Effective agent collaboration through improved communication by means of contextual reasoning

This paper describes an extension to a context‐driven agent representation paradigm that facilitates modeling collaborative tactical behaviors for simulations of team games or military missions. Called collaborative context‐based reasoning, it emphasizes communication among the collaborating agents and carries it out by exchanging their currently active context when feasible. CCxBR is founded on the concepts defined in joint intention theory (JIT). The research described here presents an architecture that incorporates JIT in a contextual framework. The ability to facilitate communication among the collaborating agents by exchanging information about active contexts resembles the ability of humans to agree on a tactic in midstream and predict the behavior of their collaborators. This allows a CCxBR agent to invoke the actions involved in the tactic in the pursuit of a common goal. The paper describes several prototypes built to evaluate the CCxBR approach and the experiments executed to determine its effectiveness. The results of the experiments and the conclusions reached are discussed. © 2010 Wiley Periodicals, Inc.

[1]  Mat Buckland,et al.  Programming Game AI by Example , 2004 .

[2]  Brian S. Stensrud,et al.  Discovery of High-Level Behavior From Observation of Human Performance in a Strategic Game , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Nicholas R. Jennings,et al.  Controlling Cooperative Problem Solving in Industrial Multi-Agent Systems Using Joint Intentions , 1995, Artif. Intell..

[4]  Milind Tambe,et al.  Towards Flexible Teamwork , 1997, J. Artif. Intell. Res..

[5]  Boicho N. Kokinov,et al.  Context-Sensitivity of Human Memory: Episode Connectivity and Its Influence on Memory Reconstruction , 2007, CONTEXT.

[6]  Patrick Brézillon Task-Realization Models in Contextual Graphs , 2005, CONTEXT.

[7]  Winfried Lamersdorf,et al.  A flexible BDI architecture supporting extensibility , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[8]  John Yen,et al.  R-CAST: Integrating Team Intelligence for Human-Centered Teamwork , 2007, AAAI.

[9]  John McCarthy,et al.  Notes on Formalizing Context , 1993, IJCAI.

[10]  Roy M. Turner,et al.  Context-mediated behavior for intelligent agents , 1998, Int. J. Hum. Comput. Stud..

[11]  Eduardo Salas,et al.  Analyzing knowledge requirements in team tasks , 2000 .

[12]  Sarit Kraus,et al.  The Evolution of Sharedplans , 1999 .

[13]  Michael Wooldridge,et al.  The Belief-Desire-Intention Model of Agency , 1998, ATAL.

[14]  Ashwin Ram,et al.  Needles in a Haystack: Plan Recognition in Large Spatial Domains Involving Multiple Agents , 1998, AAAI/IAAI.

[15]  E. Salas,et al.  Shared mental models in expert team decision making. , 1993 .

[16]  Avelino J. Gonzalez,et al.  Context-based representation of intelligent behavior in training simulations , 1998 .

[17]  John Yen,et al.  A theoretical framework on proactive information exchange in agent teamwork , 2005, Artif. Intell..

[18]  Aaron F. Bobick,et al.  A Framework for Recognizing Multi-Agent Action from Visual Evidence , 1999, AAAI/IAAI.

[19]  Brian S. Stensrud,et al.  Context-Based Reasoning: A Revised Specification , 2004, FLAIRS.

[20]  Brian S. Stensrud,et al.  Formalizing context‐based reasoning: A modeling paradigm for representing tactical human behavior , 2008, Int. J. Intell. Syst..

[21]  Milind Tambe,et al.  Monitoring deployed agent teams , 2001, AGENTS '01.