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Typed answer set programming lambda calculus theories and correctness of inverse lambda algorithms with respect to them

Published online by Cambridge University Press:  05 September 2012

CHITTA BARAL
Affiliation:
School of Computing, Informatics, and Decision Systems EngineeringArizona State University, Tempe, AZ
JURAJ DZIFCAK
Affiliation:
School of Computing, Informatics, and Decision Systems EngineeringArizona State University, Tempe, AZ
MARCOS A. GONZALEZ
Affiliation:
School of Computing, Informatics, and Decision Systems EngineeringArizona State University, Tempe, AZ
AARON GOTTESMAN
Affiliation:
School of Computing, Informatics, and Decision Systems EngineeringArizona State University, Tempe, AZ

Abstract

Our broader goal is to automatically translate English sentences into formulas in appropriate knowledge representation languages as a step towards understanding and thus answering questions with respect to English text. Our focus in this paper is on the language of Answer Set Programming (ASP). Our approach to translate sentences to ASP rules is inspired by Montague's use of lambda calculus formulas as meaning of words and phrases. With ASP as the target language the meaning of words and phrases are ASP-lambda formulas. In an earlier work we illustrated our approach by manually developing a dictionary of words and their ASP-lambda formulas. However such an approach is not scalable. In this paper our focus is on two algorithms that allow one to construct ASP-lambda formulas in an inverse manner. In particular the two algorithms take as input two lambda-calculus expressions G and H and compute a lambda-calculus expression F such that F with input as G, denoted by F@G, is equal to H; and similarly G@F = H. We present correctness and complexity results about these algorithms. To do that we develop the notion of typed ASP-lambda calculus theories and their orders and use it in developing the completeness results.

Type
Regular Papers
Copyright
Copyright © Cambridge University Press 2012

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References

Baral, C. 2003. Knowledge Representation, Reasoning, and Declarative Problem Solving. Cambridge University Press.Google Scholar
Baral, C. and Dzifcak, J. 2012. Solving puzzles described in english by automated translation to answer set programming and learning how to do that translation. In KR (to appear).Google Scholar
Baral, C., Dzifcak, J. and Son, T. C. 2008. Using answer set programming and lambda calculus to characterize natural language sentences with normatives and exceptions. In AAAI'08: Proceedings of the 23rd national conference on Artificial intelligence. 818823.Google Scholar
Baral, C., Gonzalez, M. A. and Gottesman, A. 2012. The inverse lambda calculus algorithm for typed first order logic lambda calculus and its application to translating english to fol. In Logic-based Artificial Intellgence, Erdem, E., Lee, J., Lierler, Y. and Pearce, D., Eds. Lecture Notes in Computer Science. Springer. To be published.Google Scholar
Barbara, H.Partee, A. T. M. and Wall, R. E. 1990. Mathematical Methods in Linguistics. Kluwer Academic Publishers.Google Scholar
Barendregt, H. 1992. Lambda Calculi with Types, Handbook of Logic in Computer Science. Vol. II. Oxford University Press.Google Scholar
Blackburn, P. and Bos, J. 2005. Representation and Inference for Natural Language: A First Course in Computational Semantics. Center for the Study of Language.Google Scholar
Church, A. 1940. A formulation of the simple theory of types. The Journal of Symbolic Logic 5, 2, 5668.Google Scholar
Clark, S. and Curran, J. R. 2007. Wide-coverage efficient statistical parsing with ccg and log-linear models. Computational Linguistics 33.CrossRefGoogle Scholar
Costantini, S. and Paolucci, A. 2010. Towards translating natural language sentences into asp. In Proceedings of the Intl. Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP).Google Scholar
Dowek, G. 1994. Third order matching is decidable. Annals of Pure and Applied Logic 69, 2–3, 135155.CrossRefGoogle Scholar
Dzifcak, J., Scheutz, M., Baral, C. and Schermerhorn, P. 2009. What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution. In Robotics and Automation, 2009. ICRA '09. 41634168.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Logic Programming: Proceedings of the Fifth International Conference and Symposium. 10701080.Google Scholar
Gonzalez, M. A. 2010. An Inverse Lambda Calculus Algorithm For Natural Language Processing. MS Thesis, Arizona State University.Google Scholar
Hindley, J. 1986. Introduction to Combinators and Lambda-Calculus. Cambridge University press.Google Scholar
Hindley, J. 1997. Basic Simple Type Theory. Cambridge University press.Google Scholar
Huet, G. 1973. The undecidability of unication in third order logic. Information and Control 22, 3, 257267.CrossRefGoogle Scholar
Huet, G. 1975. A unication algorithm for typed calculus. Theoretical Computer Science 1, 2757.Google Scholar
Kwiatkowski, T., Zettlemoyer, L., Goldwater, S. and Steedman, M. 2010. Inducing probabilistic CCG grammars from logical form with higher-order unification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). 12231233.Google Scholar
Loader, R. 2003. Higher-order beta-matching is undecidable. Logic Journal of IGPL 11, 1, 5168.Google Scholar
Montague, R. 1974. Formal Philosophy. Selected Papers of Richard Montague. Yale University Press, New Haven.Google Scholar
Steedman, M. 2000. The syntactic process. MIT Press.Google Scholar
Stirling, C. 2009. Decidability of higher-order matching. To appear Logical Methods in Computer Science 5, 3.Google Scholar
Zettlemoyer, L. and Collins, M. 2005. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Proceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence. 658666.Google Scholar
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