Intelligent Automated Agents for Flight Training Simulators

Training in ight simulators will be more eeective if the agents involved in the simulation behave realistically. Accomplishing this requires that the automated agents be under autonomous, intelligent control. We are using the Soar cognitive architecture to implement intelligent agents that behave as much like humans as possible. In order to approximate human behavior, the agents must integrate planning and reaction in real time, adapt to new and unexpected situations , learn with experience, and exhibit the cognitive limitations and strengths of humans. This paper describes two simple tactical ight scenarios and the knowledge required for an agent to complete them. In addition, the paper describes an implemented agent model that performs in limited tactical scenarios on three diierent ight sim-ulators. The goal of this research is to construct intelligent, automated agents for ight sim-ulators that are used to train navy pilots in ight tactics. When pilots train in tactical simulations, they learn to react to (and reason about) the behaviors of the other agents (friendly and enemy forces) in the training scenario. Thus, it is important that these agents behave as realistically as possible. Standard automated and semi-automated agents can provide this to a limited extent, but trainees can quickly recognize automated agents and take advantages of known weaknesses in their behavior. To provide a more realistic training situation , automated agents should be indistinguishable from other human pilots taking part in the simulation. To construct such intelligent, automated agents, we have applied techniques from the elds of artiicial intelligence and cog-nitive science. The agents are implemented within the Soar system, a state-of-the-art, integrated cognitive architecture (Rosen-bloom et al., 1991). These agents incorporate knowledge gleaned from interviews with experts in ight tactics and analysis of the tactical domain. Soar is a promising candidate for developing agents that behave like humans. Flexible and adaptive behavior is one of Soar's primary strengths, and Soar's learning mechanism provides it with the capability of improving its performance with experience. In addition, Soar allows the smooth integration of planning and reaction in decision making (Pearson