Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop - Biehl, Martin; Guckelsberger, Christian; Salge, Christoph; Smith, Simón C.; Polani, Daniel (Frontiers in Neurorobotics, 2018)
Active inference is an ambitious theory which treats perception and inference on one side, and action selection on the other side under the heading of a single principle. It also provides biologically plausible explanations for many cognitive phenomena, including consciousness. Action selection in active inference is driven by an objective function which evaluates possible future actions with respect to current, inferred beliefs about the world. Such independence from extrinsic rewards comes with a high level of robustness across e.g. different environments or agent morphologies. In the literature, more functions with these favourable properties have been summarised under the notion of intrinsic motivations. In contrast to active inference however, these formal models of motivation usually come without a commitment to particular inference and action selection mechanisms. Up to now it was not clear whether the inference and action selection machinery of active inference could also be used by alternatives to the originally included intrinsic motivation. Here we reconstruct the active inference approach, locate the original formulation within, and show how alternative intrinsic motivations can be used while keeping many of the original features intact. The perception-action loop serves as unifying formal concept to relate different components within active inference. Active inference research may profit from comparisons of the dynamics induced by alternative intrinsic motivations. Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that may also share the biological plausibility of active inference.