Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recoveri...
Brian Ziebart, Andrew L. Maas, J. Andrew Bagnell, ...
We study the problem of agents locating other agents that are both capable and willing to help complete assigned tasks. An agent incurs a fixed cost for each help request it sends...
We discuss the design of the Intermediary Agent's brain, the control module of an embodied conversational virtual peer in a simulation game aimed at providing learning experi...
Decentralized Markov decision processes are frequently used to model cooperative multi-agent systems. In this paper, we identify a subclass of general DEC-MDPs that features regul...
Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. We propose unsupervised induction...