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Merging deductive and inductive reasoning for processing textual descriptions of inter-human conflicts

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Abstract

We report on a novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents where we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive, inductive, and abductive components. The deductive component is based on situation calculus and describes the behavior of agents with complete information. The machine learning-based inductive and abductive components involve the previous experience with the agents, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing customer complaints in the form of textual messages that contain a multiagent conflict. The task is to predict the future actions of an opponent agent to determine the required course of action to resolve a multiagent conflict. This study demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. Suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning.

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References

  • Anshakov, O. M., Finn, V. K., & Skvortsov, D. P. (1989). On axiomatization of many-valued logics associated with formalization of plausible reasoning. Studia Logica, 42(4), 423–447.

    Article  MathSciNet  Google Scholar 

  • Bacchus, F., Halpern, J. Y., & Levesque, H. (1999). Reasoning about noisy sensors and effectors in the situation calculus. Artificial Intelligence, 111(1–2), 171–208.

    Google Scholar 

  • Barber, K. S., & Kim, J. (2001). Belief revision process based on trust: Agents evaluating reputation of information sources. In R. Flacone, M. Singh & Y.-H. Tan (Eds.), Fraud, deception, and trust in agent societies. Springer: New York, USA.

  • Ciravegna, F. (2000). Learning to tag information extraction from text. In ECAI-2000 Workshop on Machine Learning for Information Extraction.

  • Davey, B. A., & Priestley, H. A. (2002). Introduction to lattices and order. Cambridge University Press: Cambridge, UK.

  • De Raedt, L. (1999). A perspective on Inductive Logic Programming. In K. R. Apt, V. W. Marek, M. Truszczynski, & D. Warren (Eds.), The logic programming paradigm. Springer: New York, USA.

  • Fagin, R., & Halpern, J. Y. (1994). Reasoning about knowledge and probability. Journal of the ACM, 41(2), 340–367.

    Article  MATH  MathSciNet  Google Scholar 

  • Fagin, R., Halpern, J. Y., Moses, Y., & Vardi, M. Y. (1995). Reasoning about knowledge. MIT Press: Cambridge, Massachusetts.

    MATH  Google Scholar 

  • Finn, V. K. (1999). On the synthesis of cognitive procedures and the problem of induction. NTI Series 2, N1-2, 8–45.

    Google Scholar 

  • Furukawa, K. (1998). From deduction to induction: Logical perspective. In K. R. Apt, V. W. Marek, M. Truszczynski, & D. S. Warren (Eds.), The logic programming paradigm. Springer: New York, USA.

  • Galitsky, B. (2001) Semi-structured knowledge representation for the automated financial advisor. In L. Monostori et al. (Eds.), Engineering of intelligent systems (pp. 874–879). LNAI 2070: 14th IEA/AIE Conference.

  • Galitsky, B. (2003a). Using mental simulator for emotional rehabilitation of autistic patients. FLAIRS – 03, May 12–14, St. Augustine, Forida.

  • Galitsky, B. (2003b). Natural language question answering system: Technique of semantic headers. Advanced knowledge international. Australia.

  • Galitsky, B. (2005a) (n.d.) last downloaded Jan 10, 2005 http://www.dcs.bbk.ac.uk/~galitsky/ComplaintEngineIntro.html.

  • Galitsky, B. (2005b) (n.d.) last downloaded Jan 10, 2005 http://www.dcs.bbk.ac.uk/~galitsky/CLAIMS/AnalyzedClaims.htm.

  • Galitsky, B. (2005c) (n.d.) last downloaded Jan 10, 2005 http://www.dcs.bbk.ac.uk/~galitsky/CLAIMS/ComplaintManagement.html.

  • Galitsky, B., & Mirkin, B. (2003). Building interactive forms to input the scenarios of multiagent conflicts. In J. M. Rossiter, & T. P. Martin (Eds.), Proceedings of the 2003 UK Workshop on Computational Intelligence (pp. 123–130). Bristol, UK: University of Bristol, UKCI-2003.

  • Galitsky, B., & Pampapathi, R. (2003). Deductive and inductive reasoning for processing the claims of unsatisfied customers. In 16th IEA/AIE Conference, LNAI 2718 (pp. 21–30). UK: Loughborough University.

    Google Scholar 

  • Galitsky, B., & Tumarkina, I. (2004a). Justification of customer complaints using mental actions and emotional states. FLAIRS – 04, May 16–18, Miami Florida.

  • Galitsky, B., & Tumarkina, I. (2004b). Justification of customer complaints using mental actions and emotional states. In Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference. Menlo Park, California: AAAI Press.

    Google Scholar 

  • Ganter, B., & Wille, R. (1999). Formal concept analysis. Mathematical Foundations, Springer: New York, USA.

  • Grosskreutz, H., & Lakemeyer, G. (2003). Probabilistic complex actions in GOLOG. Fundamenta Informaticae, 57(2–4), 67–192.

    MathSciNet  Google Scholar 

  • Kuznetsov, S. O. (2004). Machine learning and formal concept analysis. Intern Conf on Formal Concept Analysis (pp. 287–312). Australia.

  • Lakemeyer, G. (1999). On sensing in GOLOG. In H. J. Levesque & F. Pirri (Eds.), Logical foundations for cognitive agents. Springer: New York, USA.

  • Lavrac, N., & Dzeroski, S. (1994). Inductive logic programming: Techniques and applications. New York: Ellis Horwood.

    Google Scholar 

  • Leake, D. B., Kinley, A., & Wilson, D. (1997). Learning to integrate multiple knowledge sources for case-based reasoning. In Fourteenth International Joint Conference on Artificial Intelligence. Nagoya, Japan: Morgan Kaufmann.

    Google Scholar 

  • Levesque, H. L. & Pagnucco, M. (2000). Legolog: Inexpensive experiments in cognitive robotics. In Proceedings of the Second International Cognitive Robotics Workshop, Berlin, Germany, August 21–22.

  • Levesque, H. J., Reiter, R., Lesperance, Y., Lin, F., & Scherl, R. B. (1997). GOLOG: A logic programming language for dynamic domains. Journal of Logic Programming, 31, 59–84.

    Article  MATH  MathSciNet  Google Scholar 

  • Liu, T. H., Chuter, C. J., & Barber, K. S. (1997). Virtual environment simulation for visualizing conflict resolution strategies in multiple robot systems. In 5th IASTED International Conference, Robotics and Manufacturing. Cancun, Mexico: IASTED Press.

    Google Scholar 

  • McCarthy, J. (2002). Actions and other events in situational calculus 8th International Conf On Principles of Knowledge Representation and Reasoning KR02 Toulouse France.

  • Mueller, E. T. (2004). A tool for satisfiability-based commonsense reasoning in the event calculus. In Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference. Menlo Park, CA: AAAI Press.

    Google Scholar 

  • Muggleton, S. (Ed.) (1992). Inductive logic programming. Academic Press: Burlington, MA, USA.

  • Muggleton, S., & Firth, J. (1999). CProgol4.4: Theory and use. In S. Dzeroski & N. Lavrac (Eds.), Inductive logic programming and knowledge discovery in databases.

  • Nikolopoulos, C., & Choi, C. (1994). A consumer complaint behavior neural forecasting model. Applied Informatics, 61–63.

  • Pattison-Gordon, E., Cimino, J. J., Hripcsack, G., Tu, S. W., Gennari, J. H., Jain, N. L. et al. (1996). Requirements of a sharable guideline representation for computer applications. Stanford University, Report No. SMI-96-0628.

  • Reiter, R. (1993). Proving properties of states in the situational calculus. Artificial Intelligence, 64, 337–351.

    Google Scholar 

  • Reynolds, J. C. (1970). Transformational systems and the algebraic structure of atomic formulas. In Machine Intelligence, vol. 5 (pp. 135–151). Edinburgh University Press: Edinburgh, UK.

  • Shanahan, M. (1997). Solving the frame problem. MIT Press: Boston, MA, USA.

  • Singh, P., & Barry, B. (2003). Collecting commonsense experiences. In Proceedings of the Second International Conference on Knowledge Capture (K-CAP 03). Sanibel Island, Florida.

  • Souchansky, M. (2001). On-line Decision-Theoretic GOLOG Interpreter. IJCAI-01.

  • Vinogradov, D. V. (1999). Logic programs for quasi-axiomatic theories. NTI Series 2, N1-2, 61–64.

    Google Scholar 

  • Wooldridge, M. (2002). Reasoning about rational agents. Cambridge, MA and London, England: The MIT Press.

    MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Computer Science and Information Systems, Birkbeck College, University of London, Malet Street, London, WC1E 7HfX, UK

    Boris Galitsky

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  1. Boris Galitsky
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Correspondence to Boris Galitsky.

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Galitsky, B. Merging deductive and inductive reasoning for processing textual descriptions of inter-human conflicts. J Intell Inf Syst 27, 21–48 (2006). https://doi.org/10.1007/s10844-006-1641-0

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  • DOI: https://doi.org/10.1007/s10844-006-1641-0

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