Recent Advances in AI Planning

ion and Approximate DecisionTheoretic Planning. Journal of Artificial Intelligence 89(1–2): 219–283. Proving to Problem Solving. In Proceedingsof the First International Joint Conference on Artificial Intelligence, 219–239. MenloPark, Calif.: International Joint Conferences on Artificial Intelligence. Haas, A. 1987. The Case for Domain-Specific Frame Axioms. In The Frame Problem in Artificial Intelligence, Proceedings of the 1987 Workshop, 115–128. San Francisco, Calif.:Morgan Kaufmann. Haralick, R. M., and Elliott, G. L. 1980.Increasing Tree Search Efficiency for Constraint Satisfaction Problems. Journal ofArtificial Intelligence 14(3): 263–313. Irani, K. B., and Cheng, J. 1987. SubgoalOrdering and Goal Augmentation for Heuristic Problem Solving. In Proceedingsof the Tenth International Joint Conference on Artificial Intelligence, 1018–1024.Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence. Joslin, D., and Pollack, M. 1996. Is “Early Commitment” in Plan Generation Ever aGood Idea? In Proceedings of the Thirteenth National Conference on ArtificialIntelligence, 1188–1193. Menlo Park, Calif.: American Association for ArtificialIntelligence. Joslin, D., and Pollack, M. 1994. Least-Cost Flaw Repair: A Plan Refinement Strategy forPartial-Order Planning. In Proceedings of the Twelfth National Conference on Artifi-cial Intelligence, 1004–1009. Menlo Park, Calif.: American Association for ArtificialIntelligence. Kambhampati, S. 1998a. EBL and DDB forGRAPHPLAN. TR-99-008, Department of Computer Science and Engineering, ArizonaState University. Kambhampati, S. 1998b. On the Relationsbetween Intelligent Backtracking and Failure-Driven Explanation-Based Learning inConstraint Satisfaction and Planning. TR97-018, Department of Computer Science and Engineering, Arizona State University. Kambhampati, S. 1997a. Challenges in Bridging Plan Synthesis Paradigms. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 44–49. Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence. Kambhampati, S. 1997b. Refinement Planning as a Unifying Framework for Plan Synthesis. AI Magazine 18(2): 67–97. Kambhampati, S.; Knoblock, C.; and Yang, Q. 1995. Planning as Refinement Search: A Unified Framework for Evaluating Design Trade-Offs in Partial Order Planning. Jour-nal of Artificial Intelligence 76(1–2): 167–238. Kambhampati, R.; Lambrecht, E.; and Parker, E. 1997. Understanding and ExtendingGRAPHPLAN. In Proceedings of the Fourth European Conference on Planning, 260–272.Berlin: Springer-Verlag. Kambhampati, R.; Mali, A.; and Srivastava, B. 1998. Hybrid Planning for Partially Hierarchical Domains. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, 882–888. Menlo Park, Calif.: American Association for Artificial Intelli-gence. Kautz, H., and Selman, B. 1998a. BLACKBOX:A New Approach to the Application of Theorem Proving to Problem Solving. InAIPS98 Workshop on Planning as Combinatorial Search, 58–60. Pittsburgh, Penn.:Carnegie Mellon University. Kautz, H., and Selman, B. 1998b. The Roleof Domain-Specific Knowledge in the Planning as Satisfiability Framework. In Pro-ceedings of the Fourth International Conference on Artificial Intelligence PlanningSystems, 181–189. Menlo Park, Calif.: AAAI