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We study the evolution of distributed multiagent systems under uncertainty where the autonomous agents may cooperate with each other, and/or with supervisor/operator, in order to achieve the system's objective. The cooperation is facilitated by means of information sharing among the
autonomous agents and/or supervisor/operator, which has the purpose of improving the effectiveness of the autonomous agents. The evolution of cooperative systems is modeled using discrete-state, continuous-time Markov processes. To measure and quantify the degree of cooperation within such
systems, we introduce the concept of coefficient of cooperation, which is obtained by minimizing the Kullback-Leibler or 1-norm distances between nonstationary probability distributions. The presented techniques are illustrated on several different types of multiagent search systems.
Military Operations Research is the leading peer-reviewed journal publishing articles in the fields that describe operations research (OR) methodologies and theories used in key military applications.
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