Speaker: Gabriele Farina

Abstract: Tree-form, sequential decision making (TFSDM) captures tree-form decision processes where the decision maker faces multiple decisions interleaved with observations about the way previous decisions affected the (potentially adversarial) environment. TFSDM provides a powerful and general formalism which captures the decision problem faced by a player of an adversarial imperfect-information extensive-form game (such as poker and other non-recreational strategic interactions), as well as partially-observable Markov decision processes for which the agent conditions its policy on the entire history of observations and actions. By allowing interleaved decisions and observations, TFSDM is significantly more sophisticated than non-sequential decision making, where only one action is planned `in a vacuum' prescinding from any observation about the state of the system. This sophistication is reflected in the underlying mathematical and computational details. Our understanding of TFSDM making lacks decisively behind that of non-sequential decision making. In this talk, I will try to shed some light on the theoretical and algorithmic foundations of tree-form sequential decision making, as well as the richness of connections between TFSDM and game theory.