A logically sound method for uncertain reasoning with quantified conditionals

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1244, Issue , 1997, Pages 365-379

  Kern Isberner, Gabriele ^a  

^a UNIVERSITY OF HAGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

KNOWLEDGE REPRESENTATION;

COMMONSENSE KNOWLEDGE; KNOWLEDGE REPRESENTATION AND REASONING; MINIMUM CROSS ENTROPY; UNCERTAIN REASONING;

ENTROPY;

EID: 84947808378     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0035635     Document Type: Conference Paper

References (24)

1. E.W. Adams. The Logic of Conditionals. D. Reidel, Dordrecht, 1975.
2. P.G. Calabrese. Deduction and inference using conditional logic and probability. In R. Goodman, M.M. Gupta, H.T. Nguyen, and G.S. Rogers, editors, Conditional Logic in Expert Systems, pages 71-100. Elsevier, North Holland, 1991.
3. R.T. Cox. Probability, frequency and reasonable expectation. American Journal of Physics, 14(1): 1-13, 1946.
4. D. Dubois and H. Prade. Conditional objects and non-monotonic reasoning. In Proceedings 2nd Int. Conference on Principles of Knowledge Representation and Reasoning (KR’91), pages 175-185. Morgan Kaufmann, 1991.
5. D. Dubois, H. Prade, and J.-M. Toucas. Inference with imprecise numerical quantifieres. In Z.W. Ras and M. Zemankova, editors, Intelligent Systems - state of the art and future directions, pages 52-72. Ellis Horwood Ltd., Chichester, England, 1990.
6. M. Goldszmidt, P. Morris, and J. Pearl. A maximum entropy approach to nonmonotonic reasoning. In Proceedings AAAI-90, pages 646-652, Boston, 1990.
7. I.J. Good. Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. Ann. Math. Statist., 34:911-934, 1963.
8. A.J. Grove, J.Y. Halpern, and D. Koller. Random worlds and maximum entropy. J. of Artificial Intelligence Research, 2:33-88, 1994.
9. E.T. Jaynes. Papers on Probability, Statistics and Statistical Physics. D. Reidel Publishing Company, Dordrecht, Holland, 1983.
10. R.W. Johnson and J.E. Shore. Comments on and correction to “Axiomatic derivation of the principle of maximum entropy and the principle of minimum crossentropy”. IEEE Transactions on Information Theory, IT-29(6):942-943, 1983.
11. G. Kern-Isberner. Characterizing the principle of minimum cross-entropy within a conditional logical framework. Informatik Fachbericht 206, FernUniversitat Hagen, 1996.
12. G. Kern-Isberner. Conditional logics and entropy. Informatik Fachbericht 203, FernUniversitaet Hagen, 1996.
13. S. Kullback. Information Theory and Statistics. Dover, New York, 1968.
14. S.L. Lauritzen and D.J. Spiegelhalter. Local computations with probabilities in graphical structures and their applications to expert systems. Journal of the Royal Statistical Society B, 50(2):415-448, 1988.
15. N. Rescher. Many-Valued Logic. McGraw-Hill, New York, 1969.
16. D. Nute. Topics in Conditional Logic. D. Reidel Publishing Company, Dordrecht, Holland, 1980.
17. J.B. Paris and A. Vencovská. A note on the inevitability of maximum entropy. International Journal of Approximate Reasoning, 14:183-223, 1990.
18. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, Ca., 1988.
19. W. Rödder and C.-H. Meyer. Coherent knowledge processing at maximum entropy by spirit. In E. Horvitz and F. Jensen, editors, Proceedings 12th Conference on Uncertainty in Artificial Intelligence, pages 470-476, San Francisco, Ca., 1996. Morgan Kaufmann.
20. J.E. Shore. Relative entropy, probabilistic inference and AI. In L.N. Kanal and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 211-215. North-Holland, Amsterdam, 1986.
21. J.E. Shore and R.W. Johnson. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on Information Theory, IT-26:26-37, 1980.
22. J.E. Shore and R.W. Johnson. Properties of cross-entropy minimization. IEEE Transactions on Information Theory, IT-27:472-482, 1981.
23. H. Thöne, U. Güntzer, and W. Kiessling. Towards precision of probabilistic bounds propagation. In D. Dubois, M.P. Wellmann, B. D’Ambrosio, and P. Smets, editors, Proceedings 8th Conference on Uncertainty in Artificial Intelligence, pages 315-322, San Mateo, Ca., 1992. Morgan Kaufmann.
24. J. Whittaker. Graphical models in applied multivariate statistics. John Wiley & Sons, New York, 1990.