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Representing first-order causal theories by logic programs

Published online by Cambridge University Press:  25 May 2011

PAOLO FERRARIS ,
JOOHYUNG LEE ,
YULIYA LIERLER ,
VLADIMIR LIFSCHITZ  and
FANGKAI YANG
PAOLO FERRARIS
Affiliation:
Google Inc., CA 94043, USA (e-mail: otto@cs.utexas.edu)
JOOHYUNG LEE
Affiliation:
School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287-8809, USA (e-mail: joolee@asu.edu)
YULIYA LIERLER
Affiliation:
Computer Science Department, University of Kentucky, Lexington, KY 40506-0046, USA (e-mail: yuliya@cs.utexas.edu)
VLADIMIR LIFSCHITZ
Affiliation:
Department of Computer Science, University of Texas at Austin, Austin, TX 78712-0233, USA (e-mail: vl@cs.utexas.edu, fkyang@cs.utexas.edu)
FANGKAI YANG
Affiliation:
Department of Computer Science, University of Texas at Austin, Austin, TX 78712-0233, USA (e-mail: vl@cs.utexas.edu, fkyang@cs.utexas.edu)
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Abstract

Nonmonotonic causal logic, introduced by McCain and Turner (McCain, N. and Turner, H. 1997. Causal theories of action and change. In Proceedings of National Conference on Artificial Intelligence (AAAI), Stanford, CA, 460–465) became the basis for the semantics of several expressive action languages. McCain's embedding of definite propositional causal theories into logic programming paved the way to the use of answer set solvers for answering queries about actions described in such languages. In this paper we extend this embedding to nondefinite theories and to the first-order causal logic.

Keywords

reasoning about actionsnonmonotonic causal logicanswer set programming
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 12 , Issue 3 , May 2012 , pp. 383 - 412
Creative Commons
This is a work of the U.S. Government and is not subject to copyright protection in the United States.
Copyright
Copyright © Cambridge University Press 2011. This is a work of the U.S. Government and is not subject to copyright protection in the United States.

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References

Akman, V., Erdoğan, S., Lee, J., Lifschitz, V. and Turner, H. 2004. Representing the Zoo World and the Traffic World in the language of the causal calculator. Artificial Intelligence 153, 1–2, 105–140.Google Scholar
Armando, A., Giunchiglia, E. and Ponta, S. E. 2009. Formal specification and automatic analysis of business processes under authorization constraints: An action-based approach. In Proceedings of the 6th International Conference on Trust, Privacy and Security in Digital Business (TrustBus'09), Linz, Austria.Google Scholar
Artikis, A., Sergot, M. and Pitt, J. 2009. Specifying norm-governed computational societies. ACM Transactions on Computational Logic 10, 1.Google Scholar
Caldiran, O., Haspalamutgil, K., Ok, A., Palaz, C., Erdem, E. and Patoglu, V. 2009. Bridging the gap between high-level reasoning and low-level control. In Proceedings of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR), 242–354.Google Scholar
Clark, K. 1978. Negation as failure. In Logic and Data Bases, Gallaire, H. and Minker, J., Eds. Plenum Press, New York, USA, 293–322.Google Scholar
Ferraris, P. 2005. Answer sets for propositional theories. In Proceedings of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR). Springer-Verlag, Berlin, 119–131.Google Scholar
Ferraris, P. 2006. Causal theories as logic programs. In Proceedings of Workshop on Logic Programming (WLP), Vienna, 35–44.Google Scholar
Ferraris, P. 2007. A logic program characterization of causal theories. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 366–371.Google Scholar
Ferraris, P., Lee, J. and Lifschitz, V. 2011. Stable models and circumscription. Artificial Intelligence 175, 236–263.Google Scholar
Ferraris, P., Lee, J., Lifschitz, V. and Palla, R. 2009. Symmetric splitting in the general theory of stable models. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 797–803.Google Scholar
Gebser, M., Grote, T. and Schaub, T. 2010. Coala: A compiler from action languages to ASP. In Proceedings of European Conference on Logics in Artificial Intelligence (JELIA), 169–181.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proceedings of International Logic Programming Conference and Symposium, Kowalski, R. and Bowen, K., Eds. MIT Press, Cambridge, MA, USA, 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.Google Scholar
Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N. and Turner, H. 2004. Nonmonotonic causal theories. Artificial Intelligence 153, 1–2, 49–104.Google Scholar
Giunchiglia, E. and Lifschitz, V. 1998. An action language based on causal explanation: Preliminary report. In Proceedings of National Conference on Artificial Intelligence (AAAI). AAAI Press, California, USA, 623–630.Google Scholar
Glivenko, V. 1929. Sur quelques points de la logique de M. Brouwer. Académie Royale de Belgique. Bulletins de la Classe des Sciences, se'rie 5, 15, 183–188.Google Scholar
Lee, J., Lierler, Y., Lifschitz, V. and Yang, F. 2010. Representing synonymity in causal logic and in logic programming21. In Proceedings of International Workshop on Nonmonotonic Reasoning (NMR), Toronto, Canada.Google Scholar
Lee, J. and Palla, R. 2009. System F2LP – computing answer sets of first-order formulas. In Proceedings of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR), Potsdam, Germany, 515–521.Google Scholar
Lifschitz, V. 1985. Computing circumscription. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Los Angeles, CA, USA, 121–127.Google Scholar
Lifschitz, V. 1994. Circumscription. In Handbook of Logic in AI and Logic Programming, Vol. 3, Gabbay, D., Hogger, C., and Robinson, J., Eds.. Oxford University Press, Oxford, UK, 298–352.Google Scholar
Lifschitz, V. 1997. On the logic of causal explanation. Artificial Intelligence 96, 451–465.Google Scholar
Lifschitz, V. 2008. What is answer set programming? In Proceedings of the AAAI Conference on Artificial Intelligence. MIT Press, Cambridge, MA, 1594–1597.Google Scholar
Lifschitz, V. and Ren, W. 2006. A modular action description language. In Proceedings of National Conference on Artificial Intelligence (AAAI), Boston, MA, 853–859.Google Scholar
Lifschitz, V. and Ren, W. 2007. The semantics of variables in action descriptions. In Proceedings of National Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 1025–1030.Google Scholar
Lifschitz, V. and Yang, F. 2010. Translating first-order causal theories into answer set programming. In Proceedings of the European Conference on Logics in Artificial Intelligence (JELIA), Helsinki, Finland, 247–259.Google Scholar
Marek, V. and Truszczyński, M. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm: A 25-Year Perspective. Springer Verlag, Berlin, 375–398.Google Scholar
McCain, N. 1997. Causality in commonsense reasoning about actions 22, PhD thesis, University of Texas at Austin, Austin, TX.Google Scholar
McCain, N. and Turner, H. 1997. Causal theories of action and change. In Proceedings of National Conference on Artificial Intelligence (AAAI), Stanford, CA, 460–465.Google Scholar
McCarthy, J. 1986. Applications of circumscription to formalizing common sense knowledge. Artificial Intelligence 26, 3, 89–116.Google Scholar
Mints, G. 2000. A Short Introduction to Intuitionistic Logic. Kluwer, Dordrecht, Netherlands.Google Scholar
Niemelä, I. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 241–273.Google Scholar
Ren, W. 2009. A modular language for describing actions,23 PhD thesis, University of Texas at Austin, Austin, TX.Google Scholar
Shanahan, M. 1997. Solving the Frame Problem: A Mathematical Investigation of the Common Sense Law of Inertia. MIT Press, Cambridge, MA, USA.Google Scholar