Reasoning about noisy sensors and effectors in the situation calculus

Artificial Intelligence

Volumn 111, Issue 1, 1999, Pages 171-208

  Bacchus, Fahlem ^a   Halpern, Joseph Y ^b   Levesque, Hector J ^c  

^a UNIVERSITY OF WATERLOO   (Canada)

^b Department of Computer Science and School of Operations Research and Information Engineering   (United States)

^c UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; END EFFECTORS; MATHEMATICAL MODELS; PROBABILISTIC LOGICS; SENSORS;

GENERAL LOGICAL THEORY OF ACTIONS; SITUATION CALCULUS;

ARTIFICIAL INTELLIGENCE;

EID: 0033165498     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(99)00031-4     Document Type: Article

References (40)

1. F. Bacchus, Representing and Reasoning with Probabilistic Knowledge, MIT Press, Cambridge, MA, 1990.
2. S. Bornscheuer, M. Thielscher, Explicit and implicit indeterminism: Reasoning about uncertain and contradictory specifications of dynamic systems, J. Logic Programming 31 (1-3) (1997) 119-156.
3. S. Bruning, S. Holldobler, J. Schneeberger, U.C. Sigmund, M. Thielscher, Disjunction in resource-oriented deductive planning, Technical Report Forschungbericht AIDA-94-03, Fachgebiet Intellektik, Fachbereich Informatik, Technische Hochschule Darmstadt, Darmstadt, Germany, 1994.
4. A. Darwiche, M. Goldszmidt, Action networks: A framework for reasoning about actions and change under uncertainty, in: Uncertainty in Artificial Intelligence, Proc. Annual Conference, 1994, pp. 136-144.
5. G. de Giacomo, M. Lenzirini, PDL-based framework for reasoning about actions, in: Proc. 4th Congress of the Italian Association for Artificial Intelligence, 1995, pp. 103-114.
6. T.L. Dean, M.P. Wellman, Planning and Control, Morgan Kaufmann, San Mateo, CA, 1991.
7. L. Dorst, M. van Lambalgen, F. Voorbraak (Eds.), Reasoning with Uncertainty in Robotics, Proc. International Workshop, Amsterdam, The Netherlands, Lecture Notes in Artificial Intelligence, Vol. 1093, Springer, Berlin, 1995.
8. D. Dubois, H. Prade, Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence 4 (1988) 244-264.
9. C. Elkan, Reasoning about action in first-order logic, in: Proc. Canadian Artificial Intelligence Conference, Vancouver, BC, 1992.
10. O. Etzioni, S. Hanks, D. Weld, D. Draper, N. Lesh, M. Williamson, An approach to planning with incomplete information, in: Proc. International Conference on Principles of Knowledge Representation and Reasoning (KR-92), Cambridge, MA, 1992.
11. R. Fagin, J.Y. Halpern, Y. Moses, M.Y. Vardi, Reasoning about Knowledge, MIT Press, Cambridge, MA, 1995.
12. A.R. Haas, The case for domain-specific frame axioms, in: F.M. Brown (Ed.), The Frame Problem in Artificial Intelligence: Proc. 1987 Workshop, Morgan Kaufmann, San Mateo, CA, 1987, pp. 343-348.
13. J.Y. Halpern, An analysis of first-order logics of probability, Artificial Intelligence 46 (1990) 311-350.
14. J.Y. Halpern, R. Fagin, Modelling knowledge and action in distributed systems, Distributed Computing 3 (4) (1989) 159-179. A preliminary version appeared in: Proc. 4th ACM Symposium on Principles of Distributed Computing, 1985, with the title "A formal model of knowledge, action, and communication in distributed systems: Preliminary report".
15. J.Y. Halpern, M.R. Tuttle, Knowledge, probability, and adversaries, J. ACM 40 (4) (1993) 917-962.
16. R.A. Howard, J.E. Matheson, Influence diagrams, in: R.A. Howard, J.E. Matheson (Eds.), Readings on the Principles and Applications of Decision Analysis, Strategic Decisions Group, Menlo Park, CA, 1984, pp. 721-762.
17. N. Kushmerick, S. Hanks, D. Weld, An algorithm for probabilistic planning, Artificial Intelligence 76 (1995) 239-286.
18. G. Lakemeyer, Only knowing in the situation calculus, in: L. Aiello, J. Doyle, S. Shapiro (Eds.), Proc. International Conference on Principles of Knowledge Representation and Reasoning (KR-96), Morgan Kaufmann, San Mateo, CA, 1996, pp. 14-25.
19. G. Lakemeyer, H. Levesque, AOL: A logic of acting, sensing, knowing and only knowing, in: Proc. International Conference on Principles of Knowledge Representation and Reasoning (KR-98), 1998, pp. 316-327.
20. Y. Lespérance, H.J. Levesque, S. Ruman, An experiment in using GOLOG to build a personal banking assistant, in: L. Cavedon, A. Rao, W. Wobcke (Eds.), Intelligent Agent Systems: Theoretical and Practical Issues, Lecture Notes in Artificial Intelligence, Vol. 1209, Springer, Berlin, 1997.
21. H.J. Levesque, R. Reiter, Y Lespérance, F. Lin, R. Scherl, GOLOG: A logic programming language for dynamic domains, J. Logic Programming 31 (1-3) (1997) 59-84.
22. D. Lewis, Probability of conditionals and conditional probabilities, Philos. Review 83 (5) (1976) 297-315.
23. F. Lin, H. Levesque, What robots can do: Robot programs and effective achievability, Artificial Intelligence 101 (1998)201-226.
24. F. Lin, R. Reiter, State constraints revisited, J. Logic Comput. 4 (5) (1994) 655-678.
25. D.V Lindley, Introduction to Probability and Statistics. Part 1. Probability, Cambridge University Press, Cambridge, UK, 1965.
26. D.V. Lindley, Introduction to Probability and Statistics. Part 2. Inference, Cambridge University Press, Cambridge, UK, 1965.
27. J. McCarthy, P.J. Hayes, Some philosophical problems from the standpoint of artificial intelligence, in: Machine Intelligence 4, Edinburgh University Press, Edinburgh, 1969, pp. 463-502.
28. R.C. Moore, A formal theory of knowledge and action, in: J.R. Hobbs, R.C. Moore (Eds.), Formal Theories of the Common Sense World, Ablex Publishing, Norwood, NJ, 1985, pp. 319-358.
29. J. Pearl, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, San Mateo, CA, 1988.
30. J. Pearl, Action as local surgery, in: Working Notes of AAAI Spring Symposium Series, Extending Theories of Actions, 1995, pp. 157-162.
31. E.P.D. Pednault, ADL: Exploring the middle ground between Strips and the situation calculus, in: R.J. Brachman, H.J. Levesque, R. Reiter (Eds.), Proc. International Conference on Principles of Knowledge Representation and Reasoning (KR-89), Toronto, Ont., 1989, pp. 324-332.
32. J.A. Pinto, Temporal reasoning in the situation calculus, Ph.D. Thesis, Department of Computer Science, University of Toronto, 1994.
33. D. Poole, Decision theory, the situation calculus and conditional plans, Linköping Electronic Articles in Computer and Information Science 3 (8) 1998. http://www.ep.liu.se/ea/cis/1989/008.
34. R. Reiter, The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression, in: V. Lifschitz (Ed.), Artificial Intelligence and Mathematical Theory of Computation, Academic Press, New York, 1991, pp. 359-380.
35. S.J. Rosenschein, L.P. Kaelbling, The synthesis of digital machines with provable epistemic properties, in: J.Y. Halpern (Ed.), Theoretical Aspects of Reasoning about Knowledge: Proc. 1986 Conference, Morgan Kaufmann, San Mateo, CA, 1986, pp. 83-97.
36. R.B. Scherl, H.J. Levesque, The frame problem and knowledge-producing actions, in: Proc. AAAI-93, Washington, DC, 1993, pp. 689-695.
37. L.K. Schubert, A monotonic solution to the frame problem in the situation calculus: An efficient method for worlds with fully specified actions, in: H.E. Kyburg, R.P. Loui, G.N. Carlson (Eds.), Knowledge Representation and Defeasible Reasoning, Kluwer Academic Press, London, 1990, pp. 23-67.
38. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976.
39. K. Tarn, J. Lloyd, Y. Lespérance, H. Levesque, F. Lin, D. Marcu, R. Reiter, M. Jenkin, Controlling autonomous robots with Golog, in: Proc. 10th Australian Joint Conference on Artificial Intelligence, Perth, Australia, 1997, pp. 1-12.
40. J.A. Tatman, R.D. Shachter, Dynamic programming and influence diagrams, IEEE Trans. Systems Man Cybernet. 20 (2) (1990) 365-379.