In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a r...
Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice ...
In this paper we outline a framework for performing automated discovery, composition and execution of web services based solely on the information available in interface descripti...
Agents often have preference models that are more complicated than minimizing the expected execution cost. In this paper, we study how they should act in the presence of uncertaint...
We present a faster method of solving optimal planning problems and show that our solution performs up to an order of magnitude faster than Satplan on a variety of problems from t...
This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages-...