We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using lo...
In this paper we propose a multiagent architecture for implementing concurrent reinforcement learning, an approach where several agents, sharing the same environment, perceptions ...
In this paper, we consider the problem of deriving a component X of a system knowing the behavior of the whole system C and the other components A. The component X is derived by s...
This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in w...
: We present a way to mix the lower control of agents with the high level specifications of their goals. This paper addresses various topics required to animate virtual humans in a...