-Jacques Fuchs,et al.  PlanERS-1: An expert planning system for generating spacecraft mission plane , 1990, Expert Planning Systems.

[38]  Subbarao Kambhampati,et al.  A Candidate Set Based Analysis of Subgoal Interactions in Conjunctive Goal Planning , 1996, AIPS.

[39]  Subbarao Kambhampati,et al.  On the Role of Disjunctive Representations and Constraint Propagation in Refinement Planning , 1996, KR.

[40]  Edwin P. D. Pednault,et al.  Synthesizing plans that contain actions with context‐dependent effects 1 , 1988, Comput. Intell..

[41]  James A. Hendler,et al.  A Validation-Structure-Based Theory of Plan Modification and Reuse , 1992, Artif. Intell..

[42]  David E. Wilkins,et al.  Domain-Independent Planning: Representation and Plan Generation , 1984, Artif. Intell..

[43]  C. Cordell Green,et al.  Application of Theorem Proving to Problem Solving , 1969, IJCAI.

[44]  Subbarao Kambhampati,et al.  Failure Driven Dynamic Search Control for Partial Order Planners: An Explanation Based Approach , 1996, Artif. Intell..