Earlier versions of MIDAS (e.g., as described by Banda et al., 1991) relied primarily on a pattern-matching approach to triggering reactive behavior, based on the internal perceptions of outside world states. Recent work described by Smith et al. (1996) and Shively (1997) is directed at developing a more global assessment of the situation (to be contrasted with a set of perceived states), somewhat along the lines of Reece (1996). Again, the focus is on assessing the situation in terms of the external entities: self, friendly, and threat and their locations. A four-stage assessment process (detection, recognition, identification, and comprehension) yields a list of entities and a numeric value associated with how well each entity assessment matches the actual situation. A weighted calculation of overall situation awareness is made across entities and is used to drive information-seeking behavior: low situation awareness drives the attention allocator to seek more situation-relevant information to improve overall awareness. Currently, the situation awareness model in MIDAS does not drive the decision making process (except indirectly through its influence on information-seeking behavior), so that MIDAS remains essentially event-rather than situation-driven. The current structure does not, however, appear to preclude development along these lines.
As discussed in Chapter 2, considerable effort has been devoted to applying the Soar cognitive architecture (Laird et al., 1987; Laird and Rosenbloom, 1990) to the modeling of human behavior in the tactical air domain. Initial efforts led to a limited-scope demonstration of feasibility, fixed-wing attack (FWA)-Soar (Tambe et al., 1995); 3 more recent efforts showed how Soar-intelligent forces (Soar-IFOR) could participate in the synthetic theater of war (STOW)-97 large-scale warfighting simulation (Laird, 1996).
As described in Chapter 3, much of the Soar development effort has been focused on implementing a mechanism for goal-driven behavior, in which high-level goals are successively decomposed into low-level actions. The emphasis has been on finding a feasible action sequence taking the Soar entity from the current situation to the desired goal (or end situation); less emphasis has been placed on identifying the current situation (i.e., situation awareness). However, as Tambe et al. (1995) note, identifying the situation is critical in dynamic environments, in which either Soar agents or others continually change the environment, and hence the situation. Thus, more recent emphasis in Soar has been placed on modeling the agent's dynamic perception of the environment and assessment of the situation.
In contrast with conventional "situated action agents" (e.g., Agre and Chapman, 1987), in which external system states are used to drive reflexive agents (rule-based or otherwise) to generate agent actions, Soar attempts to model the