An Information Fusion Game Component

The military domain is one of the purest possible game arenas, and history is full of examples of how mistakes in handling uncertainty about the opponent have had large consequences. For an entertaining selection, see, e.g., [16]. Commanders on each side have resources at their disposal, and want to use them to achieve their, mostly opposing, goals. In the network centric warfare [1, 2] era, they are aided by large amounts of information about the opponent from sensors and historical data bases, and about the status of their own resources from their own information technology infrastructure. In recently proposed infostructures for command and control (C2) [12], decision support tools play a prominent role. These tools seldom include game-theoretic means. Gaming is, however, a prominent feature of military training and the regulated decision processes often assign the roles of red and blue players to staff officers in manual planning activities [52]. Gaming is thus a conceptual part of the planning process in many organizations. It must be emphasized, however, that there are significant differences between practice and theory in application of such regulations. It has, for example, been shown in studies that the Swedish defense organization practices a more naturalistic decision-making process than the recommended one [51]. A pure naturalistic planning process relies more on unobservable mental capabilities of decision-makers than on rational analyses of alternative moves and their utilities [28]. The most common way to deal with uncertainty is, however, to make an assumption–and to forget that it was made. These observations have been the starting point for introducing a less complex planning model–PUT (Planning Under Time-pressure)–in the Swedish defense organization. PUT is based on analyses of a few opponent alternatives and incremental improvement of one’s own plans [51]. It thus has potential for the use of gaming tools, provided they are realized in a way that supports subjective improvement of decision situations and decision quality [3]. Data fusion aims at providing situation awareness at different levels for a commander. The JDL model [47, 56] has been proposed for structuring the fusion process into five levels where the third level consists of higher level prediction of possible future problems and possibilities. We believe that the problem of predicting the future in a C2 context comes in two variations that differ in complexity and dependencies: the problem of capturing all aspects of a complex situation, and the problem of strategic dependence in a multi-agent encounter. Considering the former problem, the influence diagram is a well-established and appropriate modeling technique for modeling everything that is not dependent on our own or the opponents’ actions, for example doctrine and terrain. Efforts in this direction have been proposed; see for example [50] for a discussion about doctrine modeling using dynamic Bayesian networks [40].