Event-Based Decompositions for Reasoning about External Change in Planners

Proceedings of the 3rd Artificial Intelligence Planning Systems Conference, AIPS 1996

Volumn , Issue , 1996, Pages

  Blythe, Jim ^a  

^a Carnegie Mellon University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHAINS; FORMAL METHODS; PROBLEM SOLVING; QUERY PROCESSING; SEMANTICS;

EVENT MODEL; EVENT-BASED; FORMAL SEMANTICS; PLANNING AGENTS; PLANNING DOMAINS; PROBABILISTIC COMPUTATION; REACHABILITY ANALYSIS; REAL WORLD DOMAIN; UNCERTAINTY;

MARKOV PROCESSES;

EID: 85166366534     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Arnborg, S. 1985. Efficient Algorithms for Combinatorial Problems on Graphs with Bounded Decomposability-A Survey. BIT 25:2-23.
2. Bayardo, R. J. and Miranker, D. P. 1994. An Optimal Backtrack Algorithm for Tree-Structured Constraint Satisfaction Problems. Artificial Intelligence 71(1):159-181.
3. Bayardo, R. J. and Miranker, D. P. 1995. On the Space-Time Trade-Off in Solving Constraint Satisfaction Problems. In Proc. 14th Intl. Joint Conf. on Artificial Intelligence, 558-562.
4. Dechter, R. 1990. Enhancement Schemes for Constraint Processing: Backjumping, Learning, and Cutset Decomposition. Artificial Intelligence 41(3):273-312.
5. Dechter, R. 1992. Constraint Networks. In Encyclopedia of Artificial Intelligence, 2nd ed., 276-285. New York, N Y: Wiley.
6. Dechter, R. and Pearl, J. 1987. Network-Based Heuristics for Constraint-Satisfaction Problems. Artificial Intelligence 34:1¬ 38.
7. Freuder, E. C. 1985. A Suficient Condition for Backtrack-Bounded Search. J. Association for Computing Machinery 32(4):755-761.
8. Freuder, E. C. and Quinn, M. J. 1985. Taking Advantage of Stable Sets of Variables in Constraint Satisfaction Problems. In Proc. Ninth Intl. Joint Conf. on Artiicial Intelligence, 1076¬ 1078.
9. Frost, D. and Dechter, R. 1994. Dead-End Driven Learning. In Proc. Twelfth National Conf. on Artiicial Intelligence, 294¬ 300.
10. Garey, M. R. and Johnson, D. S. 1979. Computers andIntrac¬ tability: A Guide to the Theory ofNP-Completeness. New York, NY: W. H. Freeman and Co..
11. Gavril, F. 1977. Some NP-complete Problems on Graphs, Proc. 11th Conf. on Information Sciences and Systems, 91-95. Cited through (Garey & Johnson 1979, pg. 201).
12. Gent, I. P. and Walsh, T. 1996. The Satisfiability Constraint Gap. Artificial Intelligence 81.
13. Ginsberg, M. L. 1993. Dynamic Backtracking. J. Artificial Intelligence Research 1:25-46.
14. Ginsberg, M. L. and McAllester, D. A. 1994. GSAT and Dynamic Backtracking. In Proc. Fourth International Conf. on Principles of Knowledge Representation and Reasoning, 226¬ 237.
15. Haralick, R. M. and Elliot, G. L. 1980. Increasing Tree Search Efficiency for Constraint Satisfaction Problems. Artificial Intelligence 14:263-313.
16. Nadel, B. 1988. Tree Search and Arc Consistency in Con¬ straint-Satisfaction Algorithms. In.Search in Artificial Intelligence, 287-342. New York, NY: Springer-Verlag.
17. Prosser, P. 1993. Hybrid Algorithms for the Constraint Satis¬ faction Problem. Computational Intelligence 9(3):268-299.
18. Swami, A. 1989. Optimization of Large Join Queries: Combining Heuristics and Combinatorial Techniques. In Proc. 1989 A C M SIGMOD Intl. Conf. on Management of Data, 367-376.