Abstract
The paper gives a short informal introduction to the knowledge representation language P-Log. The language allows natural and elaboration tolerant representation of commonsense knowledge involving logic and probabilities. The logical framework of P-Log is Answer Set Prolog which can be viewed as a significant extension of Datalog. On the probabilistic side, the authors adopt the view which understands probabilistic reasoning as commonsense reasoning about degrees of belief of a rational agent, and use causal Bayes nets as P-log probabilistic foundation. Several examples are aimed at explaining the syntax and semantics of the language and the methodology of its use.
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Anh, H.T., Kencana Ramli, C.D.P., Damásio, C.V.: An implementation of extended p-log using xasp. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 739–743. Springer, Heidelberg (2008)
Baral, C.: Knowledge Representation, Reasoning, and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)
Baral, C., Gelfond, M., Rushton, N.: Probabilistic Reasoning with Answer Sets. In: Lifschitz, V., Niemelä, I. (eds.) LPNMR 2004. LNCS (LNAI), vol. 2923, pp. 21–33. Springer, Heidelberg (2003)
Baral, C., Gelfond, M., Rushton, N.: Probabilistic reasoning with answer sets. Journal of Theory and Practice of Logic Programming (TPLP) 9(1), 57–144 (2009)
Baral, C., Hunsaker, M.: Using the probabilistic logic programming language p-log for causal and counterfactual reasoning and non-naive conditioning. In: Proceedings of IJCAI 2007, pp. 243–249 (2007)
Gebser, M., Kaufman, B., Neumann, A., Schaub, T.: Conflict-deriven answer set enumeration. In: Baral, C., Brewka, G., Schlipf, J. (eds.) LPNMR 2007. LNCS (LNAI), vol. 4483, pp. 136–148. Springer, Heidelberg (2007)
Gelfond, M., Rushton, N.: Causal and Probabilistic Reasoning in p-log. In: Dechter, R., Geffner, H., Halpern, J. (eds.) Heuristics, Probabilities and Causality. A tribute to Judea Pearl, pp. 337–359. College Publications (2010)
Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of ICLP 1988, pp. 1070–1080 (1988)
Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9(3/4), 365–386 (1991)
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The dlv system for knowledge representation and reasoning. ACM Transactions on Computational Logic 7, 499–562 (2006)
Niemela, I., Simons, P., Soininen, T.: Extending and implementing the stable model semantics. Artificial Intelligence 138(1-2), 181–234 (2002)
Pearl, J.: Causality. Cambridge University Press, Cambridge (2000)
Pereira, L.M., Anh, H.T.: Evolution prospection in decision making. Intelligent Decision Technologies 3(3), 157–171 (2009)
Hilborn, R., Mangel, L.: The Ecological Detective. Princeton University Press, Princeton (1997)
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Gelfond, M. (2011). Knowledge Representation Language P-Log – A Short Introduction. In: de Moor, O., Gottlob, G., Furche, T., Sellers, A. (eds) Datalog Reloaded. Datalog 2.0 2010. Lecture Notes in Computer Science, vol 6702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24206-9_21
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DOI: https://doi.org/10.1007/978-3-642-24206-9_21
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