An autonomous agent must know where it is, which objects are in the nearby surrounding area and where it can find already seen, far away objects. On the basis of the existing cognitive architecture Simulation of Mental Apparatus and Applications (SiMA) at the Institute of Computer Technology (ICT) we show, how cognitive architectures can get extended by location awareness and navigation. This paper deals with the problem of self-localization and path finding. Therefore, landmarks, locations and regions will be used. Actions lead the agent from one location to another. Locations are hierarchically grouped together to regions. The functionality was implemented exemplary in a multiagent simulation using simplified scenarios. The results confirm the prospects. Now we can start to introduce the functionality in our full featured main simulator.

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An autonomous agent must deal with unforeseen situations that can't be preprogrammed. Therefore, the agent has to make its own experiences, solutions and valuations to situations, actions and objects to be able to enhance previous actions and avoid repeating wrong actions. On the basis of the existing cognitive architecture Simulation of Mental Apparatus and Applications (SiMA) at the Institute of Computer Technology (ICT) an bionically inspired attempt of learning should be implemented in functional model of the human mind which is then used in a multi-agent simulation showing how bionically inspired cognitive architectures can get extended by learning. Due to the attempt in the project SiMA the learning function has to fit in the psychoanalytic model and therefore it has to be compatible with the way of learning that human beings do. This might also help to get a little bit closer to the understanding of how the human mind manipulates memory to show these until now unreached cognitive abilities.

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