Abstract
In collaborative planning dialogues, the agents have different beliefs about the domain and about each other; thus, it is inevitable that conflicts arise during the planning process. In this paper, we present a plan-based model for response generation during collaborative planning, based on a recursive Propose-Evaluate-Modify framework for modeling collaboration. We focus on identifying strategies for content selection when 1) the system initiates information-sharing to gather further information in order to make an informed decision about whether to accept a proposal from the user, and 2) the system initiates collaborative negotiation to negotiate with the user to resolve a detected conflict in the user's proposal. When our model determines that information-sharing should be pursued, it selects a focus of information-sharing from among multiple uncertainties that might be addressed, chooses an appropriate information-sharing strategy, and formulates a response that initiates an information-sharing subdialogue. When our model determines that conflicts must be resolved, it selects the most effective conflicts to address in resolving disagreement about the user's proposal, identifies appropriate justification for the system's claims, and formulates a response that initiates a negotiation subdialogue.
- Allen, James F. 1979. A Plan-Based Approach to Speech Act Recognition. Ph.D. thesis, University of Toronto. Google Scholar
- Allen, James. 1991. Discourse structure in the TRAINS project. In Darpa Speech and Natural Language Workshop. Google Scholar
- Birnbaum, Lawrence, Margot Flowers, and Rod McGuire. 1980. Towards an AI model of argumentation. In Proceedings of the National Conference on Artificial Intelligence, pages 313--315.Google Scholar
- Cawsey, Alison. 1990. Generating explanatory discourse. In R. Dale, C. Mellish, and M. Zock, editors, Current Research in Natural Language Generation. Academic Press, chapter 4, pages 75--101. Google Scholar
- Cawsey, Alison, Julia Galliers, Brian Logan, Steven Reece, and Karen Sparck Jones. 1993. Revising beliefs and intentions: A unified framework for agent interaction. In The Ninth Biennial Conference of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, pages 130--139.Google Scholar
- Chu-Carroll, Jennifer. 1996. A Plan-Based Model for Response Generation in Collaborative Consultation Dialogues. Ph.D. thesis, University of Delaware. Also available as Department of Computer and Information Sciences, Laboratories for NLP/AI/HCI, Technical Report 97--01. Google Scholar
- Chu-Carroll, Jennifer and Sandra Carberry. 1994. A plan-based model for response generation in collaborative task-oriented dialogues. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 799--805. Google Scholar
- Chu-Carroll, Jennifer and Sandra Carberry. 1995a. Communication for conflict resolution in multi-agent collaborative planning. In Proceedings of the First International Conference on Multiagent Systems, pages 49--56.Google Scholar
- Chu-Carroll, Jennifer and Sandra Carberry 1995b. Generating information-sharing subdialogues in expert-user consultation. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 1243--1250. Google Scholar
- Chu-Carroll, Jennifer and Sandra Carberry. 1995c. Response generation in collaborative negotiation. In Proceedings of the 33rd Annual Meeting, pages 136--143. Association for Computational Linguistics. Google Scholar
- Chu-Carroll, Jennifer and Sandra Carberry. 1996. Conflict detection and resolution in collaborative planning. In Intelligent Agents: Agent Theories, Architectures, and Languages, Volume II, Springer-Verlag Lecture Notes. Springer-Verlag, pages 111--126.Google Scholar
- Chu-Carroll, Jennifer and Sandra Carberry. In press. Conflict resolution in collaborative planning dialogues. International Journal of Human-Computer Studies. Google Scholar
- Clark, Herbert and Edward Schaefer. 1989. Contributing to Discourse. Cognitive Science, pages 259--294.Google Scholar
- Clark, Herbert and Deanna Wilkes-Gibbs. 1990. Referring as a Collaborative Process. In Philip Cohen, Jerry Morgan, and Martha Pollack, editors, Intentions in Communication. MIT Press, Cambridge, MA, pages 463--493.Google Scholar
- Cohen, Paul R. 1985. Heuristic Reasoning about Uncertainty: An Artificial Intelligence Approach. Pitman Publishing Company. Google Scholar
- Cohen, Philip R. and C. Raymond Perrault. 1979. Elements of a plan-based theory of speech acts. Cognitive Science, 3:177--212.Google Scholar
- Cohen, Robin. 1987. Analyzing the structure of argumentative discourse. Computational Linguistics, 13(1--2):11--24. Google Scholar
- Edmonds, Philip G. 1994. Collaboration on reference to objects that are not mutually known. In Proceedings of the 15th International Conference on Computational Linguistics, pages 1118--1122. Google Scholar
- Flowers, Margot and Michael Dyer. 1984. Really arguing with your computer. In Proceedings of the National Computer Conference, pages 653--659.Google Scholar
- Galliers, Julia R. 1992. Autonomous belief revision and communication. In Gardenfors, editor, Belief Revision. Cambridge University Press.Google Scholar
- Grice, H. Paul. 1975. Logic and conversation. In Peter Cole and Jerry L. Morgan, editors, Syntax and Semantics 3: Speech Acts. Academic Press, Inc., New York, pages 41--58.Google Scholar
- Gross, Derek, James F. Allen, and David R. Traum. 1993. The TRAINS 91 dialogues. Technical Report TN92-1, Department of Computer Science, University of Rochester. Google Scholar
- Grosz, Barbara and Sarit Kraus. 1996. Collaborative plans for complex group actions. Artificial Intelligence, 86(2):269--357. Google Scholar
- Grosz, Barbara J. and Candace L. Sidner. 1990. Plans for discourse. In Cohen, Morgan, and Pollack, editors, Intentions in Communication. MIT Press, chapter 20, pages 417--444.Google Scholar
- Hample, Dale. 1985. Refinements on the cognitive model of argument: Concreteness, involvement and group scores. The Western Journal of Speech Communication, 49:267--285.Google Scholar
- Harry Gross Transcripts. 1982. Transcripts derived from tapes of the radio talk show Harry Gross: Speaking of your money. Provided by the Dept. of Computer Science at the University of Pennsylvania.Google Scholar
- Heeman, Peter A. and Graeme Hirst. 1995. Collaborating on referring expressions. Computational Linguistics, 21(3):351--382. Google Scholar
- Lambert, Lynn and Sandra Carberry. 1991. A tripartite plan-based model of dialogue. In Proceedings of the 29th Annual Meeting, pages 47--54. Association for Computational Linguistics. Google Scholar
- Lambert, Lynn and Sandra Carberry. 1992. Modeling negotiation dialogues. In Proceedings of the 30th Annual Meeting, pages 193--200. Association for Computational Linguistics. Google Scholar
- Lochbaum, Karen E. 1994. Using Collaborative Plans to Model the Intentional Structure of Discourse. Ph.D. thesis, Harvard University. Google Scholar
- Lochbaum, Karen. 1995. The use of knowledge preconditions in language processing. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1260--1266. Google Scholar
- Logan, Brian, Steven Reece, Alison Cawsey, Julia Galliers, and Karen Sparck Jones. 1994. Belief revision and dialogue management in information retrieval. Technical Report 339, University of Cambridge, Computer Laboratory.Google Scholar
- Luchok, Joseph A. and James C. McCroskey. 1978. The effect of quality of evidence on attitude change and source credibility. The Southern Speech Communication Journal, 43:371--383.Google Scholar
- McCoy, Kathleen F. 1988. Reasoning on a highlighted user model to respond to misconceptions. Computational Linguistics, 14(3):52--63. Google Scholar
- McKeown, Kathleen R. 1985. Text Generation: Using Discourse Strategies and Focus Constraints to Generate Natural Language Text. Cambridge University Press. Google Scholar
- McKeown, Kathleen R., Myron Wish, and Kevin Matthews. 1985. Tailoring explanations for the user. In Proceedings of the 9th International Joint Conference on Artificial Intelligence, pages 794--798, Los Angeles, CA.Google Scholar
- Moore, Johanna and Cecile Paris. 1993. Planning text for advisory dialogues: Capturing intentional and rhetorical information. Computational Linguistics, 19(4):651--695. Google Scholar
- Morley, Donald D. 1987. Subjective message constructs: A theory of persuasion. Communication Monographs, 54:183--203.Google Scholar
- Paris, Cécile L. 1988. Tailoring object descriptions to a user's level of expertise. Computational Linguistics, 14(3):64--78. Google Scholar
- Petty, Richard E. and John T. Cacioppo. 1984. The effects of involvement on responses to argument quantity and quality: Central and peripheral routes to persuasion. Journal of Personality and Social Psychology, 46(1):69--81.Google Scholar
- Pollack, Martha E. 1986. A model of plan inference that distinguishes between the beliefs of actors and observers. In Proceedings of the 24th Annual Meeting, pages 207--214. Association for Computational Linguistics. Google Scholar
- Quilici, Alex. 1992. Arguing about planning alternatives. In Proceedings of the 14th International Conference on Computational Linguistics, pages 906--910. Google Scholar
- Ramshaw, Lance A. 1991. A Three-Level Model for Plan Exploration. In Proceedings of the 29th Annual Meeting, pages 36--46, Berkeley, CA. Association for Computational Linguistics. Google Scholar
- Raskutti, Bhavani and Ingrid Zukerman. 1993. Eliciting additional information during cooperative consultations. In Proceedings of the 15th Annual Meeting of the Cognitive Science Society.Google Scholar
- Raskutti, Bhavani and Ingrid Zukerman. 1994. Query and response generation during information-seeking interactions. In Proceedings of the 4th International Conference on User Modeling, pages 25--30.Google Scholar
- Reichman, Rachel. 1981. Modeling informal debates. In Proceedings of the 7th International Joint Conference on Artificial Intelligence, pages 19--24.Google Scholar
- Reinard, John C. 1988. The empirical study of the persuasive effects of evidence, the status after fifty years of research. Human Communication Research, 15(1):3--59.Google Scholar
- Reynolds, Rodney A. and Michael Burgoon. 1983. Belief processing, reasoning, and evidence. In Bostrom, editor, Communication Yearbook 7. Sage Publications, chapter 4, pages 83--104.Google Scholar
- Rosé, Carolyn P., Barbara Di Eugenio, Lori S. Levin, and Carol Van Ess-Dykema. 1995. Discourse processing of dialogues with multiple threads. In Proceedings of the 33rd Annual Meeting, pages 31--38. Association for Computational Linguistics. Google Scholar
- Sarner, Margaret H. and Sandra Carberry. 1990. Tailoring explanations using a multifaceted user model. In Proceedings of the Second International Workshop on User Models, Honolulu, Hawaii, March.Google Scholar
- Sidner, Candace L. 1992. Using discourse to negotiate in collaborative activity: An artificial language. In AAAI-92 Workshop: Cooperation Among Heterogeneous Intelligent Systems, pages 121--128.Google Scholar
- Sidner, Candace L. 1994. An artificial discourse language for collaborative negotiation. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 814--819. Google Scholar
- SRI Transcripts. 1992. Transcripts derived from audiotape conversations made at SRI International, Menlo Park, CA. Prepared by Jacqueline Kowtko under the direction of Patti Price.Google Scholar
- Sycara, Katia. 1989. Argumentation: Planning other agents' plans. In Proceedings of the 11th International Joint Conference on Artificial Intelligence, pages 517--523.Google Scholar
- Traum, David R. 1994. A Computational Theory of Grounding in Natural Language Conversation. Ph.D. thesis, University of Rochester. Google Scholar
- Udel Transcripts. 1995. Transcripts derived from audiotape conversations made at the University of Delaware. Recorded and transcribed by Rachel Sacher.Google Scholar
- van Beek, Peter, Robin Cohen, and Ken Schmidt. 1993. From plan critiquing to clarification dialogue for cooperative response generation. Computational Intelligence, 9(2):132--154.Google Scholar
- Walker, Marilyn A. 1992. Redundancy in collaborative dialogue. In Proceedings of the 15th International Conference on Computational Linguistics, pages 345--351. Google Scholar
- Walker, Marilyn. 1996a. Inferring acceptance and rejection in dialog by default reules of inference. Language and Speech, 39(2--3):265--304.Google Scholar
- Walker, Marilyn A. 1996b. The effect of resource limits and task complexity on collaborative planning in dialogue. Artificial Intelligence, 85:181--243. Google Scholar
- Walker, Marilyn and Steve Whittaker. 1990. Mixed initiative in dialogue: An investigation into discourse segmentation. In Proceedings of the 28th Annual Meeting, pages 70--78. Association for Computational Linguistics. Google Scholar
- Whittaker, Steve and Phil Stenton. 1988. Cues and control in expert-client dialogues. In Proceedings of the 26th Annual Meeting, pages 123--130, Association for Computational Linguistics. Google Scholar
- Wyer, Jr., Robert S. 1970. Information redundancy, inconsistency, and novelty and their role in impression formation. Journal of Experimental Social Psychology, 6:111--127.Google Scholar
- Young, R. Michael, Johanna D. Moore, and Martha E. Pollack. 1994. Towards a principled representation of discourse plans. In Proceedings of the Sixteenth Annual Meeting of the Cognitive Science Society, pages 946--951.Google Scholar
- Zukerman, Ingrid and Richard McConachy. 1993. Generating concise discourse that addresses a user's inferences. In Proceedings of the 1993 International Joint Conference on Artificial Intelligence.Google Scholar
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