Performance Evaluation of an Unmanned Airborne Vehicle Multi-Agent System

Consider an unmanned airborne vehicle (UAV) multi-agent system. A UAV agent is aware of the destination or goal to be achieved, its own quantitative or qualitative, of encountering enemy defenses in the region. Each agent plans its moves in order to maximize the chances of reaching the target before the required task completion time (see Fig. 1). The plans are developed based on the negotiations between different UAVs in the region with the overall goal in mind. The model is actually motivated by another large research project related to multi-agent systems. The information about enemy defenses can be communicated between UAVs and they can negotiate about the paths to be taken based on their resources, such as fuel, load, available time to complete the task and the information about the threat. In this system, we can also model the behavior of enemy defenses as independent agents, with known or unknown strategies. Each enemy defense site or gun has a probability of destroying a UAV in a neighborhood. The UAVs have an expectation of the location of enemy defenses, which is further refined as more information becomes available during the flight or from other UAVs. To successfully achieve the goal with a high probability, the UAVs need to select a good plan based on coordination and negotiation between each other. One paper dealing with this model is Atkins et al. (Atkins et al., 1996), which considered an agent capable of safe, fully-automated aircraft flight control from takeoff through landing. To build and execute plans that yield a high probability of successfully reaching the specified goals, the authors used state probabilities to guide a planner along highly-probable goal paths instead of low-probability states. Some probabilistic planning algorithms are also developed by the other researchers. Kushmerick et al. (Kushmerick et al., 1994) concentrate on probabilistic properties of actions that may be controlled by the agent, not external events. Events can occur over time without explicit provocation by the agent, and are generally less predictable than state changes due to actions. Atkins et al. (Atkins et al., 1996) presented a method by which local state probabilities are estimated from action delays and temporally-dependent event probabilities, then used to select highly probable goal paths and remove improbable states. The authors implemented these algorithms in the Cooperative Intelligent Real-time Control Architecture (CIRCA). CIRCA combines an AI planner, scheduler performance for controlling complex real-world systems (Musliner et al., 1995). CIRCA's planner is based on the philosophy that building a plan to handle all world