Ad Hoc Teamwork by Learning Teammates' Task

We address ad hoc teamwork, where an agent must coordinate with other agents in an unknown common task without pre-defined coordination. We formalize the ad hoc teamwork problem as a sequential decision problem and propose (i) the use of an online learning approach that considers the different tasks depending on their ability to predict the behavior of the teammate; and (ii) a decision-theoretic approach that models the ad hoc teamwork problem as a partially observable Markov decision problem. We provide theoretical and empirical evaluation of the performance of our proposed methods in several domains of different complexity.