We consider a resource selection game with incomplete information about the resource-cost functions. All the players know is the set of players, an upper bound on the possible cos...
Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge...
Marc Maier, Brian Taylor, Huseyin Oktay, David Jen...
We look at the problem in belief revision of trying to make inferences about what an agent believed--or will believe--at a given moment, based on an observation of how the agent h...
In this paper, we propose a policy gradient reinforcement learning algorithm to address transition-independent Dec-POMDPs. This approach aims at implicitly exploiting the locality...
Recognizing and annotating the occurrence of team actions in observations of embodied agents has applications in surveillance and in training of military or sport teams. We descri...
Linus J. Luotsinen, Hans Fernlund, Ladislau Bö...