Bionically Inspired Information Representation: Realizing Neuropsychoanalytic Concepts of Information Processing Within the Computational Framework Arsi10

This work focuses on the first ever realization of neuropsychoanalytic concepts for generating and processing mental data structures in cognitive science and compares said approach to established bionically inspired methodologies. An existing decision unit is supplemented with an information representation system. By use of a top-down design approach, the resulting adaptations are introduced into a new model whose implementation in embodied software agents produces the computational framework ARSi10. The multi-agent framework-based simulator ?Bubble World? is developed as a test-bed with predefined use cases, and the agents? abilities are evaluated through internal and external performance indicators. These show the benefits of the developed system. Internal and external sensor data are mapped to neuropsychoanalytically inspired data structures and used for the decision making process. This allows the agents to interact with their environment while keeping their system resources balanced and thus retaining their functional abilities.