We propose a new set of criteria for learning algorithms in multi-agent systems, one that is more stringent and (we argue) better justified than previous proposed criteria. Our cr...
Planning has traditionally focused on single agent systems. Although planning domain languages have been extended to multiagent domains, solution concepts have not. Previous solut...
Michael H. Bowling, Rune M. Jensen, Manuela M. Vel...
Although in theory opponent modeling can be useful in any adversarial domain, in practice it is both difficult to do accurately and to use effectively to improve game play. In thi...
Kennard Laviers, Gita Sukthankar, David W. Aha, Ma...
In the traditional voting manipulation literature, it is assumed that a group of manipulators jointly misrepresent their preferences to get a certain candidate elected, while the ...
We study a new class of decentralized algorithms for discrete optimization via simulation, which is inspired by the fictitious play algorithm applied to games with identical inte...