PUB - Publikationen an der Universität Bielefeld

Autonomous intelligent agents often fail in complex dynamic multi-tasking scenarios that are easily solvable for humans. For example, agents fail to adapt to new situations or cannot coordinate task switching between multiple tasks. A reason for this inability could be that in comparison to humans intelligent agents are missing cognitive capabilities. For one thing, humans have a sense of control (SoC), the feeling of being in control for successfully solving tasks. In general, it is unclear how to model the SoC of humans and how to make use of it for autonomous intelligent agents. This paper will present an approach to model the SoC and incorporating into autonomous intelligent agents modeled by model-based hierarchical reinforcement learning. Moreover, planned studies on the SoC in humans in multi-tasking problem scenarios are discussed. The hypothesis is that using this approach and equipping autonomous intelligent agents with a SoC will lead to improved decision-making in complex dynamic multi-tasking problems.