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Bibliography

Published online by Cambridge University Press:  05 July 2014

Michael Gelfond  and
Yulia Kahl
Michael Gelfond
Affiliation:
Texas Tech University
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Knowledge Representation, Reasoning, and the Design of Intelligent Agents
The Answer-Set Programming Approach
 , pp. 331 - 342
Publisher: Cambridge University Press
Print publication year: 2014

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References

DLV Web Page. http://www.dlvsystem.com/dlvsystem/index.php/Home.
Smodels Web Page. http://www.tcs.hut.fi/Software/smodels/.
Abiteboul, Serge, Robert, Hull, and Victor, Vianu. Foundations of Databases. Addison Wesley, 1995.Google Scholar
Aker, Erdi, Ahmetcan, Erdogan, Esra, Erdem, and Volkan, Patoglu. Causal Reasoning for Planning and Coordination of Multiple Housekeeping Robots. In LPNMR, pages 311–316, 2011.Google Scholar
Alferes, Jose Julio and Luis Moniz, Pereira. Reasoning with Logic Programming. Springer, 1996.CrossRefGoogle Scholar
Alviano, Mario, Wolfgang, Faber, Gianluigi, Greco, and Nicola, Leone. Magic Sets for Disjunctive Datalog Programs. Artificial Intelligence, 187:156–192, 2012.Google Scholar
Anh, Han The, Carroline D. Kencana, Ramli, and Carlos Viegas, Damasio. An Implementation of Extended P-Log Using XASP. In Proceedings of the 24th International Conference on Logic Programming, ICLP '08, pages 739–743. Springer-Verlag, 2008.Google Scholar
Apt, Krzysztof R., Howard, Blair, and Adrian, Walker. Towards a Theory of Declarative Knowledge. In J., Minker, editor, Foundations of Deductive Databases and Logic Programming, pages 89–148, Morgan Kaufmann, 1988.Google Scholar
Apt, Krzysztof R. and Roland, Bol. Logic Programming and Negation: A Survey. Journal of Logic Programming, 19:9–71, 1994.Google Scholar
Apt, Krzysztof R. and Kees, Doets. A New Definition of SLDNF-Resolution. Journal of Logic Programming, 28:177–190, 1994.Google Scholar
Baader, Franz, Diego, Calvanese, Deborah L., McGuinness, Daniele, Nardi, and Peter F., Patel-Schneider, editors. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, 2003.
Balai, Evgenii, Michael, Gelfond, and Yuanlin, Zhang. Towards Answer Set Programming with Sorts. In LPNMR, 2013.Google Scholar
Balduccini, Marcello. USA-Smart: Improving the Quality of Plans in Answer Set Planning. In PADL'04, Lecture Notes in Artificial Intelligence (LNCS), Jun 2004.Google Scholar
Balduccini, Marcello. CR-MODELS: An Inference Engine for CR-Prolog. In C., Baral, G., Brewka, and J., Schlipf, editors, Proceedings of the 9th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR'07), volume 3662 of Lecture Notes in Artificial Intelligence, pages 18–30. Springer, 2007.Google Scholar
Balduccini, Marcello. CR-Prolog as a Specification Language for Constraint Satisfaction Problems. In LPNMR, pages 402–408, 2009.Google Scholar
Balduccini, Marcello. Learning and Using Domain-Specific Heuristics in ASP Solvers. AI Communications, 24(2):147–164, 2011.Google Scholar
Balduccini, Marcello. An Answer Set Solver for Non-Herbrand Programs: Progress Report. In ICLP (Technical Communications), pages 49–60, 2012.Google Scholar
Balduccini, Marcello and Michael, Gelfond. Diagnostic Reasoning with A-Prolog. Journal of Theory and Practice of Logic Programming (TPLP), 3(4-5):425–461, Jul 2003.Google Scholar
Balduccini, Marcello and Michael, Gelfond. The AAA Architecture: An Overview. In AAAI Spring Symposium 2008 on Architectures for Intelligent Theory-Based Agents (AITA08), 2008.Google Scholar
Balduccini, Marcello, Michael, Gelfond, and Monica, Nogueira. A-Prolog as a Tool for Declarative Programming. In Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering (SEKE'2000), pages 63–72, 2000.Google Scholar
Balduccini, Marcello, Michael, Gelfond, and Monica, Nogueira. Answer Set Based Design of Knowledge Systems. Annals of Mathematics and Artificial Intelligence, 47: 183–219, 2006.CrossRefGoogle Scholar
Balduccini, Marcello and Sara, Girotto. Formalizing Psychological Knowledge in Answer Set Programming. In Twelfth International Conference on the Principles of Knowledge Representation and Reasoning (KR2010), May 2010.Google Scholar
Baral, Chitta. Knowledge Representation, Reasoning, and Declarative Problem Solving. Cambridge University Press, Jan 2003.CrossRefGoogle Scholar
Baral, Chitta and Juraj, Dzifcak. Solving Puzzles Described in English by Automated Translation to Answer Set Programming and Learning How to Do that Translation. In Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning (KR2012), 2012.Google Scholar
Baral, Chitta and Matt, Hunsaker. Using the Probabilistic Logic Programming Language P-log for Causal and Counterfactual Reasoning and Non-Naive Conditioning. In Proceedings of IJCAI-2007, pages 243–249.
Baral, Chitta and Michael, Gelfond. Logic Programming and Knowledge Representation. Journal of Logic Programming, 19(20):73–148, 1994.Google Scholar
Baral, Chitta and Michael, Gelfond. Reasoning Agents in Dynamic Domains. In Workshop on Logic-Based Artificial Intelligence. Kluwer Academic Publishers, 2000.Google Scholar
Baral, Chitta and Michael, Gelfond, and J. Nelson, Rushton. Probabilistic Reasoning with Answer Sets. TPLP, 9(1):57–144, 2009.Google Scholar
Baral, Chitta and Jorge, Lobo. Defeasible Specifications in Action Theories. In Proceedings of IJCAI-97, pages 1441–1446, 1997.Google Scholar
Baral, Chitta, Sheila A., McIlraith, and Tran Cao, Son. Formulating Diagnostic Problem Solving Using an Action Language with Narratives and Sensing. In Proceedings of the 2000 KR Conference, pages 311–322, 2000.Google Scholar
Bidoit, Nicole and Christine, Froidevaux. Minimalism Subsumes Default Logic and Circumscription in Stratified Logic Programming. In Proceedings of the Logic in Computer Science Conference, pages 89–97, 1987.Google Scholar
Bidoit, Nicole and Christine, Froidevaux. Negation by Default and Unstratifiable Logic Programs. Theoretical Computer Science, 79(1):86–112, 1991.Google Scholar
Blackburn, Patrick and Johan, Bos. Representation and Inference for Natural Language: A First Course in Computational Semantics. CSLI Publications, 2005.Google Scholar
Boenn, Georg, Martin, Brain, Marina De, Vos, and John, Fitch. Automatic Music Composition Using Answer Set Programming. TPLP, 11(2-3):397–427, 2011.Google Scholar
Boole, George. An Investigation of the Laws of Thought: On Which Are Founded the Mathematical Theories of Logic and Probabilities. George Boole's Collected Logical Works. Walton and Maberly, 1854.Google Scholar
Brewka, Gerhard and Thomas, Eiter. Preferred Answer Sets for Extended Logic Programs. Artificial Intelligence, 109:297–356, 1998.Google Scholar
Brewka, Gerhard, Ilkka, Niemela, and Miroslaw, Truszczynski. Answer Set Optimization. In Proceedings of IJCAI-03, pages 867–872. Morgan Kaufmann, 2003.Google Scholar
Brewka, Gerhard, Ilkka, Niemela, and Miroslaw, Truszczynski. Preferences and Nonmonotonic Reasoning. AI Magazine, 29(4):69–78, 2008.CrossRefGoogle Scholar
Calimeri, Francesco, Tina, Dell'Armi, Thomas, Eiter, Wolfgang, Faber, Georg, Gottlob, Giovanbattista, Ianni, Giuseppe, Ielpa, Christoph, Koch, Nicola, Leone, Simona, Perri, Gerard, Pfeifer, and Axel, Polleres. The DLV System. In Sergio, Flesca and Giovanbattista, Ianni, editors, Proceedings of the 8th European Conference on Artificial Intelligence (JELIA 2002), Sep 2002.Google Scholar
Chen, Weidong, Terrance, Swift, and David S., Warren. Efficient Top-Down Computation of Queries under the Well-Founded Semantics. Journal of Logic Programming, 24 (3):161–201, 1995.CrossRefGoogle Scholar
Chintabathina, Sandeep, Michael, Gelfond, and Richard, Watson. Modeling Hybrid Domains Using Process Description Language. In Answer Set Programming: Advances in Theory and Implementation, volume 142 of CEUR Workshop Proceedings, pages 303–317. CEUR-WS.org, 2005.Google Scholar
Clark, Keith. Negation as Failure. In H., Gallaire and J., Minker, editors, Logic and Data Bases, pages 293–322. Plenum Press, 1978.Google Scholar
Clocksin, William F. and Christopher S., Mellish. Programming in Prolog: Using the ISO Standard (5th ed.). Springer, 2003.CrossRefGoogle Scholar
Cohen, Jacques. A View of the Origins and Development of Prolog. Communications of the ACM, 31(1):26–36, Jan 1988.CrossRefGoogle Scholar
Colmerauer, Alain and Philippe, Roussel. The Birth of Prolog. In Thomas J., Bergin Jr., and Richard G., Gibson Jr., editors, History of Programming Languages II, pages 331–367. ACM, 1996.Google Scholar
Constable, Robert L., Stuart F., Allen, Mark, Bromley, W. Rance, Cleaveland, J. F., Cremer, R. W., Harper, Douglas J., Howe, Todd B., Knoblock, N. P., Mendler, Prakash, Panangaden, Scott F., Smith, and James T., Sasaki. ImplementingMathematics with the Nuprl Proof Development System, PRL Project, Cornell University, 1986.Google Scholar
Covington, Michael A., Donald, Nute, and Andre, Vellino. Prolog Programming in Depth. Prentice-Hall, 1997.Google Scholar
Dantsin, Evgeny, Thomas, Eiter, Georg, Gottlob, and Andrei, Voronkov. Complexity and Expressive Power of Logic Programming. ACM Computing Surveys, 33(3):374–425, Sep 2001.CrossRefGoogle Scholar
Davis, Martin George Logemann, and Donald W., Loveland. A Machine Program for Theorem-Proving. Communications of the ACM, 5(7):394–397, 1962.CrossRefGoogle Scholar
Davis, Martin and Hilary, Putnam. A Computing Procedure for Quantification Theory. Communications of the ACM, 7:201–215, 1960.Google Scholar
de Kleer, Johan, Alan K., Mackworth, and Raymond, Reiter. Characterizing Diagnoses and Systems. Artificial Intelligence, 56(2-3):197–222, 1992.CrossRefGoogle Scholar
Delgrande, James P., Torsten, Schaub, and Hans, Tompits. A Framework for Compiling Preferences in Logic Programs. Journal of Theory and Practice of Logic Programming (TPLP), 3(2):129–187, 2003.Google Scholar
Delgrande, James P., Torsten, Schaub, Hans, Tompits, and Kewen, Wang. A Classification and Survey of Preference Handling Approaches in Nonmonotonic Reasoning. Computational Intelligence, 20:308–334, 2004.CrossRefGoogle Scholar
Dijkstra, Edsger W. Under the Spell of Leibniz's Dream. Information Processing Letters, 77(2-4):53–61, 2001.CrossRef
Dimopoulos, Yannis, Jana, Koehler, and Bernhard, Nebel. Encoding Planning Problems in Nonmonotonic Logic Programs. In Proceedings of the 4th European Conference on Planning, volume 1348 of LectureNotes in Artificial Intelligence (LNCS),pages169–181, 1997.Google Scholar
Dix, Jurgen. Classifying Semantics of Logic Programs (Extended Abstract). In LPNMR, pages 166–180, 1991.Google Scholar
Doherty, Patrick, Joakim, Gustafsson, Lars, Karlsson, and Jonas, Kvarnström. TAL: Temporal Action Logics Language Specification and Tutorial. Electronic Transactions on Artificial Intelligence, 2:273–306, 1998.Google Scholar
Doherty, Patrick, John, McCarthy, and Mary-Anne, Williams, editors. Logic Programs with Consistency-Restoring Rules, AAAI 2003 Spring Symposium Series, Mar 2003.
Domingos, Pedro and Daniel, Lowd. Markov Logic: An Interface Layer for Artificial Intelligence. Morgan & Claypool, 2009.Google Scholar
Erdem, Esra and Vladimir, Lifschitz. Tight Logic Programs. Theory and Practice of Logic Programming, 3:499–518, 2003.CrossRefGoogle Scholar
Fages, Francois. Consistency of Clark's Completion and Existence of Stable Models. Journal of Methods of Logic in Computer Science, 1(1):51–60, 1994.Google Scholar
Febbraro, Onofrio, Kristian, Reale, and Francesco, Ricca. ASPIDE: Integrated Develop-mentEnvironmentforAnswerSet Programming. In Logic Programming and Non-monotonic Reasoning – 11th International Conference, LPNMR 2011, Vancouver, Canada, May 16-19, 2011. Proceedings, volume 6645, pages 317–330, 2011.Google Scholar
Ferraris, Paolo, Joohyung, Lee, and Vladimir, Lifschitz. A New Perspective on Stable Models. In IJCAI, pages 372–379, 2007.Google Scholar
Ferraris, Paolo, Joohyung, Lee, and Vladimir, Lifschitz. Stable Models and Circumscription. Artificial Intelligence, 175:236–263, 2011.CrossRefGoogle Scholar
Fikes, Richard and Nils J., Nilsson. STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Technical Note 43R, AI Center, SRI International, 1971.Google Scholar
Fitting, Melvin. A Kripke-Kleene Semantics for Logic Programs. Journal of Logic Programming, 2(4):295–312, 1985.CrossRefGoogle Scholar
Fitzpatrick, Richard. Euclids Elements. University of Texas at Austin, Institute for Fusion Studies Department of Physics, 2007.Google Scholar
Frege, Gottlob. Begriffsschrift, a Formal Language, Modeled upon that of Arithmetic, for Pure Thought. In J. van, Heijenoort, editor, From Frege to Godel: A Source Book in Mathematical Logic, pages 1–82. Harward University Press, 2002 (1879).Google Scholar
Gebser, Martin, Roland, Kaminski, Benjamin, Kaufmann, Max, Ostrowski, Torsten, Schaub, and Sven, Thiele. Engineering an Incremental ASP Solver. In ICLP, pages 190–205, 2008.Google Scholar
Gebser, Martin, Roland, Kaminski, Benjamin, Kaufmann, and Torsten, Schaub. Answer Set Solving in Practice, volume 6 of Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool, 2012.Google Scholar
Gebser, Martin, Ronald, Kaminski, Benjamin, Kaufmann, Max, Ostrowski, Torsten, Schaub, and Sven, Thiele. A User's Guide to gringo, clasp, clingo, and iclingo. http://potassco.sourceforge.
Gebser, Martin, Benjamin, Kaufmann, Ramon, Otero, Javier, Romero, Torsten, Schaub, and P., Wanko. Domain-Specific Heuristics in Answer Set Programming. In Proceedings of the Twenty-Seventh Conference on Artificial Intelligence, AAAI'2013. AAAI Press, 2013.Google Scholar
Gebser, Martin, Torsten, Schaub, and Sven, Thiele. Grin Go: A New Grounder for Answer Set Programming. In LPNMR, pages 266–271, 2007.Google Scholar
Geffner, Hector and Blai, Bonet. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool, Jun 2013, Vol. 7, No. 2, pages 1–141.Google Scholar
Gelder, Allen Van, Kenneth A., Ross, and John S., Schlipf. The Well-Founded Semantics for General Logic Programs. Journal of ACM, 38(3):620–650, 1991.Google Scholar
Gelfond, Michael. On Stratified Autoepistemic Theories. In Proceedings of Sixth National Conference on Artificial Intelligence, pages 207–212, 1987.Google Scholar
Gelfond, Michael. Representing Knowledge in A-Prolog. In A. C., Kakas and F., Sadri, editors, Computational Logic: Logic Programming and Beyond, pages 413–451. Springer, 2002.Google Scholar
Gelfond, Michael and Daniela, Inclezan. Yet Another Modular Action Language. In Proceedings of the Second International Workshop on Software Engineering for Answer Set Programming 10, pages 64–78, 2009.Google Scholar
Gelfond, Michael and Daniela, Inclezan. Some Properties of System Descriptions of ALd. Journal of Applied Non-Classical Logics, 23(1/2):105–120, 2013.CrossRefGoogle Scholar
Gelfond, Michael and Vladimir, Lifschitz. The Stable Model Semantics for Logic Programming. In Proceedings of ICLP-88, pages 1070–1080, 1988.Google Scholar
Gelfond, Michael and Vladimir, Lifschitz. Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing, 9(3/4):365–386, 1991.CrossRefGoogle Scholar
Gelfond, Michael and Vladimir, Lifschitz. Representing Action and Change by Logic Programs. Journal of Logic Programming, 17(2/3&4):301–321, 1993.CrossRefGoogle Scholar
Gelfond, Michael and Vladimir, Lifschitz. Action Languages. Electronic Transactions on AI, 3, 1998.Google Scholar
Gelfond, Michael and Vladimir, Lifschitz. The Common Core of Action Languages Band C. In Working Notes of the International Workshop on Nonmonotonic Reasoning (NMR). 2012.Google Scholar
Gelfond, Michael, Vladimir, Lifschitz, and Arkady, Rabinov. What Are the Limitations of the Situation Calculus? In R. S., Boyer, editor, Automated Reasoning: Essays in Honor of Woody Bledsoe, pages 167–180. Springer, 1991.Google Scholar
Gelfond, Michael and Nelson, Rushton. Causal and Probabilistic Reasoning in P-log. In R., Dechter, H., Geffner, and J., Halpern, editors, A Tribute to Judea Pearl, pages 337–359. College Publications, 2010.Google Scholar
Gelfond, Michael, Nelson, Rushton, and Weijun, Zhu. Combining Logical and Probabilistic Reasoning. In AAAI Spring 2006 Symposium, 2006.Google Scholar
Gelfond, Michael and Tran Cao, Son. Reasoning with Prioritized Defaults. In Third International Workshop, LPKR'97, volume 1471 of Lecture Notes in Artificial Intelligence (LNCS), pages 164–224, Oct 1997.Google Scholar
Ghallab, Malik, Dana, Nau, and Paolo, Traverso. Automated Planning: Theory and Practice. Morgan Kaufmann, 2004.Google Scholar
Ginsberg, Matthew L. and David E., Smith. Reasoning about Action I: A Possible Worlds Approach. Artificial Intelligence, 35(2):165–195, 1988.CrossRefGoogle Scholar
Giunchiglia, Enrico, Joohyung, Lee, Vladimir, Lifschitz, Norman, McCain, and Hudson, Turner. Nonmonotonic Causal Theories. Artificial Intelligence, 153:105–140, 2004.CrossRefGoogle Scholar
Giunchiglia, Enrico, Yulia, Lierler, and Marco, Maratea. Answer Set Programming Based on Propositional Satisfiability. Journal of Automated Reasoning, 36:345–377, 2006.Google Scholar
Giunchiglia, Enrico and Vladimir, Lifschitz. An Action Language Based on Causal Explanation: Preliminary Report. In AAAI/IAAI, pages 623–630, 1998.Google Scholar
Godel, Kurt. On Formally Undecidable Propositions of Principia Mathematica and Related Systems. Dover Publications (1992), 1931.Google Scholar
Green, Cordell. Application of Theorem Proving to Problem Solving. In Proceedings of the 1st International Joint Conference on Artificial Intelligence, IJCAI'69, pages 219–239, San Francisco. Morgan Kaufmann Publishers, 1969.Google Scholar
Green, Cordell. The Application of Theorem-Proving to Question-Answering Systems. Unpublished Doctoral Dissertation, Stanford University, 1969.Google Scholar
Hamscher, Walter, Luca, Console, and Johan de, Kleer, editors. Readings in Model-Based Diagnosis. Morgan Kaufmann Publishers, 1992.
Hayes, Patrick J. and John, McCarthy. Some Philosophical Problems from the Standpoint of Artificial Intelligence. In B., Meltzer and D., Michie, editors, Machine Intelligence 4, pages 463–502. Edinburgh University Press, 1969.Google Scholar
Hilbert, David. Foundations of Geometry. Open Court, 1980 (1899).Google Scholar
Hilborn, Ray and Marc, Mangel. The Ecological Detective. Princeton University Press, 1997.Google Scholar
Hoos, Holger H. and Thomas, Stutzle. Local Search Algorithms for SAT: An Empirical Evaluation. Journal of Automated Reasoning, 24:421–481, 2000.CrossRefGoogle Scholar
Inclezan, Daniela. An Application of ASP to the Field of Second Language Acquisition. In LPNMR, 2013.Google Scholar
Janhunen, Tomi, Ilkka, Niemela, and Mark, Sevalnev. Computing Stable Models via Reductions to Difference Logic. In LPNMR, pages 142–154, 2009.Google Scholar
Jaynes, Edwin T.Probability Theory: The Logic of Science. Cambridge University Press, 2003.CrossRefGoogle Scholar
Kakas, Antonis C. and Rob, Miller. Reasoning about Actions, Narratives and Ramification. Electronic Transactions on Artificial Intelligence, 1:39–72, 1997.Google Scholar
Kakas, Antonis C., Rob, Miller, and Francesca, Toni. E-RES: Reasoning about Actions, Events and Observations. In LPNMR, pages 254–266, 2001.Google Scholar
Kautz, Henry A. and Bart, Selman. Planning as Satisfiability. In ECAI, pages 359–363, 1992.Google Scholar
Koller, Daphne and Nir, Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.Google Scholar
Kowalski, Robert A.The Early Years of Logic Programming. Communications of the ACM, 31(1):38–43, January 1988.CrossRefGoogle Scholar
Kowalski, Robert A.Logic for Problem Solving. North-Holland, 1979.Google Scholar
Kowalski, Robert. Database Updates in the Event Calculus. Journal of Logic Programming, 12(1):121–146, 1992.CrossRefGoogle Scholar
Kowalski, Robert. Using Meta-Logic to Reconcile Reactive with Rational Agents. In Meta-Logics and Logic Programming, pages 227–242. MIT Press, 1995.Google Scholar
Kowalski, Robert and Donald, Kuehnm. Linear Resolution with Selection Function. Artificial Intelligence, 2(3,4):227–260, 1971.CrossRefGoogle Scholar
Kowalski, Robert A. and Marek, Sergot. A Logic-Based Calculus of Events. New Generation Computing, 4(4):319–340, 1986.CrossRefGoogle Scholar
Leibniz, Gottfried. Leibniz Selection. Charles Scribner's Sons, 1951.Google Scholar
Leone, Nicola, Gerald, Pfeifer, Wolfgang, Faber, Thomas, Eiter, George, Gottlob, Stefania, Perri, and Francesco, Scarcello. The DLV System for Knowledge Representation and Reasoning. ACM Transactions on Computational Logic, 7:499–562, 2006.CrossRefGoogle Scholar
Leone, Nicola, Pasquale, Rullo, and Francesco, Scarcello. Disjunctive Stable Models: Unfounded Sets, Fixpoint Semantics and Computation. Information and Computation, 135:69–112, 1997.CrossRefGoogle Scholar
Lierler, Yuliya and Marco, Marateo. CMODELS Web Page. http://www.dlvsystem.com/dlvsystem/index.php/.
Lifschitz, Vladimir. Computing Circumscription. In Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI'85, pages 121–127. Morgan Kaufmann Publishers, 1985.Google Scholar
Lifschitz, Vladimir. Answer Set Programming and Plan Generation. Artificial Intelligence, 138:39–54, 2002.CrossRefGoogle Scholar
Lifschitz, Vladimir, Leora, Morgenstern, and David, Plaisted. Knowledge Representation and Classical Logic. In Frank van, Harmelen, Vladimir, Lifschitz, and Bruce, Porter, editors, Handbook ofKnowledge Representation, pages 3–88. Elsevier, 2008.Google Scholar
Lifschitz, Vladimir and Alexander A., Razborov. Why Are There So Many Loop Formulas?ACM Transactions of Computational Logic, 7(2):261–268, 2006.CrossRefGoogle Scholar
Lin, Fangzhen. Embracing Causality in Specifying the Indirect Effects of Actions. In C., Mellish, editor, Proceedings of IJCAI-95, pages 1985–1993. MorganKaufmann, 1995.Google Scholar
Lin, Fangzhen and Raymond, Reiter. State Constraints Revisited. Journal of Logic and Computation, 4(5):655–678, 1994.CrossRefGoogle Scholar
Lin, Fangzhen and Yuting, Zhao. ASSAT: Computing Answer Sets of a Logic Program by SAT Solvers. Artificial Intelligence, 157(1-2):115–137, 2004.CrossRefGoogle Scholar
Liu, Guohua, Tomi, Janhunen, and Ilkka, Niemela. Answer Set Programming via Mixed Integer Programming. In KR, 2012.Google ScholarPubMed
Lloyd, John W.Foundations of Logic Programming, 2nd edition. Springer Verlag, 1987.CrossRefGoogle Scholar
Manna, Marco, Ermelinda, Oro, Massimo, Ruffolo, Mario, Alviano, and Nicola, Leone. The HiLeX System for Semantic Information Extraction. T. Large-Scale Data- and Knowledge-Centered Systems, 5:91–125, 2012.Google Scholar
Marek, Viktor W.Introduction to Mathematics of Satisfiability. Chapman & Hall/CRC Studies in Informatics Series. CRC Press, 2009.CrossRefGoogle Scholar
Marek, Viktor W., and V. S., Subrahmanian. The Relationship between Logic Program Semantics and Nonmonotonic Reasoning. In ICLP, pages 600–617, 1989.Google Scholar
Marek, Victor W. and Miroslaw, Truszczynski. Nonmonotonic Logics: Context Dependent Reasoning. Springer-Verlag, 1993.CrossRefGoogle Scholar
Marek, Victor W. and Miroslaw, Truszczynski. Stable Models and an Alternative Logic Programming Paradigm. In K. R., Apt et al., editors, The Logic Programming Paradigm: A 25-Year Perspective, pages 375–398. Springer-Verlag, 1999.Google Scholar
McCarthy, John. Programs with Common Sense. In M., Minsky, editor, Semantic Information Processing, pages 403–418. MIT Press, 1959.Google Scholar
McCarthy, John. Circumscription – A Form of Non-Monotonic Reasoning. Artificial Intelligence, 13(1-2):27–39, 1980.Google Scholar
McCarthy, John. Situations, Actions, and Causal Laws. Technical Report Memo 2, Stanford Artificial Intelligence Project, Stanford University, 1983.Google Scholar
McCarthy, John. Formalizing Common Sense, Papers by John McCarthy, edited by V., Lifschitz. Ablex, 1990.Google Scholar
McCarthy, John. Elaboration Tolerance. http://www-formal.stanford.edu/jmc/elaboration/, 1998.Google Scholar
McDermott, Drew, and Jon, Doyle. Non-Monotonic Logic I. Artificial Intelligence, 13 (1-2):41–72, 1980.CrossRefGoogle Scholar
McDermott, Drew, Malik, Ghallab, A., Howe, C., Knoblock, A., Ram, M., Veloso, D., Weld, and D., Wilkins. PDDL - The Planning Domain Definition Language. Tech. Rep. CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control, 1998.Google Scholar
Mellarkod, Veena S., Michael, Gelfond, and Yuanlin, Zhang. Integrating Answer Set Programming and Constraint Logic Programming. Annals of Math and Artificial Intelligence, 53(1-4):251–287, 2008.CrossRefGoogle Scholar
Miglioli, Pierangelo, Ugo, Moscato, and Mario, Ornaghi. PAP: A Logic Programming System Based on a Constructive Logic. In Foundations of Logic and Functional Programming, volume 306 of Lecture Notes in Computer Science, pages 141–156, 1988.Google Scholar
Minker, Jack. On Indefinite Data Bases and the Closed World Assumption. In Proceedings of CADE-82, pages 292–308, 1982.Google Scholar
Minker, Jack and Dietmar, Seipal. Disjunctive Logic Programming: A Survey and Assesment. In Computational Logic: Logic Programming and Beyond, 2002, pages 472–511. Lecture Notes in Artificial Intelligence, Springer.Google Scholar
Mints, Gregory and Enn, Tyugu. The Programming System PRIZ. Journal of Symbolic Computation, 5(3):359–375, June 1988.CrossRefGoogle Scholar
Moore, Robert C.Semantical Considerations on Nonmonotonic Logic. In Proceedings of the 8th International Joint Conference on Artificial Intelligence, pages 272–279. Morgan Kaufmann, Aug 1983.Google Scholar
Mueller, Erik T.Commonsense Reasoning. Morgan Kaufmann, 2006.Google Scholar
Nerode, Anil and Richard A., Shore. Logic for Applications. Graduate Texts in Computer Science. Springer-Verlag GmbH, 1997.CrossRefGoogle Scholar
Niemela, Ilkka. Logic Programs with Stable Model Semantics as a Constraint Programming Paradigm. Annals of Mathematics and Artificial Intelligence, 25(3-4): 241–273, 1999.CrossRefGoogle Scholar
Niemela, Ilkka and Patrik, Simons. Smodels – An Implementation of the Stable Model and Well-Founded Semantics for Normal Logic Programs. In Proceedings of the 4th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR'97), volume 1265 of Lecture Notes in Artificial Intelligence (LNCS), pages 420–429, 1997.Google Scholar
Niemela, Ilkka, Patrik, Simons, and Timo, Soininen. Extending and Implementing the Stable Model Semantics. Artificial Intelligence, 138(1-2):181–234, Jun 2002.Google Scholar
Nillson, Nils. Shakey the Robot. Technical Report 323, AI Center, SRI International, 1984.Google Scholar
Nilsson, Nils J.Probabilistic Logic. Artificial Intelligence, 28(1):71–87, 1986.CrossRefGoogle Scholar
Nogueira, Monica, Marcello, Balduccini, Michael, Gelfond, Richard, Watson, and Matthew, Barry. An A-Prolog Decision Support System for the Space Shuttle. In Alessandro, Provetti and Tran Cao, Son, editors, Answer Set Programming: Towards Efficient and Scalable Knowledge Representation and Reasoning, AAAI 2001 Spring Symposium Series, Mar 2001.Google Scholar
Ostrowski, Max and Torsten, Schaub. ASP Modulo CSP: The Clingcon System. TPLP, 12(4-5):485–503, 2012.Google Scholar
Pearce, David. A New Logical Characterisation of Stable Models and Answer Sets. In In Proceedings of NMELP 96, LNCS 1216, pages 57–70. Springer, 1997.Google Scholar
Pearce, David. Equilibrium Logic. Annals of Math and Artificial Intelligence, 47(1-2): 3–41, 2006.CrossRefGoogle Scholar
Pearl, Judea. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley Longman Publishing, 1984.Google Scholar
Pearl, Judea. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.Google Scholar
Pearl, Judea. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.Google Scholar
Pednault, Edwin P. D.ADL and the State-Transition Model of Action. Journal of Logic and Computation, 4(5):467–512, 1994.CrossRefGoogle Scholar
Pereira, Luís Moniz and Carroline Kencana, Ramli. Modelling Decision Making with Probabilistic Causation. Intelligent Decision Technologies, 4(2):133–148, 2010.CrossRefGoogle Scholar
Pinto, Javier. Occurrences and Narratives as Constraints in the Branching Structure of the Situation Calculus. Journal of Logic and Computation, 8(6):777–808, 1998.CrossRefGoogle Scholar
Pinto, Javier and Raymond, Reiter. Reasoning about Time in the Situation Calculus. Annals of Mathematics and Artificial Intelligence, 14(2-4):251–268, 1995.CrossRefGoogle Scholar
Poole, David. Representing Diagnosis Knowledge. Annals of Mathematics and Artificial Intelligence, 11:33–50, 1994.CrossRefGoogle Scholar
Poole, David. The Independent Choice Logic and Beyond. In L. De, Raedt, editor, Probabilistic Inductive Logic Programming, pages 222–243. Springer, 2008.Google Scholar
Poole, David and Alan K., Mackworth. Artificial Intelligence – Foundations of Computational Agents. Cambridge University Press, 2010.CrossRefGoogle Scholar
Poole, David, Alan, Mackworth, and Randy, Goebel. Computational Inteligence: A Logical Approach. Oxford University Press, 1998.Google Scholar
Przymusinska, Halina and Teodor, Przymusinski. Weakly Stratified Logic Programs. Fundamenta Informaticae, XIII:51–65, 1990.Google Scholar
Przymusinski, Teodor C.On the Declarative and Procedural Semantics of Logic Programs. Journal of Automated Reasoning, 5:167–205, 1995.Google Scholar
Przymusinski, Teodor. On the Declarative Semantics of Deductive Databases and Logic Programs. In Jack, Minker, editor, Foundations of Deductive Databases and Logic Programming, pages 193–216. Morgan Kaufmann, 1988.Google Scholar
Rao, Georgeff. Modeling Rational Agents within a BDI-Architecture. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning, pages 473–484. Morgan Kaufmann Publishers, 1991.Google Scholar
Reiter, Raymond. On Closed World Data Bases. In Logic and Data Bases, pages 119–140. Plenum Press, 1978.Google Scholar
Reiter, Raymond. A Logic for Default Reasoning. Artificial Intelligence, 13(1-2):81–132, 1980.Google Scholar
Reiter, Raymond. A Theory of Diagnosis from First Principles. Artificial Intelligence, 32:57–95, 1987.CrossRefGoogle Scholar
Reiter, Raymond. Knowledge in Action – Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press, Sep 2001.Google Scholar
Ricca, Francesco, Giovanni, Grasso, Mario, Alviano, Marco, Manna, Vincenzino, Lio, Salvatore, Iiritano, and Nicola, Leone. Team-Building with Answer Set Programming in the Gioia-Tauro Seaport. TPLP, 12(3):361–381, 2012.Google Scholar
Robinson, John Alan. A Machine-Oriented Logic Based on the Resolution Principle. Journal of the ACM, 12(1):23–41, January 1965.CrossRefGoogle Scholar
Ross, Kenneth. A Procedural Semantics for Well Founded Negation in Logic Programs. Journal of Logic Programming, 13(1):1–22, 1992.CrossRefGoogle Scholar
Sakama, Chiaki and Katsumi, Inoue. Prioritized Logic Programming and Its Application to Commonsense Reasoning. Artificial Intelligence, 123:185–222, 2000.CrossRefGoogle Scholar
Sandewall, Erik. Features and Fluents (Vol. 1): The Representation of Knowledge about Dynamical Systems. Oxford University Press, 1994.Google Scholar
Sato, Taisuke and Yoshitaka, Kameya. PRISM: A Language for Symbolic-Statistical Modeling. In IJCAI, pages 1330–1339, 1997.Google Scholar
Shanahan, Murray. A Circumscriptive Calculus of Events. Artificial Intelligence, 77: 249–284, 1995.CrossRefGoogle Scholar
Shanahan, Murray. Solving the Frame Problem: A Mathematical Investigation of the Commonsense Law of Inertia. MIT Press, 1997.Google Scholar
Simons, Patrik. Extending and Implementing the Stable Model Semantics. Dissertation, Helsinki University of Technology, 2000.Google Scholar
Soininen, Timo and Ilkka, Niemela. Developing a Declarative Rule Language for Applications in Product Configuration. In Proceedings of the First International Workshop on Practical Aspects of Declarative Languages, May 1999.Google Scholar
Son, Tran Cao, Chitta, Baral, Nam, Tran, and Sheila A., McIlraith. Domain-Dependent Knowledge in Answer Set Planning. ACM Transactions on Computational Logic, 7(4):613–657, 2006.CrossRefGoogle Scholar
Sowa, John F.Knowledge Representation: Logical, Philosophical and Computational Foundations. Brooks/Cole Publishing, 2000.Google Scholar
Sterling, Leon and Ehud, Shapiro. The Art of Prolog (2nd ed.): Advanced Programming Techniques. MIT Press, 1994.Google Scholar
Subrahmanian, V. S. and Carlo, Zaniolo. Relating Stable Models and AI Planning Domains. In Proceedings of ICLP-95, pages 233–247. 1995.Google Scholar
Syrjanen, Tommi. Implementation of Logical Grounding for Logic Programs with Stable Model Semantics. Technical Report 18, Digital Systems Laboratory, Helsinki University of Technology, 1998.Google Scholar
Tarski, Alfred. Introduction to Logic and the Methodology of Deductive Sciences. Dover Publication, 1995 (1941).Google Scholar
Thielscher, Michael. A Theory of Dynamic Diagnosis. LinkopingElectronic Articles in Computer and Information Science, 2(11), 1997.Google Scholar
Thielscher, Michael. Action Programming Languages. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2008.Google Scholar
Touretzky, David S.The Mathematics of Inheritance Systems. Research Notes in Artificial Intelligence. Morgan Kaufmann, 1986.Google Scholar
Tran, Vien, Khoi, Nguyen, Tran Cao, Son, Enrico, Pontelli. A Conformant Planner Based on Approximation: CpA(H). ACM Transaction on Intelligent Systems and Technology, 4(2), 2013.Google Scholar
Taylor, Bonnie and Michael, Gelfond. Representing Null Values in Logic Programming. In Anil, Nerode and Yuri, Matiyasevich, editors, LFCS, volume 813 of Lecture Notes in Computer Science. Springer, 1994.Google Scholar
Truszczynski, Miroslaw, Victor W., Marek, and Raphael A., Finkel. Generating Cellular Puzzles with Logic Programs. In IC-AI, pages 403–407, 2006.Google Scholar
Tu, Phan Huy, Tran Cao, Son, Michael, Gelfond, and A. Ricardo, Morales. Approximation of Action Theories and Its Application to Conformant Planning. Artificial Intelligence, 175(1):79–119, 2011.CrossRefGoogle Scholar
Turner, Hudson. Representing Actions in Logic Programs and Default Theories: A Situation Calculus Approach. Journal of Logic Programming, 31(1-3):245–298, Jun 1997.CrossRefGoogle Scholar
Ullman, Jeffrey D.Principles of Database and Knowledge-Base Systems. Computer Science Press, 1988.Google Scholar
Vennekens, Joost, Marc, Denecker, and Maurice, Bruynooghe. CP-Logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming. TPLP, 9 (3):245–308, 2009.Google Scholar
Whitehead, Alfred N. and Bertrand, Russell. Principia Mathematica, 3 vols. Cambridge University Press, 1910, 1912, 1913.Google Scholar
Wooldridge, Michael. Reasoning about Rational Agents. MIT Press, 2000.Google Scholar
Zaniolo, Carlo. Design and Implementaion of a Logic Based Language for Data Intensive Applications. In R., Kowalski and K., Bowen, editors, Logic Programming: Proceedings of the Fifth International Conference and Symposium, pages 1666–1687, 1988.Google Scholar
Zhu, Weijun. A New Algorithm for P-log Inference Engine. In Workshopon Answer Set Programming and Other Computing Paradigms (ASPOCP) 2010, 2010.Google Scholar
Zhu, Weijun. PLOG: Its Algorithms and Applications. PhD thesis, Texas Tech University, College of Engineering, Department of Computer Science, 2012.Google Scholar

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  • Bibliography
  • Michael Gelfond, Texas Tech University, Yulia Kahl
  • Book: Knowledge Representation, Reasoning, and the Design of Intelligent Agents
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139342124.018
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  • Bibliography
  • Michael Gelfond, Texas Tech University, Yulia Kahl
  • Book: Knowledge Representation, Reasoning, and the Design of Intelligent Agents
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139342124.018
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  • Bibliography
  • Michael Gelfond, Texas Tech University, Yulia Kahl
  • Book: Knowledge Representation, Reasoning, and the Design of Intelligent Agents
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139342124.018
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