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We present an overview of our cognitive system architecture (COSA) with applications in the multi-UAV flight guidance and mission management areas. Our work is based on a modified version of the Rasmussen scheme, which is an established model for human behaviour in ergonomics and cognitive systems. We belief that modeling in close analogy with categories of human behavior simplifies human-machine interaction as well as the knowledge engineering process. Our hybrid agent architecture is comprised of a low-level, reactive layer with prestored procedures and a goal-oriented, deliberative layer that enables inference and dynamic planning. The first, fully functional implementation of our architecture used production rules and the Soar interpreter, enhanced with syntax extensions such as type-safety and class-inheritance specific to our modeling approach. We then developed a specialized inference algorithm based on graph matching, which natively supports these extensions and resulted in performance improvements over the original Rete algorithm of Soar. A major weakness of our current implementation still lies in its static planning functionality which is realized by a means-ends plan library. We discuss a concept that interleaves the planning process with knowledge about anticipated action outcomes, followed by an interpretation of projected future world states with respect to current goals. We illustrate this principle with a multi-UAV scenario. 1 COGNITIVE AUTOMATION IN A MULTI-UAV SCENARIO In the following, a scenario is considered in which multiple simulated UAVs (Uninhabited Aerial Vehicles) have to accomplish a time critical and resource bounded military mission in a dynamic and continuous environment. The mission comprises flight planning, communication activities and payload operation tasks. We have implemented and evaluated a configuration in which a human team member steers one of the aircraft and controls an uncrewed team on a task-based level. The possible actions of the aircraft include selecting a flight plan, loitering, performing evasive maneuvers, payload management and sending messages to other participants like the request to fulfill a task. Experiments (Schulte et al., 2008) showed that an assistant system is required to aid the human operator of such a multi-UAV system to avoid human factors related problems (Rauschert et al., 2008). In a different experimental setup a mannedunmanned teaming (MUM-T) mission was flown with human operators located aboard a simulated airborne helicopter. The results showed that it generates severe problems to guide the UAVs on a state-of-theart waypoint-based level, rather than on an abstract, task-based level like in the system mentioned before (Uhrmann et al., 2009). Human decision makers and operators must trust in such a system of high autonomy, a fact that leads to the necessity that the system is able to inform about its states and future behaviors (Freed et al., 2005). For the intelligent agent to understand and correctly interpret the task commands and for the human operator to understand the agent’s feedback, we base our software development approach on cognitive automation , which is described further in (Onken and Schulte, 2009) and the following sections. Designing technical systems which are ergonomic and understandable for the human operator is a challenging task. A natural approach to designing such human-machine systems is to share work with the operator on a communication and cooperation level that is similar to that between two human operators. This is especially useful, if the technical system has the purpose of supporting the human operator as in the assistant systems mentioned in the previous section. 292 Brüggenwirth S., Strenzke R., Alexander A. and Schulte A. (2010). A GENERIC COGNITIVE SYSTEM ARCHITECTURE APPLIED TO THE UAV FLIGHT GUIDANCE DOMAIN. In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence Agents, pages 292-298 DOI: 10.5220/0002718202920298 Copyright c © SciTePress As explained in (Schulte et al., 2008), these systems must support the human operator in maintaining or even improving the operator’s situation awareness (SA). A well accepted theory of situation of SA in the human factors community stems from Endsley (Endsley and Garland, 2000). Figure 1: Situation awareness according to Endsley (Endsley and Garland, 2000). As shown in figure 1, she distinguishes three levels of SA with increasing complexity. While level 1 SA simply represents the elementary building-blocks of a perceived situation, level 2 SA strives to unify these elements in order to create a most comprehensible situational understanding. Interestingly, level 3 SA includes both, expected outcomes of own actions and the anticipated evolution of environment: Knowledge, which will also play a key role in the planning process. We argue that this human-oriented definition of situation awareness should be closely linked to what is commonly known as the belief state of an agent. Accordingly, an agent that builds up a humanlike high-level SA, e.g. inspired by Endsley’s categories, has advantages when when interacting with a human operator. Likewise, information that is used internally to derive this high-level abstraction should not be exposed to the operator. Furthermore the artificial agents must be able to pursue goals which are either matching directly to the cognitive goals of the human operator or are subgoals that the operator has assigned to them. We summarize these concepts as cognitive automation(Onken and Schulte, 2009). Cognitive automation enables technical systems to have a human-oriented situation representation, situation awareness and an explicit representation of goals. We belief that, although in some cases computationally less efficient, cognitive automation simplifies human-machine interaction as well as the knowledge engineering and system development processes. Although our first, Soar-based (Laird et al., 1987) implementation of the multi-UAV system has a relatively simple means-ends plan-library that is statically defined at development time, the knowledge based approach already shows emergent behavior not predefined by the developer. This behavior is not generated by directed reasoning or global optimization, instead it can be seen as a compromise of multiple actions related to multiple goals that arise sequentially. Figure 2: Emergent behavior due the explicit goals: avoid threat and return to base (Meitinger and Schulte, 2009). An example is the generation of a flight trajectory shown in figure 2. In the depicted situation, two goals are relevant one is the shortest possible flight path to the home base and the other is not to come too close to any SAM (Surface to Air Missile) site. The latter goal is (reactively) triggered as soon as a minimal distance is under-run and then replaces the first goal as the actively pursued one (Meitinger and Schulte, 2009). Even though the resulting flight path resembles the optimal curved trajectory, the emergent behavior described above is the result of a greedy algorithm, which follows a locally optimal heuristic (alternating between the shortest flight path and SAM site avoidance goal). Currently, no projection of future environment states is made and therefore loops in the behavior of the agent may occur. 2 THE COGNITIVE PROCESS According to the principle of cognitive automation described in section 1, we have derived an information processing scheme, which we call the cognitive process . It is structured in close analogy to Rasmussen’s model of human information processing (Rasmussen, 1983), often cited among cognitive psychologists and within the human factors engineering community. In figure 3, we present an interpreted version of the traditional Rasmussen model, tailored for application in intelligent agent environments. Our model is comprised of a low-level, subsymbolic feedback control layer (skill based), a symbolic, reactive layer with prestored procedures ( procedure-based ) and a deliberative, goal-oriented layer ( concept-based ) comparable to a BDI-agent design. From an agent-theoretical point of view, the approach resembles a hybrid agent architecture with horizontal layering, comparable to FergusonsTouringMachines(Ferguson, 1992). PerA GENERIC COGNITIVE SYSTEM ARCHITECTURE APPLIED TO THE UAV FLIGHT GUIDANCE DOMAIN