Get Logic Programming and Nonmonotonic Reasoning: 7th PDF

By Vladimir Lifschitz, Ilkka Niemelä

ISBN-10: 354020721X

ISBN-13: 9783540207214

ISBN-10: 3540246096

ISBN-13: 9783540246091

This e-book constitutes the refereed complaints of the seventh overseas convention on common sense Programming and Nonmonotonic Reasoning, LPNMR 2004, held in castle Lauderdale, Florida, united states in January 2004. The 24 revised complete papers provided including eight method descriptions have been rigorously reviewed and chosen for presentation. one of the subject matters addressed are declarative common sense programming, nonmonotonic reasoning, wisdom illustration, combinatorial seek, resolution set programming, constraint programming, deduction in ontologies, and making plans.

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Read Online or Download Logic Programming and Nonmonotonic Reasoning: 7th International Conference, LPNMR 2004, Fort Lauderdale, FL, USA, January 6-8, 2004, Proceedings PDF

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Additional resources for Logic Programming and Nonmonotonic Reasoning: 7th International Conference, LPNMR 2004, Fort Lauderdale, FL, USA, January 6-8, 2004, Proceedings

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J. J. Alferes, J. A. Leite, L. M. Pereira, H. Przymusinska, and T. C. Przymusinski. Dynamic logic programming. In Procs. of KR ’98. Morgan Kaufmann, 1998. 2. J. J. Alferes, J. A. Leite, L. M. Pereira, H. Przymusinska, and T. C. Przymusinski. Dynamic updates of non-monotonic knowledge bases. The Journal of Logic Programming, 45(1–3):43–70, September/October 2000. 3. K. R. Apt and R. N. Bol. Logic programming and negation: A survey. The Journal of Logic Programming, 19 & 20:9–72, May 1994. 4. F. Buccafurri, W.

Consider has two possible worlds, and (note the interplay between the default and rule 6 of In other words “randomness” undermines the default. Finally consider and Both programs are inconsistent. instead of 1/2. has one possible world has three possible worlds, and each with unnormalized probability 1/2. Hence instead of 1/2. (Let be a multiset of such that for some Then it can be shown that if is consistent then for every B and the sum of the values in is 1). 3 Representing Knowledge in P-log Now we give several examples of non-trivial probabilistic knowledge representation and reasoning performed in P-log.

Sets of sample points) of the classical theory. Second, P-log allows us to elaborate on defaults by adding probabilities as in Examples 6-7. Preferences among explanations, in the form of defaults, are often more easily Probabilistic Reasoning with Answer Sets 33 available from domain experts than are numerical probabilities. In some cases, we may want to move from the former to the latter as we acquire more information. P-log allows us to represent defaults, and later integrate numerical probabilities by adding to our existing program rather than modifying it.

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Logic Programming and Nonmonotonic Reasoning: 7th International Conference, LPNMR 2004, Fort Lauderdale, FL, USA, January 6-8, 2004, Proceedings by Vladimir Lifschitz, Ilkka Niemelä


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