Efficient methods for qualitative spatial reasoning

Journal of Artificial Intelligence Research

Volumn 15, Issue , 2001, Pages 289-318

  Renz, Jochen ^a   Nebel, Bernhard ^b  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FORMAL LOGIC; HEURISTIC METHODS; PHASE TRANSITIONS; PROBLEM SOLVING;

SPATIAL REASONING; TEMPORAL REASONING;

COMPUTATIONAL METHODS;

EID: 7544224860     PISSN: 10769757     EISSN: None     Source Type: Journal    
DOI: 10.1613/jair.872     Document Type: Article

References (27)

1. Achlioptas, D., Kirousis, L., Kranakis, E., Krizanc, D., Molloy, M., & Stamatiou, Y. (1997). Random constraint satisfaction: a more accurate picture. In 3rd Conference on the Principles and Practice of Constraint Programming (CP'97), Vol. 1330 of LNCS, pp. 107-120. Springer-Verlag.
2. Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843.
3. Bennett, B. (1994). Spatial reasoning with propositional logic. In Doyle, J., Sandewall, E., & Torasso, P. (Eds.), Principles of Knowledge Representation and Reasoning: Proceedings of the 4th International Conference, pp. 51-62, Bonn, Germany. Morgan Kaufmann.
4. Bennett, B. (1997). Logical Representations for Automated Reasoning about Spatial Relationships. Ph.D. thesis, School of Computer Studies, The University of Leeds.
5. Cheeseman, P., Kanefsky, B., & Taylor, W. M. (1991). Where the really hard problems are. In Proceedings of the 12th International Joint Conference on Artificial Intelligence, pp. 331-337, Sydney, Australia. Morgan Kaufmann.
6. Gent, I., Maclntyre, E., Prosser, P., Smith, B., & Walsh, T. (2001). Random constraint satisfaction: Flaws and structure. CONSTRAINTS, 6(4), 345-372.
7. Gent, I., MacIntyre, E., Prosser, P., & Walsh, T. (1996). The constrainedness of search. In Proceedings of the 13th National Conference on AI (AAAI'96), pp. 246-252.
8. Golumbic, M. C., & Shamir, R. (1993). Complexity and algorithms for reasoning about time: A graph-theoretic approach. Journal of the Association for Computing Machinery, 40(5), 1128-1133.
9. Grigni, M., Papadias, D., & Papadimitriou, C. (1995). Topological inference. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 901-906, Montreal, Canada.
10. Haralick, R. M., & Elliot, G. L. (1980). Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14, 263-313.
11. Huberman, B., Lukose, R., & Hogg, T. (1997). An economics approach to hard computational problems. Science, 275, 51-54.
12. Ladkin, P. B., & Reinefeld, A. (1992). Effective solution of qualitative interval constraint problems. Artificial Intelligence, 57(1), 105-124.
13. Ladkin, P. B., & Reinefeld, A. (1997). Fast algebraic methods for interval constraint problems. Annals of Mathematics and Artificial Intelligence, 19(3,4).
14. Mackworth, A. K. (1977). Consistency in networks of relations. Artificial Intelligence, 8, 99-118.
15. Mackworth, A. K., & Freuder, E. C. (1985). The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25, 65-73.
16. Montanari, U. (1974). Networks of constraints: fundamental properties and applications to picture processing. Information Science, 7, 95-132.
17. Nebel, B. (1995). Computational properties of qualitative spatial reasoning: First results. In Wachsmuth, I., Rollinger, C.-R., & Brauer, W. (Eds.), KI-95: Advances in Artificial Intelligence, Vol. 981 of Lecture Notes in Artificial Intelligence, pp. 233-244, Bielefeld, Germany. Springer-Verlag.
18. Nebel, B. (1997). Solving hard qualitative temporal reasoning problems: Evaluating the efficiency of using the ORD-Horn class. CONSTRAINTS, 3(1), 175-190.
19. Randell, D. A., Cohn, A. G., & Cui, Z. (1992a). Computing transitivity tables: A challenge for automated theorem provers. In Proceedings of the 11th CADE. Springer-Verlag.
20. Randell, D. A., Cui, Z., & Cohn, A. G. (1992b). A spatial logic based on regions and connection. In Nebel, B., Swartout, W., & Rich, C. (Eds.), Principles of Knowledge Representation and Reasoning: Proceedings of the 3rd International Conference, pp. 165-176, Cambridge, MA. Morgan Kaufmann.
21. Renz, J. (1999). Maximal tractable fragments of the Region Connection Calculus: A complete analysis. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp. 448-454, Stockholm, Sweden.
22. Renz, J. (2000). Qualitative Spatial Reasoning with Topological Information. Ph.D. thesis, Institut für Informatik, Albert-Ludwigs-Universität Freiburg.
23. Renz, J., & Nebel, B. (1998). Efficient methods for qualitative spatial reasoning. In Proceedings of the 13th European Conference on Artificial Intelligence, pp. 562-566, Amsterdam, The Netherlands. Wiley.
24. Renz, J., & Nebel, B. (1999). On the complexity of qualitative spatial reasoning: A maximal tractable fragment of the Region Connection Calculus. Artificial Intelligence, 108(1-2), 69-123.
25. Selman, B., Levesque, H. J., & Mitchell, D. (1992). A new method for solving hard satisfiability problems. In Proceedings of the 10th National Conference of the American Association for Artificial Intelligence, pp. 440-446, San Jose, CA. MIT Press.
26. van Beek, P., & Manchak, D. W. (1996). The design and experimental analysis of algorithms for temporal reasoning. Journal of Artificial Intelligence Research, 4, 1-18.
27. Williams, C. P., & Hogg, T. (1994). Exploiting the deep structure of constraint problems. Artificial Intelligence, 70, 73-117.