This paper introduces a multiagent reinforcement learning algorithm that converges with a given accuracy to stationary Nash equilibria in general-sum discounted stochastic games. ...
A general technique is proposed to deal with the formalization of intuition and human-oriented concepts in competition thinking games like chess, such as defensive play, attack, t...
We present the agent programming language GTGolog, which integrates explicit agent programming in Golog with gametheoretic multi-agent planning in Markov games. It is a generalizat...
We introduce a new reliability infrastructure for file systems called I/O shepherding. I/O shepherding allows a file system developer to craft nuanced reliability policies to de...
Haryadi S. Gunawi, Vijayan Prabhakaran, Swetha Kri...
We consider two-player games played over finite state spaces for an infinite number of rounds. At each state, the players simultaneously choose moves; the moves determine a succ...
Luca de Alfaro, Rupak Majumdar, Vishwanath Raman, ...