The small unit tactical trainer (SUTT)2 (see Chapters 2 and 8) includes computer-controlled hostiles that behave as "smart" adversaries to evade and counterattack blue forces. Knowledge of the current situation by the computer-controlled hostiles is specified by a number of fairly low-level state variables defining "self" status: location, speed/direction of movement, posture, weapon state, and the like. No attempt is made to model this type of perception/assessment function; rather, the "true" individual combatant states are used directly. State variables defining the status of others are less extensive and focus primarily on detection/identification status and location relative to self, thus following the conventional military definition of situation awareness—positional knowledge of all players in the scenario at hand. Here, an attempt is made to model the perception of these states in accordance with the level 1 situation awareness process postulated by Endsley (1995). The net result is a list of entities, with attributes specifying the observer's knowledge of each (e.g., undetected, detected but unrecognized) and with parameters specifying the state of each (position, and possibly velocity). In the SUTT human behavior representation, situation awareness is modeled as primarily a low-level collection of "events" (identified and located entities), with no attempt made to assess or infer higher-level situations (e.g., "Is this group of individual entities part of an enemy squad?''). Thus actions or plans for actions are necessarily reflexive at a fairly low level (implemented as either rulebases or decision trees), with little abstraction or generalization involved. While this may be adequate for modeling a wide range of "battle drill" exercises in which highly choreographed offensive and defensive movements are triggered by relatively low-level events (e.g., detection of a threat at a particular location), it is unclear how such battle drills can be selected reliably without the specification of an adequate context afforded by higher-level situation awareness processes (e.g., "We're probably facing an entire platoon hiding behind the building"). Certainly it is unclear how more "inventive" situation-specific tactics can be formulated on the fly without an adequate situation assessment capability.
The man-machine integration design and analysis system (MIDAS) was developed by the National Aeronautics and Space Administration (NASA) and the U.S. Army to model pilot behavior, primarily in support of rotorcraft crew station design and procedural analyses (Banda et al., 1991; Smith et al., 1996; see also Chapters 3 and 8). As described earlier in Chapter 3, an agent-based operator model (comprising three basic modules for representing perceptual, cognitive, and motor processing) interacts with the proximal environment (displays, controls) and, in combination with the distal environment (e.g., own ship, other aircraft), results in observable behavioral activities (such as scanning, deciding, reaching, and communicating). Much effort has gone into developing environmental models, as well as perceptual and motor submodels. Work has also gone