Daniel Polani is Professor of Artificial Intelligence, Director of the Centre for Computer Science and Informatics Research (CCSIR) and Head of the Adaptive Systems Research Group at the University of Hertfordshire. His research interests principles of cognition and intelligent decision-making, expressed in the language of information theory. In the past decades, he developed a fully information-theoretic account of the perception-action loop. Amongst other, he developed the concept of relevant information which permit an information-theoretic approach to decision-making under bounded informational resources to understand cognitive constraints to task execution. He pioneered the concept of empowerment, the external action-perception channel capacity of an agent, which has been shown to be a general and widely applicable model for generic taskless behaviour generation in ill-specified domains. This including assistance of third parties. The empowerment model is now also used by companies such as DeepMind, Volkswagen and others. The information-theoretic framework has been also more widely used to model multi-agent scenarios. He has been PI e.g. in the Horizon 2020 projects socSMCs (about social sensorimotor contingencies) and WiMUST (mission control of robotic submarine UAVs), FP7 CORBYS (cognitive control framework for robotic systems), and Co-PI in FP7 RoboSkin (artificial skin technology and its detection) and FEELIX GROWING (robots detecting and responding to emotional cue). He was President of the RoboCup Federation in 2017-2019.
Intrinsic motivations: Where from, where to?
Intrinsic motivations have now increasingly become established as a way to look at how agents find desirable things to do. Do we understand where this comes from? Hand-designed rewards are the way of traditional AI to do so, but the reality is that they are in general sparse, sometimes very, and often need a lot of additional tweaking to achieve the desired results. Not only is this clearly not what biological organisms do, but in fact, a lo of prior knowledge is infused in any reward shaping explicitly undertaken. Intrinsic motivations, on the other hand, seem to get much of this for free from the very structure of the environment. In my talk, I am going to review a few such relevant intrinsic motivations and what might make them work or fail. It is clear that the past of an agent and its actual (or potential) futures are crucial for this to work. We will discuss the manner in which this happens and, if there is time, also what this may tell us about the nature of decision-making in embodied agents.