Skip to main content
SpringerLink
Log in

Explanation and Argumentation Capabilities:Towards the Creation of More Persuasive Agents

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

During the past two decades many research teams have worked on the enhancement of the explanation capabilities of knowledge-based systems and decision support systems. During the same period, other researchers have worked on the development of argumentative techniques for software systems. We think that it would be interesting for the researchers belonging to these different communities to share their experiences and to develop systems that take advantage of the advances gained in each domain.

We start by reviewing the evolution of explanation systems from the simple reasoning traces associated with early expert systems to recent research on interactive and collaborative explanations. We then discuss the characteristics of critiquing systems that test the credibility of the user's solution. The rest of the paper deals with the different application domains that use argumentative techniques. First, we discuss how argumentative reasoning can be captured by a general structure in which a given claim or conclusions inferred from a set of data and how this argument structure relates to pragmatic knowledge, explanation production and practical reasoning. We discuss the role of argument indefeasible reasoning and present some works in the new field of computer-mediated defeasibleargumentation. We review different application domains such as computer-mediated communication, design rationale, crisis management and knowledge management, in which argumentation support tools are used. We describe models in which arguments are associated to mental attitudes such as goals, plans and beliefs. We present recent advances in the application of argumentative techniques to multi-agent systems. Finally, we propose research perspectives for the integration of explanation and argumentation capabilities in knowledge-based systems and make suggestions for enhancing the argumentation and persuasion capabilities of software agents

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alexander, S. M. & Evans, G. W. (1988). The Integration of Multiple Experts: A Review of Methodologies. In Turban E. & Watkins P. R. (eds.), Applied Expert Systems, 47–53. North Holland: Amsterdam.

    Google Scholar 

  • Allen, J. F. & Perrault, C. R. (1980). Analyzing Intention in Utterances. Artificial Intelligence 15: 143–178.

    Article  Google Scholar 

  • Alvarado, S. J. (1990). Understanding Editorial Text: A Computer Model of Argument Comprehension. Kluwer Academic Publishers.

  • Amgoud, L. & Cayrol, C. (2000). A Reasoning Model Based on the Production of Acceptable Arguments. In Linköping Series of Articles in Computer and Information Science, 5 (http://www.ida.liu.se/ext/epa/cis/ufn-00/01/tcover.html).

  • Amgoud, L., Maudet, N. & Parsons S. (2000). Modeling Dialogues Using Argumentation. In Proceedings of the 4th International Conference on MultiAgent Systems (ICMAS2000), 31-38. Boston, USA.

  • Anscombre, J-C. (1995). Théorie des topoï. Kimé: Paris.

    Google Scholar 

  • Anscombre, J-C. & Ducrot O. (1983). L'argumentation dans la langue. Magrada: Bruxelles.

    Google Scholar 

  • Ashley, K. D. (1990). Modeling Legal Argument: Reasoning with Cases and Hypothetical. The MIT Press.

  • Baker, M. (1992). The Collaborative Construction of Explanations, Actes des Deuxièmes Journées du PRC-GDR-IA du CNRS, 25-40. Sophia Antipolis: France (In French).

  • Baker, M., Dessalles, J-L., Joab, M., Raccah, P-Y., Safar, B. & Schlienger, D. (1994). La génération d'explications négociées dans un système à base de connaissances, Actes des Cinquièmes Journées du PRC-GDR-IA, 296–316. Teknéa: Toulouse.

    Google Scholar 

  • Ball, W. J. (1994). Using Virgil to Analyse Public Policy Arguments: A System Based on Toulmin's Informal Logic. Social Science Computer Review 12: 26–37.

    Article  Google Scholar 

  • Baroni, P., Giacomin, M. & Guida, G. (2000). Extending Abstract Argumentation Systems Theory. Artificial Intelligence 120: 251–270.

    Article  MATH  MathSciNet  Google Scholar 

  • van Beek, P. (1987). A Model for Generating Better Explanations. Proceedings of the 25th Annual Meeting of the Association for Computational Linguistics. Palo Alto, California.

  • Bell, R. A., Zahn, C. J. & Hoper, R. (1984). Disclaiming: A Test of Two Competing Views. Communications Quaterly 32: 28–36.

    Google Scholar 

  • Bench-Capon, T. J. M., Lowes, D. & McEnery, A. M. (1991). Argument-Based Explanation of Logic Programs. Knowledge Based Systems 4: 177–183.

    Article  Google Scholar 

  • Bench-Capon, T. M., Coenen, F. & Orton, P. (1993). Argument-Based Explanation of the British Nationality Act as a Logic Program. Computers, Law and AI 2: 53–66.

    Google Scholar 

  • Bench-Capon, T. J. M. (1998). Specification and Implementation of Toulmin Dialogue Game. In Hage, J. C., Bench-Capon, T., Koers, A., de Vey Mestdagh, C. & Grutters, C. (eds.) Jurix 1998, 5–20. Foundation for Legal Knowledge Based Systems, Gerard Noodt Institut: Nijmegen, The Netherlands.

    Google Scholar 

  • Benyon, D. & Murray, D. (1993). Applying User Modeling to Human-Computer Interaction Design. Artificial Intelligence Review 7: 199–225.

    Article  Google Scholar 

  • Birnbaum, L., Flowers, M. & McGuire, R. (1980). Towards an AI Model of Argumentation. In Proceedings of the AAAI Conference, 313-315.

  • Buckingham, S. S. & Hammond, N. (1994). Argumentation-Based Design Rationale: What Use at What Cost? International Journal of Human-Computer Studies 40: 603–652.

    Article  Google Scholar 

  • Buckingham, S. S., MacLean, A., Bellotti, V. & Hammond N., (1997). Graphical Argumentation and Design Cognition. Human Computer Interaction 12: 267–300.

    Article  Google Scholar 

  • Bunt, H. C. (1990). Modular Incremental Modeling of Belief and Intention. Proceedings of the Second International Workshop on User Modeling.

  • Carberry, S. (1988). Modeling the User's Plan and Goals. Computational Linguistics 14: 23–37.

    Google Scholar 

  • Carenini, G. & Moore, J. (1999). Tailoring Evaluative Arguments to User's Preferences. Proceedings of the Seventh International Conference on User Modeling (UM-99). Banff, Canada, June 20-24.

  • Cavalli-Sforza, V. & Moore, J. D. (1992). Collaborating on Arguments and Explanations. In Working Notes for the AAAI Spring Symposium on Producing Cooperative Explanation. Stanford University, CA.

    Google Scholar 

  • Cawsey, A., Galliers, J., Reece, S. & Sparck-Jones, K. (1992). The Role of Explanations in Collaborative Problem Solving. In Brézillon, P. (ed.) Proceedings of the ECAI-92 Workshop on Improving the use of Knowledge-Based Systems with Explanations. Université Paris: Paris, 6.

    Google Scholar 

  • Cawsey, A. (1993). Planning Interactive Explanations. International Journal of Man-Machine Studies 38.

  • Cawsey, A. (1995). Developing an Explanation Component for a Knowledge-Based System: Discussion. Expert Systems with Applications 8: 527–531.

    Article  Google Scholar 

  • Chandrasekaran, A. B. & Mittal, S. (1983). Deep versus Compiled Knowledge Approaches to Diagnostic Problem-Solving. International Journal of Man-Machine Studies 19: 425–436.

    Article  Google Scholar 

  • Chandrasekaran, B. (1986). Generic Tasks in Knowledge Based Reasoning. IEEE Expert 1: 23–30.

    Article  Google Scholar 

  • Chandrasekaran, B., Tanner, M. C. & Josephson, J. R. (1989). Explaining Control Strategies in Problem Solving. IEEE Expert 4: 9–24.

    Article  Google Scholar 

  • Chesnevar, C. I., Maguitman, A. G. & Loui, R. P. (2000). Logical Models of Argument. ACM Computing Surveys (to appear).

  • Chin, D. N. (1993). Acquiring User Models. Artificial Intelligence Review 7: 185–197.

    Article  Google Scholar 

  • Clancey, W. J. (1983a). The Epistemology of a Rule-Based Expert System: A Framework for Explanation. Artificial Intelligence 20: 215–251.

    Article  Google Scholar 

  • Clancey, W. J. (1983b). The Advantages of Abstract Control Knowledge in Expert System Design. Proceedings of the National Conference on Artificial Intelligence.

  • Clark, P. (1990). Representing knowledge as Arguments: Applying Expert system Technology to Judgemental Problem-Solving, In Addes, T. & Muir, R. (eds.) Research and Development in Expert Systems III, 147-159. Cambridge University Press.

  • Clark, P. (1991). A Model of Argumentation and its Application in a Cooperative Expert System. Ph.D. Thesis, Turing Institute, University of Strathclyde, Glasgow, U.K.

    Google Scholar 

  • Cohen, R., Jones, M., Sanmugasunderam, A., Spencer, B. & Dent, L. (1989). Providing Responses Specific to a User's Goals and Background. International Journal of Expert Systems 2.

  • Conklin, J. & Begeman, M. L. (1987). gIBIS: A Hypertext Tool for Team Design Deliberation. In Smith, J. B. & Halasz, F. (eds.) Hypertext'87, 247-251. Association for Computing Machinery.

  • Conklin, J. & Begeman, M. L. (1988). gIBIS: A Hypertext Tool for Exploratory Policy Discussion. Transactions on Office Information Systems 6: 303–331.

    Article  Google Scholar 

  • Coombs, M. & Alty, J. (1984). Expert Systems: An Alternative Paradigm. International Journal of Man-Machine Studies 20: 21–43.

    Article  Google Scholar 

  • Daniel, B. H., Bares, W. H., Callaway, B. C. & Lester, J. C. (1999). Student-Sensitive Multimodal Explanation Generation for 3D Learning Environments. Proceedings of the 16th National Conference on Artificial Intelligence.

  • Davis, R. (1982). Teiresias: Applications of Meta-Level Knowledge. In Davis, R. and Lenat, D. B. (eds.) Knowledge-Based Systems in Artificial Intelligence. McGraw-Hill.

  • Després, S. (1996). Enoncés graduels, prédicats graduels ou topoï? In (Raccah 1996), 57-77.

  • Dhaliwal, J. S. (1993). An Experimental Investigation of the Use of Explanations Provided by Knowledge-Based Systems. Unpublished Ph.D Thesis, University of British Columbia, Management Information System Division.

  • Dieng, R. (1989). Generation of Topoi from Expert Systems. CCAI 6:4, P.Y. Raccah (ed.), Gand.

    Google Scholar 

  • Dick, J. P. (1987). Conceptual Retrieval and Case Law. In Proceedings of the First International Conference on Artificial Intelligence and Law, 106-115. ACM Press.

  • Draper, S. W. (1987). A User-Centered Concept of Explanation. Proceedings of the 2nd Workshop of the Explanation Special Interest Group, 15-23.

  • Dubois, D. & Prades, H. (1987). Théorie des possibilités: Applications àla représentation des connaissances en informatique. Masson: Paris.

    Google Scholar 

  • Ducrot, O. (1991). Dire ou ne pas Dire, Principes de sémantique linguistique. Herman, Collection Savoir: Paris.

    Google Scholar 

  • Dung, P. H. (1995). On the acceptability of Arguments and Its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games. Artificial Intelligence 77: 321–357.

    Article  MATH  MathSciNet  Google Scholar 

  • Farley, A. M. & Freeman, K. (1995). Burden of Proof in Legal Argumentation. Proceedings of the Fifth International Conference on Artificial Intelligence and Law, 156–164. ACM Press: USA.

    Chapter  Google Scholar 

  • Feiner, S. K. & McKeown, K. R. (1991). Automating the Generation of Coordinated Multimedia Explanations. IEEE Computer 24: 33–41.

    Article  Google Scholar 

  • Fischer, G. & Mastaglio, Th. (1991). A Conceptual Framework for Knowledge-Based Critiquing Systems. Decision Support Systems 7: 355–378.

    Article  Google Scholar 

  • Flowers, M., McGuire, R. & Birnbaum, L. (1982). Adversary Arguments and the Logic of Personal Attacks. In Lehnert, W. G. & Ringle, M. G. (eds.) Strategies for Natural Language Processing. Lawrence Erlbaum: Hillsdale, NJ.

    Google Scholar 

  • Foner, L. (1997). Yenta: A Multi-Agent, Referral-BasedMatchmaking System. In Proceedings of the First International Conference on Autonomous Agents, 301-307.

  • Forsythe, D. E. (1995). Using Ethnography in the Design of an Explanation System. Expert Systems with Applications 8: 403–417.

    Article  Google Scholar 

  • Freeman, K. (1994). Toward formalizing Dialectical Argumentation. PhD thesis, Department of Computer Science and Information Science, University of Oregon.

  • Galarreta, D. & Trousse, B. (1996). Place de l'argumentation dans la conception d'outils d'assistance à une activité de résolution de problème. In (Raccah 1996), 79-103.

  • Giboin, A. (1995). Les explications destinées aux utilisateurs de systèmes à base de connaissances. Bulletin de l'AFIA 20: 21–30.

    Google Scholar 

  • Gilbert, N. (1989). Explanation and Dialogue. Knowledge Engineering Review 4: 205–231.

    Article  Google Scholar 

  • Giunchiglia, F. & Serafini, L. (1994). Multi-Language-Hierarchical Logics (or: How We Can Do Without Modal Logic). Artificial Intelligence 65: 29–70.

    Article  MATH  MathSciNet  Google Scholar 

  • Goguen, J. A., Weiner, J. L. & Linde, C. (1983). Reasoning and Natural Explanation. International Journal of Man-Machine Studies 19: 521–559.

    Article  Google Scholar 

  • Gordon, T. F. (1995). The Pleadings Game: An exercise in Computational Dialectics. Artificial Intelligence and Law 2: 239–292.

    Article  Google Scholar 

  • Grasso, F. (1997). Using Dialectical Argumentation for User Modeling In Decision Support Systems. In Jameson A., Paris, C. & Tasso, C. (eds.) User Modeling: Proceedings of the Sixth International Conference, UM97. Springer: Vienna, New York.

    Google Scholar 

  • Guida, G., Mussio, P. & Zanella, M. (1997). User Interaction in Decision Support Systems: The role of justification. SMC's Conference Proceedings, 3215-3220. Orlando, Florida, October.

  • Guida, G. & Zanella, M. (1997). Bridging the Gap between Users and complex Decision Support Systems. Proceedings of the 3rd International Conference on Engineering of Complex Computer Systems, 229-238. Como, Italy, September.

  • Gumpertz, J. J. & Hymes, D. (1972). Directions in Sociolinguistics: The Ethnography of Communication. Holt, Rinehart and Winston.

  • Hahn, U. (1991). Erklärung als argumentativer Gruppendiskurs. Proceedings of Erklärung im Gespräch-Erklärung im Mensch-Maschine-Dialog. Springer-Verlag.

  • Hasling, D. W., Clancey, W. J. & Rennels, G. (1984). Strategic Explanations for a Diagnostic Consultation System. International Journal of Man-Machine Studies 20: 3–19.

    Article  Google Scholar 

  • Hayes, P. J. & Reddy, D. R. (1983). Steps Toward Graceful Interaction in Spoken and Written Man-Machine Communication. International Journal of Man-Machine Studies 19: 231–284.

    Article  Google Scholar 

  • Heider, F. (1946). Attitudes and Cognitive Organization. Journal of Psychology (21): 107-112.

    Google Scholar 

  • van Heijst, G., van der Spek, R. & Kruizinga, E. (1996). Organizing Corporate Memories. Workshop of the Tenth Banff, Workshop on Knowledge Acquisition for Knowledge-Based Systems, Banff, Canada (http://ksi.cpsc.ucalgary.ca/KAW/KAW96/KAW96Proc.html).

  • Hermann, J., Kloth, M. & Feldkamp, F. (1998). The Role of Explanations in an Intelligent Assistant System. Artificial Intelligence in Engineering 12: 107–126.

    Article  Google Scholar 

  • Hollnagel, E. (1987). Commentary: Issues in Knowledge-based Decision Support. International Journal of Man-Machine Studies 27: 743–751.

    Article  Google Scholar 

  • Holsapple, C. W. & Whinston, A. B. (1996). Decision Support Systems: A Knowledge-Based Approach. International Thomson Publishing Company.

  • Horn, R. E. (ed.) (1998). Can Computers Think? The Debate. MacroVU Press: www.macrovu.com.

  • Hovland, C. I., Janis, I. L. & Kelley, H. H. (1953). Communication and Persuasion. Yale University Press: New Haven, CT.

    Google Scholar 

  • Hughes, S. (1986). Question Classification in Rule-Based Systems. Proceedings of Expert Systems 1986.

  • Hughes, W. (1992). Critical Thinking. Broadview Press: Petersborough, ON.

    Google Scholar 

  • Jiang, J. J., Muhanna, W. A. & Klein, G. (2000). User Resistance and Strategies for Promoting Acceptance across System Types. Information and Management 37: 25–36.

    Article  Google Scholar 

  • Johnson, P. E., Zualkernan, I. A. & Tukey, D. (1993). Types of Expertise: An Invariant of Problem Solving. International Journal of Man Machine Studies 39: 6–41.

    Google Scholar 

  • Joshi, A. & Webber, B. (1984). Living up to Expectations; Computing Expert Responses. Proceedings of AAAI-84, 169-175. Austin, Texas.

  • Jung, H., Tambe, M. & Kulkarni, S. (2001). Argumentation as Distributed Constraint Satisfaction: Applications and Results. In Müller, J. P., André, E., Sen, S., & Frasson, C. (eds.) Proceedings of the Fifth International Conference on Autonomous Agents, 324-331. Montreal Canada.

  • Karlins, M. & Abelson, H. I. (1970). Persuasion: How Opinions and Attitudes are Changed. Springer Verlag: Berlin.

    Google Scholar 

  • Karsenty, L. & Brézillon, P. J. (1995). Cooperative Problem Solving and Explanation. Expert Systems with Applications 8: 445–462.

    Article  Google Scholar 

  • Kasper, G. M. (1996). A Theory of Decision Support System Design for User Calibration. Information Systems Research 7: 215–232.

    Article  Google Scholar 

  • Kass, R. (1991). Building a User Model. User Model and User Adapted Interaction 1: 203–258.

    Article  Google Scholar 

  • Kay, J. (1993). Reusable Tools for User Modeling. Artificial Intelligence Review 7: 241–225.

    Article  Google Scholar 

  • Klein, D. & Finin, T. (1987). What's in a Deep Model? Proceedings of IJCAI-87. Milan, Italy.

  • Kolodner, J. (1993). Case-Based Reasoning. Morgan Kaufmann.

  • Kostulski, K. & Trognon, A. (eds.) (1998). Processus de coordination dans les situations de travail collectif. Presses Universitaires de Nancy: Nancy.

    Google Scholar 

  • Kraus, P., Ambler, S., Elvang-Goransson, M. & Fox, J. (1995). A Logic of Argumentation for Reasoning under Uncertainty. Computational Intelligence 11: 113–131.

    Article  MathSciNet  Google Scholar 

  • Kraus, S., Sycara, K. & Evenchik, A. (1998). Reaching Agreements Through Argumentation: a Logical Model and Implementation. Artificial Intelligence 104: 1–69.

    Article  MATH  MathSciNet  Google Scholar 

  • Kunz, W. & Rittel, H. W. J. (1970). Issues as Elements of Information Systems, Working paper, Center of Planning and Development Research, University of California, Berkeley.

    Google Scholar 

  • Langlotz, C. P. & Shortliffe, E. H. (1983). Adapting a Consultation System to Critique User Plans. International Journal of Man-Machine Studies 19: 479–496.

    Article  Google Scholar 

  • Leake, D. B. (1995). Towards a Goal-Driven Integration of Explanation and Action. In Ram, A. & Leake, D. G. (eds.) Goal-Driven Learning. MIT Press.

  • Lehman, J. F. & Carbonell, J. G (1989). Learning the User's Language: A Step Towards Automated Creation of User Models. In Kobsa, A. & Wahlster, W. (eds.) User Models in Dialogue Systems. Springer Verlag: Berlin-New York.

    Google Scholar 

  • Lehnert, W. G. (1978). The Process of Question Answering. Lawrence Erlbaum Associates.

  • Lemaire, B. (1992). Construction d'explications: Utilisation d'une architecture de tableau noir. Actes des 4èmes Journées Nationales du PRC-GDR-Intelligence Artificielle. Marseille.

  • Lin, F. & Shoham, Y. (1989). Argument Systems: A Uniform Basis for Nonmonotonic Reasoning. In Proceedings of First International Conference on Knowledge Representation and Reasoning, 245-255. Toronto.

  • Lodder, A. R. (1997). On Structure and Naturalness in Dialogical Models of Argumentation. In Hage, J. C. et al. (eds.) Legal Knowledge-Based Systems. JURIX: The Eleventh Conference, 45–58. GNI: Nijmegen.

    Google Scholar 

  • Lodder, A. R. (1998). Procedural Arguments. In Oskamp, A. et al. (eds.) Legal Knowledge-Based Systems. JURIX: The tenth Conference, 21–32. GNI: Nijmegen.

    Google Scholar 

  • Loui, R. P. (1987). Defeat Among Arguments: A System of Defeasible Inference. Computational Intelligence 3: 157–365.

    Article  Google Scholar 

  • Loui, R. P. & Norman, J. (1995). Rationales and Argument Moves. Artificial Intelligence and Law 3: 159–189.

    Article  Google Scholar 

  • Loui, R. P., Norman, J., Alteper, J., Pinckard, D., Craven, D., Lindsay, J. & Foltz, M. (1997). Progress on Room 5. A Testbed for Public Interactive Semi-Formal Legal Argumentation. Proceedings of the Sixth International Conference on Artificial Intelligence and Law, 207–214. ACM: New York.

    Chapter  Google Scholar 

  • Mani, I., Conception, K. & Van Guilder, L. (2000). Automated Briefing Production for Lessons Learned Systems. In Proceedings of Intelligent Lessons Learned Systems, a Workshop at AAAI 2000, Austin (Tx), 43-45. American Association of Artificial Intelligence (AAAI).

  • Marshall, C. C. (1989). Representing the Structure of Legal Argument, In Proceedings of Second International Conference on Artificial Intelligence and Law, 121–127. ACM Press: USA.

    Chapter  Google Scholar 

  • Matthijssen, L. J. (1999). Interfacing between Lawyers and Computers. An Architecture for Knowledge-Based Interfaces to Legal Databases. Kluwer Law International: The Netherlands.

    Google Scholar 

  • Maybury, M. T. (1993). Planning Multimedia Explanations Using Communicative Acts. Intelligent Multimedia Interfaces. MIT Press.

  • McKeown, K. R. (1985). Discourse Strategies for Generating Natural-Language Text. Artificial Intelligence 27.

  • McKeown, K. R. (1988). Generating Goal-Oriented Explanations. International Journal of Expert Systems 1.

  • McKeown, K. R. (1993). Tailoring Lexical Choice to the User's Vocabulary in Multimedia Explanation Generation. Proceedings of the 31st Annual Meeting of the ACL. Columbus, Ohio.

  • McTear, M. F. (1993). User Modeling for Adaptive Computer Systems: A Survey of Recent Developments. Artificial Intelligence Review 7: 157–184.

    Article  Google Scholar 

  • Mili, F. (1988). A Framework for a Decision Critic and Advisor. Proceedings of the 21st Hawaiian Conference on System Sciences 3: 381–386.

    Google Scholar 

  • Miller, P. L. (1984). A Critique Approach to Expert Computer Advice: ATTENDING. Pitman.

  • Mittal, V. O. & Paris, C. L. (1995). Generating Explanations in Context: The System Perspective. Expert Systems with Applications 8: 491–503.

    Article  Google Scholar 

  • Moeshler, J. (1985). Argumentation et conversation: Eléments pour une analyse pragmatique du discours. Hatier: Paris.

    Google Scholar 

  • Mooney, D. J., Carberry, S. & McCoy, K. F. (1991). Capturing High-Level Structure of Naturally Occurring, Extended Explanations Using bottom-up Strategies. Proceedings of the 5th International Language Generation Workshop. Dorson.

  • Moore, J. D. & Swartout, W. R. (1990). A Reactive Approach to Explanation: Taking the User's Feedback into Account. In Paris, C. L., Swartout, W. R. & Mann, W. C. (eds.) Natural Language Generation in Artificial Intelligence and Computational Linguistics. Kluwer Academic Publishers.

  • Moran, T. P. & Caroll J. M. (eds.) (1996). Design Rationale: Concepts, Techniques and Use. Lawrence Erlbaum Associates: Hillsdale N.J.

    Google Scholar 

  • Moulin, B. (1998). An Awareness-Based Model for Agents Involved in Reporting Activities. In Zhang, C. & Lukose, D. (eds.), Multi-Agent Systems, Theories, Languages and Applications, Lecture Notes in Artificial Intelligence 1544, 105–121. Springer Verlag: Berlin.

    Chapter  Google Scholar 

  • Neches, R., Swartout, W. R. & Moore, J. D. (1985). Enhanced Maintenance and Explanation of Expert Systems Through Explicit Models of Their Development. IEEE Transactions on Software Engineering SE-11: 1337–1351.

    Article  Google Scholar 

  • O'Keefe, D. J. (1990). Persuasion: Theory and Research. SAGE Publications.

  • O'Malley, C. (1987). Understanding Explanation. Cognitive Science research Report, n. CRSP 88. University of Sussex (UK).

    Google Scholar 

  • Papamichail, K. N. (1998). Explaining and Justifying Decision Support Advice in Intuitive Terms. Proceedings of the 13th European Conference on Artificial Intelligence. Brighton, UK, August.

  • Paris, C. L. (1988). Tailoring Object Descriptions to the User's Level of Expertise. Computational Linguistics 14: 64–78.

    Google Scholar 

  • Paris, C. L. (1989). The Use of Explicit User Models in a Generation System for Tailoring Answers to the User's Level of Expertise. In Kobsa. A. & Wahlster, W. (eds.) User Models in Dialog Systems. Springer Verlag: Berlin.

    Google Scholar 

  • Paris, C. L. (1991). The Role of User's Domain Knowledge in Generation. Computational Intelligence 7.

  • Parnagama P., Burstein, F. & Arnott, D. (1997). Modeling the Personality of Decision Makers for Active Decision Support. In Jameson, A., Paris, C. & Tasso, C. (eds.) User Modeling: Proceedings of the Sixth International Conference, UM97. Springer: Vienna, New York.

    Google Scholar 

  • Parsons, S., Sierra, C. & Jennings, N. (1998). Agents that Reason and Negotiate by Arguing, Journal of Logic and Computation 8: 261–292.

    Article  MATH  MathSciNet  Google Scholar 

  • Perelman, C. & Obrechts-Tyteca, L. (1958). La Nouvelle Rhétorique: Traité de l'Argumentation. Presses Universitaires de France, translated by Wilkenson J. and Weaver P. (1969). The New Rhetoric. University of Notre Dame Press: Notre Dame, Indiana.

    Google Scholar 

  • Pollack, M. E. (1984). Good Answers to Bad Questions, Goal Inference in Expert Advice-Giving. Proceedings of the Canadian Conference on Artificial Intelligence.

  • Pollack, M. E. (1986). A Model of Plan Inference that Distinguishes Between the Beliefs of Actors and Observers. Proceedings of the 24th Annual Meeting of the Association for Computational Linguistics. New York, NY.

  • Pollock, J. L. (1992). How to Reason Defeasibly? Artificial Intelligence 57: 1–42.

    Article  MATH  MathSciNet  Google Scholar 

  • Pollock, J. L. (1994). Justification and Defeat. Artificial Intelligence 67: 377–407.

    Article  MATH  MathSciNet  Google Scholar 

  • Prakken, H. & Sartor, G. (1997). Argument-Based Extended Logic Programming with Defeasible Priorities. Journal of Applied Non-Classical Logics 7: 25–75.

    MATH  MathSciNet  Google Scholar 

  • Quillici, A. (1991). Arguing over Plans. Proceedings of the AAAI Spring Symposium Series: Argumentation and Belief. Stanford, CA.

  • Raccah, P-Y. (ed.) (1996). Topoï et gestion des connaissances. Masson: Paris.

    Google Scholar 

  • Ram, A. (1994). AQUA: Questions that Drive the Explanation Process. In Schank, R. C., Kass, A. & Riesbeck, C. K. (eds.) Case-based Explanation, chapter 7, 207-261. Lawrence Erlbaum Associates.

  • Resnick, L. B., Salmon, M. H., Wathen, P. & Hollochak, J. B. (1993). Reasoning in Conversation. Cognition and Instruction 11: 347–364. (special issue on Discourse and SharedReasoning).

    Article  Google Scholar 

  • Rich, E. A. (1989). Stereotypes and User Modeling. In Kobsa. A. & Wahlster, W. (eds.) User Models in Dialog Systems. Springer Verlag: Berlin.

    Google Scholar 

  • Ringle, M. H. & Bruce, B. C. (1981). Conversation Failure. In Lehnert, W. G. & Ringle, M. H. (eds.) Knowledge Representation and Natural Language Processing, 203–221. Lawrence Erlbaum Associates: Hillsdale, NJ.

    Google Scholar 

  • Rissland, E. L. (1983). Examples in Legal Reasoning. Proceedings of the 8th International Joint Conference on Artificial Intelligence. Karlsruhe, Germany.

  • Rissland et al. (1984). Explaining and Arguing with Examples. Proceedings of AAAI-84, Austin, Texas, 288-294.

  • Rissland, E., Skalak, D. & Friedman, M. (1993). Bankxx: A Program to Generate Argument Through Case-Based Search. Proceedings of the Fourth International Conference on AI an Law 117-124. Amsterdam, the Netherlands.

  • Rosenblum, J. A. & Moore, J. D. (1993). Participating in Instructional Dialogues: Finding and Exploring Relevant Prior Explanations. In Brna, P., Ohlsson, S. & Pain, H. (eds.) Proceedings of AI-ED 93: World Conference on Artificial Intelligence and Education, 145-152. Charlottesville, VA.

  • de Rosis, F., Grasso, F., Berry, D. C. & Gillie, T. (1995). Mediating between Hearer's and Speaker's Views in the Generation of Adaptive Explanations. Expert Systems with Applications 8: 429–443.

    Article  Google Scholar 

  • Routley, R. & Meyer, R. (1976). Dialectical Logic, Classical Logic and the Conistency of the World. Studies in Soviet Thought, 16: 1–25.

    Article  Google Scholar 

  • Sawamura, H., Umeda, Y. & Meyer, R. K. (2000). Computational Dialectics for Argument-Based Agent Systems. In Proceedings of the Fourth International Conference on Multi-Agent Systems (ICMAS'2000), 271-278. IEEE Computer Society.

  • Schanck, R. C. (1986). Explanation: A First Pass. In Kolodner, J. L & Riesbeck, C. K. (eds.) Experience, Memory, and Reasoning, 139–165. Lawrence Erlbaum Associates: Hillsdale, NJ.

    Google Scholar 

  • Schillo,M. & Funk, P. (1999).Who Can You Trust: Dealing with Deception. In Proceedings of the Workshop on Deception, Fraud and Trust in Agent Societies, International Conference on Autonomous Agent, 95-106.

  • Scott, A. C., Clancey, W. J., Davis, R. & Shortliffe, E. H. (1977). Explanation Capabilities of Knowledge-Based Production Systems. In Buchanan, B. G. & Shortliffe, E. H. (eds.) Rule-Based Expert Systems. Addison Wesley.

  • Schroeder, M. (1999). An Efficient Argumentation Framework for Negotiating Autonomous Agents, In Proceedings of the 9th European Workshop on Modeling Autonomous Agents in a Multi-Agent World (MAAMAW'99), 140-149. Valencia, Spain.

  • Schroeder, M. (2000). Towards a Visualization of Arguing Agents. To appear in Journal of Future Generation Computing Systems. Elsevier. Available at: http://www.soi.city.ac.uk/homes/msch

  • Sherif, M. & Hovland C. I. (1961). Social Judgement. Yale University Press: New Haven, CT.

    Google Scholar 

  • Shipman, F. M. III & McCall, R. J. (1997). Integrating Different Perspectives on Design Rationale: Supporting the Emergence of Design Rationale from Design Communication. Artificial Intelligence in Engineering Design, Analysis and Manufacturing (AIEDAM) 11: 141–154.

    Article  Google Scholar 

  • Shipman, F. M. III & Marshall, C. C. (1999). Formality Considered Harmful: Experiences, Emerging Themes, and Directions on the use of Formal Representations in Interactive Systems. Computer-Supported Cooperative Work 8: 333–352.

    Article  Google Scholar 

  • Shneiderman, B. (1992). Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley: Reading, MA.

    Google Scholar 

  • Shortliffe, E. H. (1976). Computer-Based Medical Consultations: MYCIN. Elsevier.

  • Sillince, J. A. A. (1994). Multi-Agent Conflict Resolution: A Computational Framework for an Intelligent Argumentation Program. Knowledge-Based Systems 7: 75–90.

    Article  Google Scholar 

  • Sillince, J. A. A. (1996). Argumentation and Contract Models for Strategic Organization Support Systems, Decision Support Systems 16: 325–326.

    Article  Google Scholar 

  • Sillince, J. A. A. & Saeedi, M. H. (1999). Computer-Mediated Communication: Problems and Potential of Argumentation Support Systems. Decision Support Systems 26: 287–306.

    Article  Google Scholar 

  • Silverman, B. G. (1992). Survey of Expert Critiquing Systems: Practical and Theoretical Frontiers. Communications of the ACM 35: 106–127.

    Article  Google Scholar 

  • Simari, G. R. & Loui, R. P. (1992). A Mathematical Treatment of Defeasible Reasoning and Its Implementation. Artificial Intelligence 53: 125–157.

    Article  MATH  MathSciNet  Google Scholar 

  • Slotnick, S. A. & Moore, J. (1995). Explaining Quantitative Systems to Uninitiated Users. Experts Systems with Applications 8: 475–490.

    Article  Google Scholar 

  • Smolensky, P., Bell, B., Fox, B., King, R. & Lewis, C. (1987). Constraint-Based Hypertext for Argumentation. In Smith. J. B. & Halasz, F. (eds.) Hypertext'87, 215-245. Association for Computing Machinery.

  • Southwick, R. W. (1988). Topic Explanation in Expert Systems. In Kelly, B. & Rector, A. (eds.) Research and Development in Expert Systems V. Cambridge University Press.

  • Southwick, R.W. (1991). Explaining Reasoning: An Overview of Explanation in Knowledge-Based Systems. The Knowledge Engineering Review 6: 1–19.

    Article  Google Scholar 

  • Sparck-Jones, K. (1989). Realism about User Modeling. In Kobsa, A. & Wahlster, W. (eds.) User Models in Dialog Systems. Springer Verlag: Berlin.

    Google Scholar 

  • Stranieri, A., Zeleznikow, J., Gawler,M. & Lewis, B. (1999). A Hybrid Rule-Neural Approach for the Automation of Legal Reasoning in the Discretionary Domain of Family Law in Australia. Artificial Intelligence and Law, 7: 153–183.

    Article  Google Scholar 

  • Stranieri, A. & Zeleznikow, J. (1999). A Survey of Argument Structures for Intelligent Decision Support. In proceedings of International Conference ISDSS99, International Society for Decision Support Systems, rcorded on CD-Rom, Monash University, Australia.

    Google Scholar 

  • Stranieri, A. & Zeleznikow, J. (2000). Copyright Regulation with Argumentation Agents Submitted to Journal of Information and Communication Law.

  • Suthers, D. D. (1991). Task-Appropriate Hybrid Architectures for Explanation. Computational Intelligence 7.

  • Swartout, W. R. (1983). XPLAIN: A System for Creating an Explaining Expert Consulting Programs. Artificial Intelligence 21: 285–325.

    Article  Google Scholar 

  • Swartout, W. R. & Smoliar, S. W. (1987). On Making Expert Systems More Like Experts. Expert Systems 4: 96–207.

    Article  Google Scholar 

  • Swartout, W. R., Paris, C. L. & Moore, J. D. (1991). Design for Explainable Expert Systems. IEEE Expert 6: 58–64.

    Article  Google Scholar 

  • Swartout, W. R. & Moore, J. D. (1993). Explanation in Second Generation Expert Systems. In David, J.-M., Krivine, J.-P., & Simmons, R. (eds.) Second Generation Expert Systems. Springer Verlag Publishers.

  • Sycara, K. P. (1990). Persuasive Argumentation in Negotiation. Theory and Decision 28: 203–242.

    Article  Google Scholar 

  • Tanner, M. C. & Keuneke, A. M. (1991). Explanation in Knowledge Systems: The Roles of the Task Structure and Functional Models. IEEE Expert 6: 5–57.

    Article  Google Scholar 

  • Tanner, M. C. (1995). Task-Based Explanations. Expert Systems with Applications 8: 502–512.

    Article  Google Scholar 

  • Thomas, S. N. (1981). Practical Reasoning in Natural Language, 2nd edn. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Thomas, B., Shoham, Y., Schwartz, A. & Kraus, S. (1991). Preliminary Thoughts on an Agent Description Language. International Journal on Intelligent Systems, 6: 497–508.

    Article  MATH  Google Scholar 

  • Toulmin, S. (1958). The Uses of Argument. Cambridge University Press: Cambridge, England.

    Google Scholar 

  • Toulmin, S., Rieke R. & Janik, A. (1984). An Introduction to Reasoning. MacMillan: New York.

    Google Scholar 

  • Trenouth, J. & Ford, L. (1990). The User Interface - Computational Models of Computer Users. In McTear M. F. & Anderson T. J. (eds.) Understanding Knowledge Engineering. Ellis Horwood: Chichester.

    Google Scholar 

  • Trognon, A. & Larrue, J. (1994). Pragmatique du discours politique. Armand Collin: Paris.

    Google Scholar 

  • Umeda, Y., Yamashita, M., Inagaki, M. & Sawamura, H. (2000). Argumentation as a Social Computing Paradigm. In Proceedings of the Third Pacific Rim International Workshop on Multi-Agent Systems (PRIMA 2000). Springer Verlag Lecture Notes in Artificial Intelligence, 46-60.

  • Vahidov, R. & Elrod, R. (1999). Incorporating Critique and Argumentation in DSS. Decision Support Systems, vol. 26, 249–258. Elsevier.

    Article  Google Scholar 

  • Verheij, B. (1996). Rules, Reasons and Arguments: Formal Studies of Argumentation and Defeat. Ph.D. Thesis, Maastricht University, Maastricht (Netherlands).

    Google Scholar 

  • Verheij, B. (1998). Argue! An Implemented System for Fomputer-Mediated Defeasible Argumentation. In Proceedings of the Tenth Netherlands/Belgium Conference on Artificial Intelligence, 57-66. CWI: Amsterdam.

  • Vreeswijk, G. A. W. (1995). IACAS: An Implementation of Chisholm's Principles of Knowledge. Proceedings of the Second Dutch/German Workshop on Nonmonotonic Reasoning, 225-234. Delft University of Technology.

  • Vreeswijk, G. A.W. (1997). Abstract Argumentation Systems. Artificial Intelligence 90: 225–279.

    Article  MATH  MathSciNet  Google Scholar 

  • Walton, D. (1996). Argument Structure: A Pragmatic Theory. University of Toronto Press.

  • Walton, D. N. & Krabbe, E. C. W. (1995). Commitment in Dialogue: Basic Concepts of Interpersonal Reasoning. State Unviersity of New York Press: Albany N.Y.

    Google Scholar 

  • Wick,M. R. & Thompson,W. B. (1992). Reconstructive Expert System Explanation. Artificial Intelligence 54.

  • Wick, M. R., Dutta, P., Wineinger, T. & Conner, J. (1995). Reconstructive Explanation: A Case Study in Integral Calculus. Expert Systems with Applications 8: 463–473.

    Article  Google Scholar 

  • Wu, D. (1991). Active Acquisition of User Models: Implications for Decision-Theoretic Dialog Planning and Plan Recognition. User Model and User Adapted Interaction 1: 149–172.

    Article  Google Scholar 

  • Ye, L. R. (1995). The Value of Explanation in Expert Systems for Auditing: An Experimental Investigation. Expert Systems with Applications 9: 543–556.

    Article  Google Scholar 

  • Ye, L. R. & Johnson, P. E. (1995): The Impact of Explanation Facilities on User Acceptance of Expert System Advices. MIS Quarterly (June): Management Information Systems, 157-172.

  • Yearwood, J. & Stranieri, A. (1999). The Integration of Retrieval, Reasoning and Drafting for Refugee Law: A Third Generation Legal Knowledge Based System. In Proceedings of the Seventh International Conference on Artificial Intelligence and Law: ICAIL'99, 117-137. ACM Press.

  • Yu, B. & Singh M. P. (2000). A Social Mechanism of Reputation Management in Electronic Communities. In Klusch, M. & Kerschberg, L. (eds.) Cooperative Information Agents IV: The Future of Information Agents in Cyberspace, 154-165. Springer Verlag.

  • Zanella,M. & Lamperti, G. (1999). Justification Dimensions for Complex Computer Systems. Proceedings of the World Multiconference on Systems, Cybernetics and Informatics, vol. 8, 317–324. Orlando, Florida, June-August.

    Google Scholar 

Download references

Authors
  1. B. Moulin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Irandoust
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Bélanger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Desbordes
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moulin, B., Irandoust, H., Bélanger, M. et al. Explanation and Argumentation Capabilities:Towards the Creation of More Persuasive Agents. Artificial Intelligence Review 17, 169–222 (2002). https://doi.org/10.1023/A:1015023512975

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1015023512975

Keywords

Search

Navigation