Higher levels of the data fusion process call for prediction and awareness of the development of a situation. Since the situations handled by command and control systems develop by actions performed by opposing agents, pure probabilistic or evidential techniques are not fully sufficient tools for prediction. Game-theoretic tools can give an improved appreciation of the real uncertainty in this prediction task, and also be a tool in the planning process. Based on a combination of graphical inference models and game theory, we propose a decision support tool architecture for command and control situation awareness enhancements.

This paper outlines a framework for command and control decision-making in multi-agent settings. Decision-makers represent beliefs over models incorporating other decision-makers and the state of the environment. When combined, the decision-makers’ equilibrium strategies of the game can be inserted into a representation of the state of the environment to achieve a joint probability distribution for the whole situation in the form of a Bayesian network representation.