Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-19T08:39:07.981Z Has data issue: false hasContentIssue false
  • English
  • Français

An ASP Methodology for Understanding Narratives about Stereotypical Activities

Published online by Cambridge University Press:  10 August 2018

DANIELA INCLEZAN ,
QINGLIN ZHANG ,
MARCELLO BALDUCCINI  and
ANKUSH ISRANEY
DANIELA INCLEZAN
Affiliation:
Miami University, Oxford OH 45056, USA (e-mail: inclezd@miamioh.edu, zhangq7@miamioh.edu)
QINGLIN ZHANG
Affiliation:
Miami University, Oxford OH 45056, USA (e-mail: inclezd@miamioh.edu, zhangq7@miamioh.edu)
MARCELLO BALDUCCINI
Affiliation:
Saint Joseph's University, Philadelphia PA 19131, USA (e-mail: marcello.balduccini@gmail.com)
ANKUSH ISRANEY
Affiliation:
Drexel University, Philadelphia PA 19104, USA (e-mail: avi26@drexel.edu)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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.

Keywords

natural language understandingstereotypical activitiesintentions
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 18 , Special Issue 3-4: 34th International Conference on Logic Programming , July 2018 , pp. 535 - 552
Copyright
Copyright © Cambridge University Press 2018 

References

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, 18–30.Google Scholar
Balduccini, M., Baral, C., and Lierler, Y. 2007. Handbook of Knowledge Representation. Foundations of Artificial Intelligence. Elsevier, Chapter 20. Knowledge Representation and Question Answering.Google Scholar
Balduccini, M. and Gelfond, M. 2003. Logic Programs with Consistency-Restoring Rules. In Proceedings of Commonsense-03. AAAI Press, 9–18.Google Scholar
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, 1–6.Google Scholar
Baral, C. and Gelfond, M. 2005. Reasoning about Intended Actions. In Proceedings of AAAI-05. AAAI Press, 689–694.Google Scholar
Blount, J. 2013. An Architecture for Intentional Agents. Ph.D. thesis, Texas Tech University, Lubbock, TX, USA.Google Scholar
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.Google Scholar
Bordini, R. H., Hübner, J. F., and Wooldridge, M. 2007. Programming Multi-Agent Systems in AgentSpeak Using Jason. John Wiley & Sons, Ltd.Google Scholar
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.Google Scholar
Gabaldon, A. 2009. Activity recognition with intended actions. In Proceedings of IJCAI 2009, Boutilier, C., Ed. 1696–1701.Google Scholar
Gelfond, M. and Kahl, Y. 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents. Cambridge University Press.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing 9, 3/4, 365–386.Google Scholar
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.Google Scholar
Kamp, H. and Reyle, U. 1993. From discourse to logic. Vol. 1,2. Kluwer.Google Scholar
Lierler, Y., Inclezan, D., and Gelfond, M. 2017. Action languages and question answering. In IWCS 2017 - 12th International Conference on Computational Semantics - Short papers.Google Scholar
Michael, L. 2013. Story understanding... calculemus! 11th International Symposium on Logical Formalizations of Commonsense Reasoning (Commonsense'13). 64–70.Google Scholar
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.Google Scholar
Mueller, E. T. 2004. Understanding script-based stories using commonsense reasoning. Cognitive Systems Research 5, 4, 307–340.Google Scholar
Mueller, E. T. 2007. Modelling space and time in narratives about restaurants. Literary and Linguistic Computing 22, 1, 67–84.Google Scholar
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.Google Scholar
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.Google Scholar
Palmer, M., Gildea, D., and Kingsbury, P. 2005. The Proposition Bank: An annotated corpus of semantic roles. Computational Linguistics 31, 1 (Mar.), 71–106.Google Scholar
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.Google Scholar
Regneri, M., Koller, A., and Pinkal, M. 2010. Learning script knowledge with web experiments. In Proceedings of ACL '10. 979–988.Google Scholar
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.Google Scholar
Schank, R. C. and Abelson, R. P. 1977. Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Lawrence Erlbaum.Google Scholar
Shanahan, M. 1997. Solving the Frame Problem. MIT Press.Google Scholar
Todorova, Y. and Gelfond, M. 2012. Toward Question Answering in Travel Domains. In Correct Reasoning. 311–326.Google Scholar
Wooldridge, M. 2009. An Introduction to MultiAgent Systems, 2nd ed. Wiley Publishing.Google Scholar
Zhang, Q. and Inclezan, D. 2017. An application of ASP theories of intentions to understanding restaurant scenarios. In Proceedings of PAoASP'17.Google Scholar
Supplementary material: PDF

Inclezan et al. supplementary material

Appendix A

Download Inclezan et al. supplementary material(PDF)
PDF 36.5 KB