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Belief Revision and Defeasible Reasoning

Belief revision (also called truth maintenance) and defeasible reasoning are aspects of the general issue of belief change---what to do with a knowledge base when: a contradiction is uncovered; beliefs about the world must change when change occurs in the world; an informant changes his/her opinion; multiple informants disagree with each other.
Contents
Papers on Belief Revision and Defeasible Reasoning

Papers on Belief Revision and Defeasible Reasoning

  1. Ari I. Fogel and Stuart C. Shapiro, On the Use of Epistemic Ordering Functions as Decision Criteria for Automated and Assisted Belief Revision in SNePS: (Preliminary Report). In Sebastian Sardina and Stavros Vassos, Eds., Proceedings of the Ninth International Workshop on Non-Monotonic Reasoning, Action, and Change (NRAC'11), Technical Report RMIT-TR-11-02, School of Computer Science and Information Technology, RMIT University, Melbourne, Australia, July, 2011, 31-38.

  2. Ari I. Fogel, On the Use of Epistemic Ordering Functions as Decision Criteria for Automated and Assisted Belief Revision in SNePS, MS Thesis, Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, June 1, 2011.

  3. Haythem O. Ismail and Nasr Kasrin, Focused Belief Revision as a Model of Fallible Relevance-Sensitive Perception, Proceedings of the 33rd German AI Conference (KI 2010), Springer-Verlag, Berlin, 2010, 126-134.

  4. Haythem O. Ismail and Nasr S. Kasrin, High-Level Perception as Focused Belief Revision, Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010), IOS Press, Amsterdam, 2010, 1145-1146.

  5. Haythem Ismail, A Reason Maintenance Perspective on Relevant Ramsey Conditionals, Logic Journal of the IGPL, 18, 4 (August 2009), 508-529. doi:10.1093/jigpal/jzp036.

  6. Haythem O. Ismail, Reason Maintenance and the Ramsey Test. In Christoph Beierle and Gabriele Kern-Isberner, Eds., Dynamics of Knowledge and Belief: Proceedings of the Workshop at the 30th German Conference on Artificial Intelligence (KI-2007), 2007, 42-56.

  7. Frances L. Johnson and Stuart C. Shapiro, Base Belief Change and Optimized Recovery. In Loris Penserini, Pavlos Peppas, and Anna Perini, Eds., STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium, Frontiers in Artificial Intelligence and Applications, vol. 142, IOS Press, Amsterdam, 2006, 162-173.

  8. Frances L. Johnson and Stuart C. Shapiro, Reconsideration on Non-Linear Base Orderings. In Loris Penserini, Pavlos Peppas, and Anna Perini, Eds., STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium, Frontiers in Artificial Intelligence and Applications, vol. 142, IOS Press, Amsterdam, 2006, 261-262.

  9. Frances L. Johnson, Dependency-Directed Reconsideration: An Anytime Algorithm for Hindsight Knowledge-Base Optimization, Ph.D. Dissertation, Department of Computer Science and Engineering, University at Buffalo, The State University of New York, January 11, 2006.

  10. Frances L. Johnson and Stuart C. Shapiro, Improving Recovery for Belief Bases. In Leora Morgenstern and Maurice Pagnucco, Eds., IJCAI-05 Workshop on Nonmonotonic Reasoning, Action, and Change (NRAC'05): Working Notes, IJCAII, Edinburgh, 2005, 65-70.

  11. Frances L Johnson and Stuart C. Shapiro, Dependency-Directed Reconsideration: Belief Base Optimization for Truth Maintenance Systems, Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), AAAI Press, Menlo Park, CA, 2005, 313-320.

  12. Frances L. Johnson and Stuart C. Shapiro, Dependency-Directed Reconsideration. In K. Forbus, D. Gentner, & T. Reigier, Eds., Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society (CogSci2004), Lawrence Erlbaum Assoc., Mahwah, NJ, 2005, p. 1573.

  13. Frances L. Johnson and Stuart C. Shapiro, Knowledge State Reconsideration: Hindsight Belief Revision (student abstract), Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI-04), AAAI Press/The MIT Press, Menlo Park, CA, 2004, 956-957.

  14. Karen Ehrlich, Default Reasoning Using Monotonic Logic: Nutter's Modest Proposal Revisited, Revised and Implemented, Proceedings of the 15th Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2004), Roosevelt University, Chicago, IL, 48-54

  15. Bharat Bhushan, Preferential Ordering of Beliefs for Default Reasoning, MS Thesis, Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, April 28, 2003. (ps version)

  16. Frances L. Johnson and Stuart C. Shapiro, Redefining Belief Change Terminology for Implemented Systems, Leopoldo Bertossi & Jan Chomicki, Eds., Working Notes for the IJCAI 2001 Workshop on Inconsistency in Data and Knowledge, IJCAII & AAAI, Seattle, WA, August 6, 2001, 11-21. (ps version)

  17. Frances L. Johnson and Stuart C. Shapiro, Implementing Integrity Constraints in an Existing Belief Revision System. In C. Baral & M. Truszczynski, Eds., Proceedings of the 8th International Workshop on Non-Monotonic Reasoning NMR2000, 2000, unpaginated, 8 pages.

  18. Frances L. Johnson and Stuart C. Shapiro, Formalizing a Deductively Open Belief Space, Technical Report 2000-02, Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, January 24, 2000. (ps version)

  19. Frances L. Johnson and Stuart C. Shapiro, Finding and Resolving Contradictions in a Battle Scenario, Technical Report 99-09, Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, September 9, 1999. (ps version)

  20. Frances L. Johnson and Stuart C. Shapiro, Says Who?---Incorporating Source Credibility Issues into Belief Revision, Technical Report 99-08, Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, July 31, 1999. (ps version)

  21. Stuart C. Shapiro, Belief Revision and Truth Maintenance Systems: An Overview and a Proposal Technical Report 98-10, Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, December 31, 1998. (ps version)

  22. Cravo, Maria R. & Martins, João P., (1993), "SNePSwD: A Newcomer to the SNePS Family", Journal of Experimental and Theoretical Artificial Intelligence 5: 135-148.

  23. Deepak Kumar and Stuart C. Shapiro. Deductive efficiency, belief revision and acting. Journal of Experimental and Theoretical Artificial Intelligence (JETAI), 5(2&3):167-177, April-September 1993.

  24. Deepak Kumar & Stuart C. Shapiro, Deductive efficiency + belief revision: how they affect an ontology of actions and acting. In Working Notes of the AAAI 1992 Spring Symposium on Propositional Knowledge Representation, AAAI, March, 1992, 93-99.

  25. Stuart C. Shapiro. Relevance logic in computer science. Section 83 of Alan Ross Anderson and Nuel D. Belnap, Jr. and J. Michael Dunn et al. Entailment, Volume II, pages 553-563. Princeton University Press, Princeton, NJ, 1992.

  26. J. P. Martins and S. C. Shapiro. A model for belief revision. Artificial Intelligence, 35(1):25-79, 1988.

  27. S. S. Campbell and S. C. Shapiro. Using belief revision to detect faults in circuits. SNeRG Technical Note 15, Department of Computer Science, University at Buffalo, 1986.

  28. J. P. Martins and S. C. Shapiro. Belief revision in SNePS. In Proceedings of the Sixth Canadian Conference on Artificial Intelligence, pages 230-234. Presses de l'Université du Québec, 1986.

  29. J. P. Martins and S. C. Shapiro. Hypothetical reasoning. In Applications of Artificial Intelligence to Engineering Problems: Proceedings of The 1st International Conference, pages 1029-1042, Berlin, 1986. Springer-Verlag.

  30. J. P. Martins and S. C. Shapiro. Theoretical foundations for belief revision. In J. Y. Halpern, editor, Theoretical Aspects of Reasoning About Knowledge, pages 383-398. Morgan Kaufmann Publishers, Los Altos, CA, 1986.

  31. J. T. Nutter. Default reasoning in A.I. systems. Master's thesis, Technical Report 204, Department of Computer Science, University at Buffalo, 1983.

  32. J. T. Nutter. Default reasoning using monotonic logic: a modest proposal. In Proceedings of The National Conference on Artificial Intelligence, pages 297-300, Los Altos, CA, 1983. Morgan Kaufmann.

  33. J. P. Martins and S. C. Shapiro. Reasoning in multiple belief spaces. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83), pages 370-373, Los Altos, CA, 1983. Morgan Kaufmann.

  34. J. P. Martins. Reasoning in Multiple Belief Spaces. PhD thesis, Technical Report 203, Department of Computer Science, University at Buffalo, 1983.

  35. J. T. Nutter. What else is wrong with non-monotonic logics?: Representational and informational shortcomings. In Proceedings of the Fifth Annual Meeting of the Cognitive Science Society, page 5, Rochester, NY, 1983.

  36. J. P. Martins. Belief revision in MBR. In Proceedings of the 1983 Conference on Artificial Intelligence, Rochester, Michigan, 1983.

  37. J. T. Nutter. Defaults revisited or ``Tell me if you're guessing''. In Proceedings of the Fourth Annual Conference of the Cognitive Science Society, pages 67-69, Ann Arbor, MI, 1982. the Program in Cognitive Science of The University of Chicago and The University of Michigan.

  38. J. Martins and S. C. Shapiro. A belief revision system based on relevance logic and heterarchical contexts. Technical Report 175, Department of Computer Science, University at Buffalo, 1981.

  39. S. C. Shapiro and M. Wand. The relevance of relevance. Technical Report 46, Computer Science Department, Indiana University, Bloomington, IN, 1976.
Last modified: Mon Dec 17 16:10:39 2012
Stuart C. Shapiro <shapiro@cse.buffalo.edu>
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