Using regression-match graphs to control search in planning

Artificial Intelligence

Volumn 109, Issue 1, 1999, Pages 111-159

  McDermott, Drew ^a  

^a YALE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; ESTIMATION; GRAPH THEORY; HEURISTIC METHODS; PROBLEM SOLVING; REGRESSION ANALYSIS;

CLASSICAL PLANNING; HEURISTIC ESTIMATOR; REGRESSION MATCH GRAPHS;

ARTIFICIAL INTELLIGENCE;

EID: 0033522716     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(99)00010-7     Document Type: Article

References (38)

1. J.L. Ambite, C.A. Knoblock, Planning by rewriting: Efficiently generating high-quality plans, in: Proc. AAAI-97, Providence, RI, 1997.
2. C. Anderson, D. Weld, D. Smith, Conditional effects in graphplan, in: Proc. 4th Internat. Conference AI Planning Systems, 1998.
3. A. Barrett, D.S. Weld, Partial-order planning: Evaluating possible efficiency gains, Artificial Intelligence 67 (1) (1994) 71-112.
4. A.L. Blum, M.L. Furst, Fast planning through planning graph analysis, in: Proc. IJCAI-95, Montreal, Quebec, 1995.
5. A.L. Blum, M.L. Furst, Fast planning through planning graph analysis, Artificial Intelligence 90 (1-2) (1997) 279-298.
6. B. Bonet, G. Loerincs, H. Geffner, A fast and robust action selection mechanism for planning, in: Proc. AAAI-97, Providence, RI, 1997.
7. E. Charniak, D. McDermott, Introduction to Artificial Intelligence, Addison-Wesley, Reading, MA, 1985.
8. E. Charniak, C. Riesbeck, D. McDermott, J. Meehan, Artificial Intelligence Programming, 2nd Ed., Lawrence Erlbaum Associates, 1987.
9. K. Currie, A. Tate, O-plan: The open planning architecture, Artificial Intelligence 52 (1) (1991) 49-86.
10. G.W. Ernst, A. Newell, GPS: A Case Study in Generality and Problem Solving, Academic Press, New York, 1969.
11. K. Erol, D. Nau, V.S. Subrahmanian, Complexity, decidability and undecidability results for domain-independent planning, in: D. McDermott, J. Hendler (Eds.), Artificial Intelligence, Vol. 76, Special Issue on Planning and Scheduling, 1995, pp. 75-88.
12. R. Fikes, P.E. Hart, N.J. Nilsson, Learning and executing generalized robot plans, Artificial Intelligence 3 (4) (1972) 349-371.
13. R. Fikes, N.J. Nilsson, Strips: A new approach to the application of theorem proving to problem solving, Artificial Intelligence 2 (1971) 189-208.
14. E. Fink, M. Veloso, Prodigy Planning Algorithm, Technical Report 94-123, CMU School of Computer Science, Pittsburgh, PA, 1994.
15. A. Gerevini, L.K. Schubert, Accelerating partial-order planners: Some techniques for effective search control and pruning, J. Artif. Intell. Res. 5 (1996) 95-137.
16. W.D. Harvey, M.L. Ginsberg, Limited discrepancy search, in: Proc. IJCAI-95, Montreal, Quebec, 1995, pp. 607-613.
17. D. Joslin, M. Pollack, Passive and active decision postponement in plan generation, in: Proc. 3rd European Workshop on Planning, 1995.
18. S. Kambhampati, Universal classical planner: An algorithm for unifying state-space and plan-space planning, in: Proc. AAAI-95, 1995.
19. S. Kambhampati, On the role of disjunctive representations and constraint propagation in refinement planning, in: Proc. Internat. Conference on the Principles of Knowledge Representation and Reasoning (KR-96), 1996.
20. S. Kambhampati, C.A. Knoblock, Q. Yang, Planning as refinement search: A unified framework for evaluating design tradeoffs in partial-order planning, in: D. McDermott, J. Hendler (Eds.), Artificial Intelligence, Vol. 76, Special Issue on Planning and Scheduling, 1995, pp. 167-238.
21. H.A. Kautz, D. McAllester, B. Selman, Encoding plans in propositional logic, in: Proc. Internat. Conference on the Principles of Knowledge Representation and Reasoning (KR-96), 1996.
22. J. Koehler, B. Nebel, J. Hoffmann, Y. Dimopoulos, Extending planning graphs to an adl subset, in: Proc. European Conference on Planning, 1997.
23. D. McDermott, Revised Nisp Manual, Technical Report 642, Yale Computer Science Department, New Hawen, CT, 1988.
24. D. McDermott, A heuristic estimator for means-ends analysis in planning, in: Proc. International Conference on AI Planning Systems 3, 1996, pp. 142-149.
25. D. McDermott, The planning domain definition language manual, Technical Report 1165, Yale Computer Science, New Hawen, CT, 1998. (CVC Report 98-003.)
26. A. Newell, H. Simon, GPS: A Program that Simulates Human Thought, In: R. Oldenbourg KG, Lernende Automaten, pp. 279-293. Reprinted in Feigenbaum and Feldman 1963, 1961.
27. N.J. Nilsson, Principles of Artificial Intelligence, Tioga Publishing Company, Palo Alto, CA, 1980.
28. P. Norvig, Paradigms of Artificial Intelligence Programming: Case Studies in Lisp, Morgan Kaufmann, San Mateo, CA, 1992.
29. E.P.D. Pednault, Toward a Mathematical Theory of Plan Synthesis, Ph.D. Thesis, 1986.
30. E.P.D. Pednault, Adl: Exploring the middle ground between strips and the situation calculus, in: Proc. Internat. Conference on the Principles Knowledge Representation and Reasoning, Toronto, Ontario, Vol. 1, 1989, pp. 324-332.
31. M. Stefik, Planning with constraints, Artificial Intelligence 16 (2) (1980) 111-139.
32. G.J. Sussman, A Computer Model of Skill Acquisition, American Elsevier, New York, 1975.
33. R. Tsuneto, D. Nau, J. Hendler, Plan-refinement strategies and search-space size, in: Proc. 4th European Conference on Planning (ECP-97), 1997.
34. M. Veloso, P. Stone, Flees: Planning with a flexible commitment strategy, J. Artif. Intel. Res. 3 (1995) 25-52.
35. M. Veloso, J. Blythe, Linkability: Examining causal link commitments in partial-order planning, in: Proc. AIPS-94, 1994.
36. M. Veloso, J. Carbonell, Derivational analogy in progigy: Automating case acquisition, storage, and utilization, Machine Learning 10 (1993) 249-278.
37. D. Weld, An introduction to least-commitment planning, AI Magazine, 1994.
38. D. Wilkins, Practical Planning: Extending the Classical AI Planning Paradigm, Morgan Kaufmann, San Mateo, CA, 1988.