Developers of tomorrow’s Command and Control centers are facing numerous problems related to the vast amount of available information obtained from various sources. On a lower level, huge amounts of uncertain reports from different sensors need to be fused into comprehensible information. On a higher level, representation and management of the aggregated information will be the main task, with the overall objective to provide reliable and comprehensible situation awareness to commanders. Hence, we consider prediction of future course of events being a necessary ingredient. Unfortunately, traditional agent modeling techniques do not capture gaming situations, i.e., situations where commanders make decisions based on other commanders’ reasoning about one’s own reasoning. To cope with this problem, we propose an architecture based on game theory for inference, coupled with traditional methods for uncertainty modeling. Applying an example, we show that our architecture could be used as a decision support tool, offering enhanced situation awareness in Command and Control. Finally, we wind up with a philosophical discussion regarding the ambiguities and the difficulties in interpreting the solution that game theory offers in the form of mixed strategy Nash equilibria.
The 15th Mini-EURO Conference: Managing Uncertainty in Decision Support Models (MUDSM 2004)