Coordination within decentralized agent groups frequently requires reaching global consensus, but typical hierarchical approaches to reaching such decisions can be complex, slow, ...
Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function be...
Renato L. G. Cavalcante, Alex Rogers, Nicholas R. ...
Much work in AI deals with the selection of proper actions in a given (known or unknown) environment. However, the way to select a proper action when facing other agents is quite ...
Consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. Consensus clustering (also known as aggregation of clustering) ca...
We present a new dynamic probabilistic state exploration algorithm based on hash compaction. Our method has a low state omission probability and low memory usage that is independen...
William J. Knottenbelt, Mark Mestern, Peter G. Har...