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An ASP Methodology for Understanding Narratives about Stereotypical Activities

  • DANIELA INCLEZAN (a1), QINGLIN ZHANG (a1), MARCELLO BALDUCCINI (a2) and ANKUSH ISRANEY (a3)

Abstract

We describe an application of Answer Set Programming to the understanding of narratives about stereotypical activities, demonstrated via question answering. Substantial work in this direction was done by Erik Mueller, who modeled stereotypical activities as scripts. His systems were able to understand a good number of narratives, but could not process texts describing exceptional scenarios. We propose addressing this problem by using a theory of intentions developed by Blount, Gelfond, and Balduccini. We present a methodology in which we substitute scripts by activities (i.e., hierarchical plans associated with goals) and employ the concept of an intentional agent to reason about both normal and exceptional scenarios. We exemplify the application of this methodology by answering questions about a number of restaurant stories. This paper is under consideration for acceptance in TPLP.

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References

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Balduccini, M. 2007. CR-MODELS: An inference engine for CR-Prolog. In Proceedings of LPNMR 2007, Baral, C., Brewka, G., and Schlipf, J. S., Eds. LNCS, vol. 4483. Springer, 1830.
Balduccini, M., Baral, C., and Lierler, Y. 2007. Handbook of Knowledge Representation. Foundations of Artificial Intelligence. Elsevier, Chapter 20. Knowledge Representation and Question Answering.
Balduccini, M. and Gelfond, M. 2003. Logic Programs with Consistency-Restoring Rules. In Proceedings of Commonsense-03. AAAI Press, 918.
Balduccini, M. and Gelfond, M. 2008. The AAA architecture: An overview. In Architectures for Intelligent Theory-Based Agents, Papers from the 2008 AAAI Spring Symposium, 2008. AAAI Press, 16.
Baral, C. and Gelfond, M. 2005. Reasoning about Intended Actions. In Proceedings of AAAI-05. AAAI Press, 689–694.
Blount, J. 2013. An Architecture for Intentional Agents. Ph.D. thesis, Texas Tech University, Lubbock, TX, USA.
Blount, J., Gelfond, M., and Balduccini, M. 2015. A theory of intentions for intelligent agents. In Proceedings of LPNMR 2015, Calimeri, F., Ianni, G., and Truszczynski, M., Eds. LNCS, vol. 9345. Springer, 134–142.
Bordini, R. H., Hübner, J. F., and Wooldridge, M. 2007. Programming Multi-Agent Systems in AgentSpeak Using Jason. John Wiley & Sons, Ltd.
Diakidoy, I.-A., Kakas, A., Michael, L., and Miller, R. 2015. Star: A system of argumentation for story comprehension and beyond. 12th International Symposium on Logical Formalizations of Commonsense Reasoning (Commonsense'15). 64–70.
Gabaldon, A. 2009. Activity recognition with intended actions. In Proceedings of IJCAI 2009, Boutilier, C., Ed. 1696–1701.
Gelfond, M. and Kahl, Y. 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents. Cambridge University Press.
Gelfond, M. and Lifschitz, V. 1991. Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing 9, 3/4, 365386.
Inclezan, D., Zhang, Q., Balduccini, M., and Israney, A. 2017. Understanding restaurant stories using an ASP theory of intentions (Extended abstract). In Technical Communications of the 33rd International Conference on Logic Programming (ICLP-TC 2017). OASIcs.
Kamp, H. and Reyle, U. 1993. From discourse to logic. Vol. 1,2. Kluwer.
Lierler, Y., Inclezan, D., and Gelfond, M. 2017. Action languages and question answering. In IWCS 2017 - 12th International Conference on Computational Semantics - Short papers.
Michael, L. 2013. Story understanding... calculemus! 11th International Symposium on Logical Formalizations of Commonsense Reasoning (Commonsense'13). 64–70.
Mostafazadeh, N., Vanderwende, L., Yih, W.-t., Kohli, P., and Allen, J. 2016. Story cloze evaluator: Vector space representation evaluation by predicting what happens next. In Proceedings of RepEval'16. Association for Computational Linguistics, 24–29.
Mueller, E. T. 2004. Understanding script-based stories using commonsense reasoning. Cognitive Systems Research 5, 4, 307340.
Mueller, E. T. 2007. Modelling space and time in narratives about restaurants. Literary and Linguistic Computing 22, 1, 6784.
Ng, H. T. and Mooney, R. J. 1992. Abductive plan recognition and diagnosis: A comprehensive empirical evaluation. In Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning (KR'92). 499–508.
Nieves, J. C., Guerrero, E., and Lindgren, H. 2013. Reasoning about human activities: an argumentative approach. In Twelfth Scandinavian Conference on Artificial Intelligence, SCAI 2013, Aalborg, Denmark, November 20-22, 2013. 195–204.
Palmer, M., Gildea, D., and Kingsbury, P. 2005. The Proposition Bank: An annotated corpus of semantic roles. Computational Linguistics 31, 1 (Mar.), 71106.
Rao, A. S. and Georgeff, M. P. 1991. Modeling rational agents within a BDI-architecture. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (KR'91). Cambridge, MA, USA, April 22-25, 1991. 473–484.
Regneri, M., Koller, A., and Pinkal, M. 2010. Learning script knowledge with web experiments. In Proceedings of ACL '10. 979–988.
Richardson, M., Burges, C. J. C., and Renshaw, E. 2013. Mctest: A challenge dataset for the open-domain machine comprehension of text. In EMNLP. ACL, 193–203.
Schank, R. C. and Abelson, R. P. 1977. Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Lawrence Erlbaum.
Shanahan, M. 1997. Solving the Frame Problem. MIT Press.
Todorova, Y. and Gelfond, M. 2012. Toward Question Answering in Travel Domains. In Correct Reasoning. 311–326.
Wooldridge, M. 2009. An Introduction to MultiAgent Systems, 2nd ed. Wiley Publishing.
Zhang, Q. and Inclezan, D. 2017. An application of ASP theories of intentions to understanding restaurant scenarios. In Proceedings of PAoASP'17.

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An ASP Methodology for Understanding Narratives about Stereotypical Activities

  • DANIELA INCLEZAN (a1), QINGLIN ZHANG (a1), MARCELLO BALDUCCINI (a2) and ANKUSH ISRANEY (a3)

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