Despite much research that has been done on constraint satisfaction problems (CSP's), the framework is sometimes inflexible and the results are not very satisfactory when app...
One difficulty with existing theoretical work on HTN planning is that it does not address some of the planning constructs that are commonly used in HTN planners for practical appl...
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 ...
Stochastic games are a generalization of MDPs to multiple agents, and can be used as a framework for investigating multiagent learning. Hu and Wellman (1998) recently proposed a m...
By using an example from a robot navigating domain, we argue that to specify declaratively the behavior of an agent, we need to have a formal and explicit notion of \quality plans...