How should an intelligent agent behave in order to best realize their goals? What inferences or actions should they make in order to solve an important computational task? Cognitive science aims to answer these questions at an abstract computational level, using tools from probability theory, statistical inference, and elsewhere.

In this session we will discuss how such optimal behavior should change under different conditions of uncertainty, background knowledge, multiple agents, or constraints on resource. This can be used to understand human behavior in the real world or the lab, as well as build artificial agents that learn robust and generalizable world models from small amounts of data.