Multiagent learning di ers from standard machine learning in that most existing learning methods assume that all knowledge is available locally in a single agent. In multiagent sy...
As heterogeneous distributed systems, multi-agent systems present some challenging con guration management issues. There are the problems of knowing how to allocate agents to comp...
Joseph A. Giampapa, Octavio H. Juarez-Espinosa, Ka...
Agent-based approaches to manufacturing scheduling and control have gained increasing attention in recent years. Such approaches are attractive because they o er increased robustn...
We present a centralized and a distributed algorithms for scheduling multi-task agents in heterogeneous networks. Our centralized algorithm has an upper bound on the overall compl...
The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain on...