Towards a Taxonomy of Task-Oriented Domains of Dialogue

  • Tânia MarquesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9387)


To deal with a broad spectrum of domains, intelligent agents have to generate their own task-oriented dialogue that stems from the need to interact with another agent when solving their own individual task. Most work created to date has either been focused on the task or on the dialogue, but not on both. A taxonomy that describes how the characteristics of a domain determine the types of dialogue needed would be useful, both for understanding how to create agents that are more adaptable to different domains, and also to facilitate reusing previous work. In this paper, we present a number of dimensions that could be included in such a taxonomy, and illustrate how they could be used to determine the nature of dialogue needed in a particular type of domain.


Agent communication Taxonomy Task-oriented dialogue 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Morgan Kaufmann, San Francisco (2004)zbMATHGoogle Scholar
  2. 2.
    Meneguzzi, F., de Silva, L.: Planning in BDI agents: a survey of the integration of planning algorithms and agent reasoning. The Knowledge Engineering Review 30, 1–44 (2015)CrossRefGoogle Scholar
  3. 3.
    Rahwan, I., Simari, G.R. (eds.).: Argumentation in artificial intelligence, vol. 47. Springer (2009)Google Scholar
  4. 4.
    Jennings, N.R., Faratin, P., et al.: Automated negotiation: prospects, methods and challenges. Group Decision and Negotiation 10(2), 199–215 (2001)CrossRefGoogle Scholar
  5. 5.
    Nicoletta, F., Viganó, F.: Colombetti. M.: Agent communication and artificial institutions. Autonomous Agents and Multi-Agent Systems 14(2), 121–142 (2007)CrossRefGoogle Scholar
  6. 6.
    Goldman, C.V., Zilberstein, S.: Optimizing information exchange in cooperative multi-agent systems. In: the 2nd international joint conference on Autonomous agents and multiagent system, pp. 137–144. ACM (2003)Google Scholar
  7. 7.
    Austin, J.L.: How To Do Things With Words. In: Urmson, J.O., Sbisà, M. (eds.). Oxford, Oxford University Press (1975)Google Scholar
  8. 8.
    Searle, J.R.: A taxonomy of illocutionary acts. Language in Society 5(1–23), 344–369 (1975)Google Scholar
  9. 9.
    O’Brian, P.D., Nicol, R.C.: FIPA: Towards a Standard for Software Agents. BT Technology Journal 16(3), 51–59 (1998)CrossRefGoogle Scholar
  10. 10.
    Walton, D.N., Krabbe, E.C.W.: Commitment in Dialogue: Basic Concepts of Interpersonal Reasoning. State University of New York Press, SUNY Series in Logic and Language (1995)Google Scholar
  11. 11.
    Bernstein, D.S., Givan, R., et al.: The complexity of decentralized control of Markov decision processes. Mathematics of Operations Research 27(4), 819–840 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    López-Sánchez, M., Esteva, F., et al.: Map generation by cooperative low-cost robots in structured unknown environments. In: Autonomous Agents. Springer (1998)Google Scholar
  13. 13.
    Jackson, M.O., Simon, L.K. et al.: Communication and equilibrium in discontinuous games of incomplete information. Econometrica, pp. 1711–1740 (2002)Google Scholar
  14. 14.
    Asher, N., Lascarides, A.: Strategic Conversation. Semantics and Pragmatics 6(2), 1–62 (2013)Google Scholar
  15. 15.
    Gal, Y., Grosz, B. et al.: Colored Trails: a Formalism for Investigating Decision-Making in Strategic Environments. In: Workshop on Reasoning, Representation and Learning in Computer Games, pp. 25–30. AAAI Press, Menlo Park (2005)Google Scholar
  16. 16.
    Brafman, R.I., Domshlak, C.: From one to many: planning for loosely coupled multi-agent systems. In: The 8th International Conference on Automated Planning and Scheduling, pp. 28–35 (2008)Google Scholar
  17. 17.
    Doran, J.E., Franklin, S.R.J.N., et al.: On cooperation in multi-agent systems. The Knowledge Engineering Review 12(3), 309–314 (1997)CrossRefGoogle Scholar
  18. 18.
    Roth, M., Simmons, R., Veloso, M.: What to communicate? Execution-time decision in multi-agent POMDPs. In: Distributed Autonomous Robotic Systems 7, pp. 177–186. Springer (2006)Google Scholar
  19. 19.
    Genesereth, M.R., Ginsberg, M.L., Rosenschein, J.S.: Cooperation without communication, Heuristic Programming Project, Computer Science Department, pp. 51–57. Stanford University (1984)Google Scholar
  20. 20.
    Tan, M.: Multi-agent reinforcement learning: Independent vs. cooperative agents. In: The 10th international conference on machine learning, pp. 330–337 (1993)Google Scholar
  21. 21.
    Rosenschein, J.S., Zlotkin, G.: Rules of Encounter: Designing Conventions for Automated Negotiation Among Computers. MIT Press, Cambridge (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK

Personalised recommendations