A Logical View on Teamwork

  • Barbara Dunin-Kęplicz
  • Rineke Verbrugge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7010)


This chapter presents the non-dynamic part of a formal framework for teamwork in multi-agent systems. The framework consists of both a static part, defining collective motivational attitudes in such a way that the system developer can adapt them to the circumstances, and a dynamic part monitoring the changes in team attitudes during the course of cooperative problem solving (CPS).

In the first part of this chapter, the notion of collective intention in teams of agents is investigated. Starting from individual intentions, goals, and beliefs defining agents’ local attitudes, we arrive at an understanding of collective intention in cooperative teams as a rather strong concept: it implies that all members intend for all others to share that intention. This way a team is glued together by collective intention, and exists as long as this attitude holds, after which it may disintegrate.

Collective intentions are formalized in a multi-modal logical framework. Together with individual and common knowledge and/or belief, collective intention constitutes a basis for preparing a plan, reflected in the strongest attitude, i.e., in collective commitment, defined and investigated in the next part. Distinct versions of collective commitments that are applicable in various situations, differ with respect to the aspects of teamwork of which the agents involved are aware, and the kind of awareness present within a team. This way a kind of tuning mechanism is provided for the system developer to tune a version of collective commitment fitting the circumstances. Finally, a few exemplar versions of collective commitment resulting from instantiating the general tuning scheme are presented.


Modal Logic Multiagent System Common Belief Kripke Model Social Plan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Barbara Dunin-Kęplicz
    • 1
    • 2
  • Rineke Verbrugge
    • 3
  1. 1.Institute of InformaticsWarsaw UniversityWarsawPoland
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  3. 3.Department of Artificial Intelligence, Faculty of Mathematics and Natural SciencesUniversity of GroningenThe Netherlands

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