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Formalization of psychological knowledge in answer set programming and its application

Published online by Cambridge University Press:  09 July 2010

MARCELLO BALDUCCINI  and
SARA GIROTTO
MARCELLO BALDUCCINI
Affiliation:
Intelligent Systems, KRL, Eastman Kodak Company, Rochester, NY 14650-2102, USA (e-mail: marcello.balduccini@gmail.com)
SARA GIROTTO
Affiliation:
Department of Psychology, Texas Tech University, Lubbock, TX 79409, USA (e-mail: sara.girotto@ttu.edu)
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Abstract

In this paper we explore the use of Answer Set Programming (ASP) to formalize, and reason about, psychological knowledge. In the field of psychology, a considerable amount of knowledge is still expressed using only natural language. This lack of a formalization complicates accurate studies, comparisons, and verification of theories. We believe that ASP, a knowledge representation formalism allowing for concise and simple representation of defaults, uncertainty, and evolving domains, can be used successfully for the formalization of psychological knowledge. To demonstrate the viability of ASP for this task, in this paper we develop an ASP-based formalization of the mechanics of Short-Term Memory. We also show that our approach can have rather immediate practical uses by demonstrating an application of our formalization to the task of predicting a user's interaction with a graphical interface.

Keywords

answer set programmingreasoning about actions and changepsychologyshort-term memory
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 10 , Special Issue 4-6: 26th International Conference on Logic Programming , July 2010 , pp. 725 - 740
Copyright
Copyright © Cambridge University Press 2010

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References

Atkinson, R. C. and Shiffrin, R. M. 1971. The control of short-term memory. Scientific American 225, 82–90.CrossRefGoogle ScholarPubMed
Balduccini, M., Gelfond, M. and Nogueira, M. 2000. A-Prolog as a tool for declarative programming. In Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering (SEKE'2000). 63–72.Google Scholar
Baral, C. and Gelfond, M. 2005. Reasoning about intended actions. In Proceedings of the 20th National Conference on Artificial Intelligence. 689–694.Google Scholar
Card, S. K., Moran, T. P. and Newell, A. 1983. The Psychology of Human-Computer Interaction. L. Erlbaum Associates Inc.Google Scholar
Chintabathina, S., Gelfond, M., and Watson, R. 2005. Modeling hybrid domains using process description language. In Proceedings of ASP '05 – Answer Set Programming: Advances in Theory and Implementation. 303–317.Google Scholar
Cowan, N. 2000. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences 24, 87–185.CrossRefGoogle Scholar
Delgrande, J. P., Grote, T., and Hunter, A. 2009. A general approach to the verification of cryptographic protocols using answer set programming. In 10th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR09). 355–367.CrossRefGoogle Scholar
Gelfond, M. 2002. Representing knowledge in A-Prolog. In Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II, Kakas, A. C. and Sadri, F., Eds. Vol. 2408. Springer, Berlin, 413–451.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1998. Action languages. Electronic Transactions on AI 3, 16.Google Scholar
Kassin, S. 2006. Psychology in Modules. Prentice Hall.Google Scholar
Kieras, D. and Polson, P. G. 1983. A generalized transition network representation for interactive systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (SIGCHI-83). 103–106.CrossRefGoogle Scholar
Kieras, D. and Polson, P. G. 1985. A quantitative model of the learning and performance of text editing knowledge. 16, 4 (April), 207–212.Google Scholar
Lee, J. and Lifschitz, V. 2001. Additive fluents. In Answer Set Programming: Towards Efficient and Scalable Knowledge Representation and Reasoning, Provetti, A. and Son, T. C., Eds. AAAI Spring Symposium Series.Google Scholar
Leite, J. and Ilic, M. 2007. Answer-set programming based dynamic user modeling for recommender systems. In Progress in Artificial Intelligence, 13th Portuguese Conference on Artificial Intelligence. 29–42.Google Scholar
Marek, V. W. and Truszczynski, M. 1999. The Logic Programming Paradigm: A 25-Year Perspective. Springer, Berlin (Chapter Stable models and an alternative logic programming paradigm), 375–398.CrossRefGoogle Scholar
McCarley, J. S., Wickens, C. D., Gob, J. and Horrey, W. J. 2002. A computational model of attention/situation awareness. In Proceedings of the 46th Annual Meeting of the Human Factors and Ergonomics Society.CrossRefGoogle Scholar
Miller, G. A. 1956. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review 63, 81–97.CrossRefGoogle ScholarPubMed
Nevid, J. S. 2007. Psychology: Concepts and Applications, 2nd ed.Houghton Mifflin Company.Google Scholar
Niemela, I. and Simons, P. 2000. Logic-Based Artificial Intelligence. Kluwer (Chapter extending the smodels system with cardinality and weight constraints), 491–521.CrossRefGoogle Scholar
Son, T. C. and Sakama, C. 2009. Negotiation using logic programming with consistency restoring rules. In International Joint Conferences on Artificial Intelligence (IJCAI).Google Scholar
Thielscher, M. 2009. Answer set programming for single-player games in general game playing. In 10th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR09). 327–341.CrossRefGoogle Scholar