Studying Emotion and Interaction between Autonomous Cognitive Agents in Simulated and Robotic Environments

Studying Emotion and Interaction between Autonomous Cognitive Agents in Simulated and Robotic Environments Joscha Bach (jbach@uos.de) Institute for Cognitive Science, University of Osnabruck 49086 Osnabruck, Germany The MicroPsi Framework The Psi theory of Dietrich Dorner (Dorner 1999, Dorner et. al. 2002) is an attempt at a unified architecture of cognition; originating in psychological modeling, it proposes functional models of representation, action regulation, emotion, memory and perception. While the classical cognitive architectures (ACT, see Anderson and Lebiere 1998, Soar, see Laird, Newell, & Rosenbloom 1987, EPAM, see Gobet et al. 1997) primarily focus on symbolic models of cognitive processes and tend to study these processes in isolation, the Psi theory directs its attention instead on the modulation of cognition by motivational and emotional processes and the resulting relationship of an individual to its environment, specifically with respect to affordances (Gibson, 1977). Thus, it is less interested in modeling problem solving paradigms and more keen on examining situated agents in interaction with an environment and each other. Dorner’s work has lead to several agent implementations, primarily to model the influence of emotional processes on the execution complex tasks, which has been compared to emotional modulation in human subjects. In recent years, we have developed MicroPsi, a framework for representation and interaction of agents based on the Psi theory in the context of Artificial Intelligence, which has been used for the simulation of autonomous, motive-based knowledge acquisition in simulated agent environments and as a control architecture for robots (Bach 2003, Bach and Vuine 2003). Figure 1: MicroPsi framework. The goal of MicroPsi is the transformation of the Psi theory into a cognitive architecture by supplying formal models of Psi concepts and a toolkit to define agents based on the theory, conduct experiments and evaluate them. The toolkit is platform independent and consists of graphical editors for agents and their environments, a 3D viewer of the agent world, tools for performing evolutionary experiments, interfaces for robots. MicroPsi agents are defined using hierarchical spreading activation networks, which make up their control structures, knowledge representation and learning mechanism, all combined in a homogenous architecture. Current work is concerned with object classification in simulated environments, simulation of emotional configurations during action execution and agent interaction, and joint exploration tasks in simulated and robotic environments. References Anderson, J. R. , & Lebiere, C. (1998): Atomic Components of Thought. Hillsdale, NJ: Lawrence Erlbaum Bach, J. (2003). The MicroPsi Agent Architecture Proceedings of ICCM-5, International Conference on Cognitive Modeling, Bamberg, Germany (pp. 15-20) Bach, J. (2003). Connecting MicroPsi Agents to Virtual and Physical Environments. Workshops and Tutorials, 7th European Conference on Artificial Life, Dortmund, Germany. (pp. 128-132) Bach, J., Vuine, R. (2003). The AEP Toolkit for Agent Design and Simulation. M. Schillo et al. (eds.): MATES 2003, LNAI 2831, Springer Berlin, Heidelberg Bach, J. (2005). Representations for a Complex World: Combining Distributed and Localist Representations for Learning and Planning. In: Wermter, S. , Palm, G. (eds.): Biomimetic Neural Learning for Intelligent Robots. Springer Dorner, D. (1999). Bauplan fur eine Seele. Reinbeck Dorner, D. , Bartl, C. , Detje, F. , Gerdes, J., Halcour, (2002). Die Mechanik des Seelenwagens. Handlungsregulation. Verlag Hans Huber, Bern Gibson, J. J. (1977). The theory of affordances. In R. E. Shaw & J. Bransford (Eds.), Perceiving, Acting, and Knowing. Hillsdale, NJ: Lawrence Erlbaum Associates Gobet, F. , Richman, H. , Staszewski, J., & Simon, H. A. (1997). Goals, representations, and strategies in a concept attainment task: The EPAM model. The Psychology of Learning and Motivation, 37, 265-290 Laird, J. E. , Newell, A. , & Rosenbloom, P. S. (1987). SOAR: An Architecture for General Intelligence. Artificial Intelligence, 33: 1-64