Title: Uncertain Knowledge Representation and Communicative Behavior
       in Coordinated Defense

Author: Sanguk Noh and Piotr J. Gmytrasiewicz

Address: Department of Computer Science and Engineering
         University of Texas at Arlington
         Arlington, TX 76019, Box 19015
         noh@cse.uta.edu, piotr@cse.uta.edu

Office: (817)272-3399, 272-3334 
Fax: (817)272-3784
 

Abstract:

This  paper reports  on  results  we obtained on  communication  among
artificial and human agents   interacting in a simulated air   defense
domain.  In our research, we postulate  that the artificial agents use
a decision-theoretic   method to  select  optimal  communicative acts,
given the   characteristics of  the particular situation.    Thus, the
agents  we implemented compute  the    expected utilities of   various
alternative communicative acts, and execute  the best one.  The agents
use a probabilistic  frame-based knowledge formalism to  represent the
uncertain information  they have about the domain  and about the other
agents present.  We build on our earlier work  that uses the Recursive
Modeling  Method  (RMM) for coordination,  and   apply RMM to rational
communication  in  an  anti-air   defense   domain.  In this   domain,
distributed units  coordinate  and communicate to  defend  a specified
territory   from a number  of   attacking  missiles.  We  measure  the
benefits of rational communication  by showing the improvement in  the
quality of interactions the communication results in.  We show how the
benefit of rational  communication measured after  the interactions is
related to the expected utilities of best messages computed before the
interaction   takes  place.    Further,  we compare    our results  to
improvement due to communication  achieved by human subjects under the
same circumstances.


Acknowledgements: 

This is  an extended and  revised version   of a  paper which will  be
presented in  Proceedings of the   Third  International Conference  on
Autonomous  Agents.   This work  has been supported   by the Office of
Naval Research Artificial Intelligence  Program under  contract N00014
-95-1-0775,  and  by the National     Science Foundation CAREER  award
IRI-9702132.