We have constructed ADVISOR, a two-agent machine learning architecture for intelligent tutoring systems (ITS). The purpose of this architecture is to centralize the reasoning of a...
In ad hoc wireless LANs populated by mutually impenetrable groups of anonymous stations, honest stations are prone to "bandwidth stealing" by selfish stations. The proble...
It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems. In t...
Policy search is a method for approximately solving an optimal control problem by performing a parametric optimization search in a given class of parameterized policies. In order ...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...