As learning agents move from research labs to the real world, it is increasingly important that human users, including those without programming skills, be able to teach agents de...
In this paper, we propose a Quantified Distributed Constraint Optimization problem (QDCOP) that extends the framework of Distributed Constraint Optimization problems (DCOPs). DCOP...
Successful interaction between autonomous agents is contingent on those agents making decisions consistent with the expectations of their peers -- these expectations are based on ...
In the strategyproof classification setting, a set of labeled examples is partitioned among multiple agents. Given the reported labels, an optimal classification mechanism returns...
Reshef Meir, Ariel D. Procaccia, Jeffrey S. Rosens...
A major challenge for traditional approaches to multiagent learning is to train teams that easily scale to include additional agents. The problem is that such approaches typically...
David B. D'Ambrosio, Joel Lehman, Sebastian Risi, ...