Recent multi-agent extensions of Q-Learning require knowledge of other agents’ payoffs and Q-functions, and assume game-theoretic play at all times by all other agents. This pap...
We improve upon a method introduced in (Bertalmio et. al. JCP 2001) for solving evolution PDEs on codimension-one surfaces in RN. As in the original method, by representing the su...
We present a two-phase algorithm for solving large-scale quadratic programs (QPs). In the first phase, gradient-projection iterations approximately minimize an augmented Lagrangian...
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...
Abstract Experience with the development and maintenance of large test suites specified using the Testing and Test Control Notation (TTCN-3) has shown that it is difficult to const...