Multi-level knowledge processing in cognitive technical systems

Abstract

Companion-Systems are composed of different modules that have to share a single, sound estimate of the current situation. While the long-term decision-making of automated planning requires knowledge about the user’s goals, short-term decisions, like choosing among modes of user-interaction, depend on properties such as lighting conditions. In addition to the diverse scopes of the involved models, a large portion of the information required within such a system cannot be directly observed, but has to be inferred from background knowledge and sensory data—sometimes via a cascade of abstraction layers, and often resulting in uncertain predictions. In this contribution, we interpret an existing cognitive technical system under the assumption that it solves a factored, partially observable Markov decision process. Our interpretation heavily draws from the concepts of probabilistic graphical models and hierarchical reinforcement learning, and fosters a view that cleanly separates between inference and decision making. The results are discussed and compared to those of existing approaches from other application domains.