A key characteristic of intelligent systems, both biological and artificial, is the ability to efficiently adapt behavior in order to interact with the environment for their benefit. Importantly, these systems are subject to information processing limitations due to both, their own computational constraints and the lack of knowledge about the environment. In this talk we give an introduction to a framework for optimal decision-making under such information constraints that is grounded in information-theory. The generality of the proposed approach allows modeling important aspects of the decision-making process based on the same first principles. In particular, the framework can describe not only bounded rationality i.e. decision-making with computational limitations, but also, model uncertainty (lack of knowledge about the environment), risk-sensitivity, hierarchies of abstractions and lastly, likelihood synthesis for perception action systems.
In the first part of the talk we will give an introduction to decision-making with information constraints whereas in the second part we will show an application to planning in unknown Markov Decision Processes and to hierarchical decision-making scenarios.