Interactional Motivation in artificial systems: Between extrinsic and intrinsic motivation

This paper introduces Interactional Motivation (IM) as a way to implement self-motivation in artificial systems. An interactionally motivated agent selects behaviors for the sake of enacting the behavior itself rather than for the value of the behavior´s outcome. IM contrasts with extrinsic motivation by the fact that it defines the agent´s motivation independently from the environment´s state. Because IM does not refer to the environment´s states, we argue that IM is a form of self-motivation on the same level as intrinsic motivation. IM, however, differs from intrinsic motivation by the fact that IM allows specifying the agent´s inborn value system explicitly. This paper introduces a formal definition of the IM paradigm and compares it to the reinforcement-learning paradigm as traditionally implemented in Partially Observable Markov Decision Processes (POMDPs).