We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one’s rewards in the presence of other agents who might also...
We introduce a learning framework that combines elements of the well-known PAC and mistake-bound models. The KWIK (knows what it knows) framework was designed particularly for its...
Learning on real robots in an real, unaltered environment provides an extremely challenging problem. Many of the simplifying assumptions made in other areas of learning cannot be ...