Argumentation in Weak Theory Domains

  • Kathleen Freeman
  • Arthur M. Farley
Conference paper
Part of the Workshops in Computing book series (WORKSHOPS COMP.)

Abstract

We present argumentation as a method for reasoning in “weak theory domains”, i.e., domains where knowledge is incomplete, uncertain, and/or inconsistent. We see these factors as related: methods for reasoning under incomplete knowledge, for example, default reasoning, plausible reasoning, and evidential reasoning, may result in conclusions that are uncertain and/or inconsistent. Knowledge in many domains in which we’d like computers to reason can be expected to be incomplete, and therefore, possibly inconsistent. Also, some domains, e.g., legal reasoning, may be inherently inconsistent. Therefore, it is important to investigate methods for reasoning under inconsistency.

We explore the use of argumentation as a basis for this task. Argumentation is a method for locating, highlighting, and organizing relevant information both in support of and counter to a plausible claim. This information can then serve as a vehicle for comparing the merits of competing claims.

We present aspects of our preliminary investigation of a formal theory of argumentation: (i) identifying a formal theory of argumentation; (ii) implementing the theory in a computer program; (iii) gathering example problems and associated arguments; and (iv) evaluating the theory with respect to the example arguments. Current work concentrates on the structure and generation of independent arguments for an input claim and its negation. Future work will focus on argumentation as a series of adversarial moves that support and counter a claim.

Keywords

Smoke Defend Shoe Ethos Undercut 

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References

  1. Ashley, K. (1989). Toward a computational theory of arguing with precedents. Proceedings of the Second International Conference on Artificial Intelligence and Law, 93–102.Google Scholar
  2. Buchanan, B. & Shortliffe, E. (1984). Rule-based expert systems. Reading, MA: Addison-Wesley.Google Scholar
  3. Conklin, J. (1988). Design rationale and maintainability (Report STP-249-88). Microelectronics and Computer Technology Corporation, Austin, TX.Google Scholar
  4. Cox, J. R. & Willard, C. A. (Eds.). (1982). Advances in argumentation theory and research. Carbondale, IL: Southern Illinois University Press.Google Scholar
  5. Dubois, D. & Prade, H. (1989). Handling uncertainty in expert systems — pitfalls, difficulties, remedies. In E. Hollnagel (Ed.), The reliability of expert systems (pp. 64–118). West Sussex: Ellis Horwood Limited.Google Scholar
  6. Flowers, M., McGuire, R., & Birnbaum, L. (1982). Adversary arguments and the logic of personal attacks. In W. Lehnert & M. Ringle (Eds.), Strategies for natural language processing (pp. 275–294). Hillsdale, NJ: Erlbaum Associates.Google Scholar
  7. Freeley, A. (1990). Argumentation and debate: Critical thinking for reasoned decision making (7th ed.). Belmont, CA: Wadsworth Publishing Company.Google Scholar
  8. Goldszmidt, M. & Pearl J. (1991). On the consistency of defeasible databases, Artificial Intelligence, 52, 121–1CrossRefMathSciNetMATHGoogle Scholar
  9. Hample, D. (1982). Modeling argument. In J. R. Cox & C. A. Willard (Eds.), Advances in argumentation theory and research (pp. 259–284). Carbondale, IL: Southern Illinois University Press.Google Scholar
  10. Horner, W. (1988). Rhetoric in the classical tradition. New York, NY: St. Martin’s Press.Google Scholar
  11. Kahane, H. (1988). Logic and contemporary rhetoric (5th ed.). Belmont, CA: Wadsworth Publishing Co.Google Scholar
  12. Lea Sombe (1990). Reasoning under incomplete information in artificial intelligence. International Journal of Intelligent Systems, 5, 323–472.CrossRefMATHGoogle Scholar
  13. Lee, J. (1991). DRL: A task-specific argumentation language. Proceedings of the AAAI Spring Symposium on Argumentation and Belief 122–132.Google Scholar
  14. Levi, E. (1949). An introduction to legal reasoning. Chicago, IL: University of Chicago Press.Google Scholar
  15. Marshall, C. (1989). Representing the structure of a legal argument. Proceedings of the Second International Conference on Artificial Intelligence and Law, 121–127.Google Scholar
  16. Pearl, J. (1987). Embracing causality in formal reasoning. Proceedings of the Sixth National Conference on Artificial Intelligence, 369–373.Google Scholar
  17. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufmann Publishers.Google Scholar
  18. Pollock, J. (1987). Defeasible reasoning. Cognitive Science, 11, 481–518.CrossRefGoogle Scholar
  19. Polya, G. (1968). Mathematics and plausible reasoning (2nd ed.) (vol. II). Princeton, NJ: Princeton University Press.MATHGoogle Scholar
  20. Poole, D. (1989). Explanation and prediction: an architecture for default and abductive reasoning. Computational Intelligence, 5, 97–110.CrossRefGoogle Scholar
  21. Porter, B., Bareiss, R., & Holte, R. (1990). Concept learning and heuristic classification in weak theory domains. Artificial Intelligence, 45, 229–263. Proceedings of the AAAI Spring Symposium on Argumentation and Belief. (1991).CrossRefGoogle Scholar
  22. Rescher, N. (1977). Dialectics: A controversy-oriented approach to the theory of knowledge. Albany, NY: State University of New York Press.Google Scholar
  23. Rissland, E. (1985). Argument moves and hypotheticals. In C. Walter (Ed.), Computing power and legal reasoning. St. Paul, MN: West Publishing Co.Google Scholar
  24. Rissland, E. (1989). Dimension-based analysis of hypotheticals from Supreme Court oral argument. Proceedings of the Second International Conference on Artificial Intelligence and Law, 111–120.Google Scholar
  25. Storrs, G. (1991). Extensions to Toulmin for capturing real arguments. Proceedings of the AAAI Spring Symposium on Argumentation and Belief, 195–204.Google Scholar
  26. Toulmin, S. (1958). The uses of argument. Cambridge: Cambridge University Press.Google Scholar

Copyright information

© British Computer Society 1993

Authors and Affiliations

  • Kathleen Freeman
    • 1
  • Arthur M. Farley
    • 1
  1. 1.Department of Computer and Information ScienceUniversity of OregonEugeneUSA

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