Peruasion as a form of inter-agent negotiation

  • Chris Reed
  • Derek Longe
  • Maria Fox
  • Max Garagnani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1286)


Agents in a multi-agent environment must often cooperate to achieve their objectives. In this paper an agent, B, cooperates with another agent, A, if B adopts a goal that furthers A's objectives in the environment. If agents are independent and motivated by their own interests, cooperation cannot be relied upon and it may be necessary for A to persuade B to adopt a cooperative goal. This paper is concerned with the organisation and construction of persuasive argument, and examines how a rational agent comes to hold a belief, and thus, how new beliefs might be engendered and existing beliefs altered, through the process of argumentation. Argument represents an opportunity for an agent to convince a possibly sceptical or resistant audience of the veracity of its own beliefs. This ability is a vital component of rich communication, facilitating explanation, instruction, cooperation and conflict resolution. An architecture is described in which a hierarchical planner is used to develop discourse plans which can be realised in natural language using the LOLITA system. Planning is concerned with the intentional, contextual and pragmatic aspects of discourse structure as well as with the logical form of the argument and its stylistic organisation. In this paper attention is restricted to the planning of persuasive discourse, or monologue.


agent communication argumentation theory rhetoric belief modelling planning 


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  1. [1]
    Ackermann, R.J. Belief and Knowledge, Anchor, New York (1972)Google Scholar
  2. [2]
    Baccus F. & Yang Q. The expected value of hierarchical problem-solving, in Proceedings of the National Conference on Artifical Intelligence (1992)Google Scholar
  3. [3]
    Blair, H. Lectures on Rhetoric and Belles Lettres, Charles Daly, London (1838)Google Scholar
  4. [4]
    Cohen, P.R., Levesque, H.J. “Rational Interaction as the Basis for Communication” in Cohen, P.R., Morgan, J., Pollack, M.E., (eds), Intentions in Communication, MIT Press, Boston (1990) 221–255Google Scholar
  5. [5]
    Fox, M. & Long, D.P. “Hierarchical Planning using Abstraction”, IEE Proc on Control Theory and Applications 142 (3) (1995) 197–210Google Scholar
  6. [6]
    Freese, J.H. (trans), Aristotle The Art of Rhetoric, Heinmann, London (1926)Google Scholar
  7. [7]
    Galliers, J.R. “Autonomous belief revision and communication” in Gardenfors, P., (ed), Belief Revision, Cambridge University Press, Cambridge (1992) 220–246Google Scholar
  8. [8]
    Grosz, B., Sidner, C.L “Plans for Discourse” in Cohen, P.R., Morgan, J. & Pollack, M.E., (eds), Intentions in Communication, MIT Press, Boston (1990) 418–444Google Scholar
  9. [9]
    Hovy, E. H. “Automated Discourse Generation Using Discourse Structure Relations”, Artificial Intelligence 63 (1993) 341–385Google Scholar
  10. [10]
    Long, D., Garigliano, R. Reasoning by Analogy and Causality: A Model and Application, Ellis Horwood (1994)Google Scholar
  11. [11]
    Mann, W.C., Thompson, S.A. “Rhetorical structure theory: description and construction of text structures” in Kempen, G., Natural Language Generation: New Results in AI, Psychology and Linguistics, Kluwer (1986) 279–300Google Scholar
  12. [12]
    Moore, J.D., Paris, C.L. “Planning Text for Advisory Dialogues: Capturing Intentional and Rhetorical Information”, Computational Linguistics 19 (4) (1994) 651–694Google Scholar
  13. [13]
    Popper, K.R. The Logic of Scientific Discovery, Hutchinson Education (1959)Google Scholar
  14. [14]
    Reed, C.A., Long, D.P. & Fox, M. “An Architecture for Argumentative Dialogue Planning”, in Gabbay, D. & Ohlbach, H.J. Practical Reasoning, Lecture Notes in AI Vol. 1085, Springer Verlag (1996) 555–566Google Scholar
  15. [15]
    Sacerdoti, E.D. A structure for plans and behaviour, Elsevier, North Holland (1977)Google Scholar
  16. [16]
    [16]Sandell, R. Linguistic Style and Persuasion, Academic Press, London (1977)Google Scholar
  17. [17]
    Shiu, S., Luo, Z., Garigliano, R. “A type theoretic semantics for SemNet”, in Gabbay, D. & Ohlbach, H.J. Practical Reasoning, Lecture Notes in AI Vol. 1085, Springer Verlag (1996) 582–595Google Scholar
  18. [18]
    Smith, M.H., Garigliano, R., Morgan, R.C. “Generation in the LOLITA system: An engineering approach” in Proceedings of the 7th International Workshop on Natural Language Generation, Kennebunkport, Maine (1994)Google Scholar
  19. [19]
    Smith, M.H. Natural Language Generation in LOLITA, PhD Thesis, Durham University (1996)Google Scholar
  20. [20]
    Sycara, K. “Argumentation: Planning Other Agent's Plans” in Proceedings of the International Joint Conference on Artificial Intelligence (1989) 517-523Google Scholar
  21. [21]
    Tate, A. Project planning using a hierarchic non-linear planner, Technical Report, Department of Artifical Intelligence, University of Edinburgh (1976)Google Scholar
  22. [22]
    Vreeswijk, G. “IACAS: an implementation of Chisolm's Principles of Knowledge”, Proceedings of the 2nd Dutch/German Workshop on Nonmonotonic Reasoning, Utrecht, Witteveen, C. et al. (eds) (1995) 225–234Google Scholar
  23. [23]
    Whately, R. Logic, Richard Griffin, London (1855)Google Scholar
  24. [24]
    Wilkins D. Practical planning: Extending the classical AI paradigm, Addison-Wesley (1988)Google Scholar
  25. [25]
    Wilson, B.A. The Anatomy of Argument, University Press of America, Washington (1980)Google Scholar
  26. [26]
    Young, R.M., Moore, J.D. “DPOCL: A principled approach to discourse planning” in Proceedings of the 7th International Workshop on Natural Language Generation, Kennebunkport, Maine (1994) 13-20Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Chris Reed
    • 1
  • Derek Longe
    • 1
  • Maria Fox
    • 2
  • Max Garagnani
    • 2
  1. 1.Department of Computer ScienceUniversity College LondonLondon
  2. 2.Department of Computer ScienceDurham UniversityDurham

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