Skip to main content
SpringerLink
Log in

Context-Centric Needs Anticipation Using Information Needs Graphs

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Effective agent teamwork requires information exchange to be conducted in a proactive, selective, and intelligent way. In the field of distributed artificial intelligence, there has been increasing number of research focusing on need-driven proactive communication, both theoretically and practically. Among these works, CAST has realized a team-oriented agent architecture where agents, based on a computational shared mental model, are able to anticipate teammates' information needs and proactively deliver relevant information to the needers in a timely manner. However, the first implementation of CAST takes little consideration of the dynamics of the anticipated information needs, which can change in various ways as the context develops. In this paper we describe a novel mechanism for organizing and managing the “context” of information needs. This allows agents to dynamically activate and deactivate information needs progressively. It has been shown that the two-level context-centric approach can enhance team performance considerably.

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

  1. J. Grunig, “Information and decision making in economic development,” Journalism Quarterly, pp. 565–575, 1969.

  2. J. Larson and C. Christensen, “Groups a problem-solving units: Toward a new meaning of social cognition,” British Journal of Social Psychology, pp. 5–30, 1993.

  3. J. Crant, “Proactive behavior in organization,” Journal of management, vol. 26, no. 3, pp. 435–462, 2000.

    Article  Google Scholar 

  4. T. Dickinson and R. McIntyre, “A conceptual framework for teamwork measurement,” in Team Performance Assessment and Measurement: Theory, Methods and Applications, edited by M. T. Brannick, E. Salas, and C. Prince, pp. 19–44, 1997.

  5. R. McIntyre and E. Salas, “Measuring and managing for team performance: Emerging principles from complex environments,” in Team Effectiveness and Decision Making in Organizations, edited by R. Guzzo and E. Salas, San Francisco: Jossey-Bass, pp. 149–203, 1995.

  6. K.A. Smith-Jentsch, J.H. Johnson, and S.C. Payne, “Measuring team-related expertise in complex environments,” in Making Decisions Under Stress: Implications for Individual and Team Training, edited by J.A. Cannon-Bowers and E. Salas, pp. 61–87, 2000.

  7. J. Yen, J. Yin, T. Ioerger, M. Miller, D. Xu, and R. Volz, “CAST: Collaborative agents for simulating teamworks,” in Proceedings of IJCAI'2001, 2001, pp. 1135–1142.

  8. P. Stone and M. Veloso, “Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork,” AI, vol. 110, pp. 241–273, 1999.

    Google Scholar 

  9. B.J. Clement and A.C. Barrett, “Continual coordination through shared activities,” in Proceedings of the Second International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'03), 2003.

  10. M. Tambe, “Towards flexible teamwork,” Journal of Artificial Intelligence Research, vol. 7, pp. 83–124, 1997.

    Google Scholar 

  11. P.R. Cohen, H.J. Levesque, and I.A. Smith, “On team formation,” Contemporary Action Theory, 1997.

  12. B. Grosz and S. Kraus, “The evolution of sharedplans,” Found. and Theories of Rational Agencies, pp. 227–262, 1999.

  13. J.A. Cannon-Bowers, E. Salas, and S. A. Converse, “Shared mental models in expert team decision making,” in Individual and Group Decision Making, 1993, pp. 221–246.

  14. J. Yen, X. Fan, and R.A. Volz, “Information needs in agent teamwork,” Web Intelligence and Agent Systems: An International Journal, no. 4, pp. 231–247, 2004.

  15. X. Fan, R. Wang, B. Sun, S. Sun, and J. Yen, “Multi-agent information dependence,” in Proceedings of the 2005 IEEE International conference on Integration of Knowledge Intensive Multi-Agent Systems, 2005, pp. 41–46.

  16. M. Williamson, K.S. Decker, and K. Sycara, “Unified information and control flow in hierarchical task networks,” in Proceedings of the AAAI-96 Workshop on Theories of Planning, Action, and Control, 1996.

  17. J.S. Sichman, R. Conte, C. Castelfranchi, and Y. Demazeau, “Social reasoning mechanism based on dependence networks,” in Proceedings of the 11th European Conference on Artificial Intelligence, edited by A.G. Cohn, 1994.

  18. J. S. Sichman and R. Conte, “Multi-agent dependence by dependence graphs,” in AAMAS 2002, 2002, pp. 483–490.

  19. J. Yin, M.S. Miller, T.R. Ioerger, J. Yen, and R.A. Volz, “A knowledge-based approach for designing intelligent team training systems,” in Proc. of the 4th International Conference on Autonomous Agents, 2000, pp. 427–434.

  20. D. Alberts, J. Garstka, and S.F., “Network centric warfare, ccrp.”

  21. J. Yen, X. Fan, S. Sun, T. Hanratty, and J. Dumer, “Agents with shared mental models for enhancing team decision-makings,” Decision Support Systems, p. (in press), 2005.

  22. X. Fan, J. Yen, M. Miller, and R.A. Volz, “The semantics of MALLET—an agent teamwork encoding language,” in Declarative Agent Languages and Technologies II: Second International Workshop, DALT 2004, edited by J. Leite, A. Omicini, and P. Torroni, Springer-Verlag, Berlin, 2005, vol. 3476, pp. 69–91.

  23. B. Grosz and S. Kraus, “Collaborative plans for complex group actions,” Artificial Intelligence, vol. 86, pp. 269–358, 1996.

    Article  MathSciNet  Google Scholar 

  24. E. Davis, “Knowledge preconditions for plans,” Journal of Logic and Computation, vol. 4, no. 5, pp. 721–766, 1994.

    MATH  MathSciNet  Google Scholar 

  25. P.R. Cohen and H. J. Levesque, “Teamwork,” Nous, vol. 25, no. 4, pp. 487–512, 1991.

    Google Scholar 

  26. K.E. Korf, “Depth-first iterative deepening: An optimal admissible tree search,” Artificial Intelligence, vol. 27, no. 1, pp. 97–109, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  27. K. Decker, “TAEMS: A framework for environment centered analysis & design of coordination mechanisms,” in Foundations of Distributed Artificial Intelligence, Chapter 16, edited by G. O'Hare and N. Jennings, Wiley Inter-Science, January 1996, pp. 429–448.

  28. J. Giampapa and K. Sycara, “Team-oriented agent coordination in the RETSINA multi-agent system,” in Tech. Report CMU-RI-TR-02-34, Robotics Institute, Carnegie Mellon University, 2002.

  29. K. S. Decker and V. R. Lesser, “Quantitative modeling of complex computational task environments,” in Proceedings of the 11th National Conference on Artificial Intelligence, 1993.

  30. T. Wagner, V. Guralnik, and J. Phelps, “A key-based coordination algorithm for dynamic readiness and repair service coordination,” in Proceedings of the 2nd International Conference on Autonomous Agents and MAS, 2003.

  31. J. Graham, K. Decker, and M. Mersic, “DECAF—a flexible multi agent system architecture,” Autonomous Agents and Multi-Agent Systems, vol. 7, nos. 1–2, pp. 7–27, 2003.

    Google Scholar 

  32. K. Sycara, K. Decker, A. Pannu, M. Williamson, and D. Zeng, “Distributed intelligent agents,” IEEE Expert, Intelligent Systems and their Applications, vol. 11, no. 6, pp. 36–45, 1996.

    Google Scholar 

  33. F. Giunchiglia, “Contextual reasoning,” Epistemologia, special issue on I Linguaggi e le Macchine, vol. XVI, pp. 345–364, 1993.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, 16802

    Xiaocong Fan, Rui Wang, Shuang Sun & John Yen

  2. Department of Computer Science, Texas A&M University, College Station, TX, 77843

    Richard A. Volz

Authors
  1. Xiaocong Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John Yen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Richard A. Volz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaocong Fan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fan, X., Wang, R., Sun, S. et al. Context-Centric Needs Anticipation Using Information Needs Graphs. Appl Intell 24, 75–89 (2006). https://doi.org/10.1007/s10489-006-6931-2

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-006-6931-2

Keywords

Search

Navigation