Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
The paper describes a simulation substrate that allows thinking agents to interact with a world. The world is simulated by standard discrete event simulation, but the timing of an...
Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intell...
For any embodied, mobile, autonomous agent it is essential to control its actuators appropriately for the faced task. This holds for natural organisms as well as for robots. If sev...
When an agent receives a query from another agent, it tries to satisfy it by building an answer based on its current knowledge. Depending on the available time or the urgency of t...