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 argue that allowing self-interested agents to activate social institutions during run-time can improve the robustness (i.e., stability, cooperation or fairness) ...
In this paper, we describe a method for pedagogical agents to choose when to interact with learners in interactive learning environments. This method is based on observations of h...
This paper intends to introduce the development of a terminal agent for QoS measurement that is suitable for an NGN environment, and to summarize the results of its performance tes...
ChinChol Kim, SangChul Shin, Sang Yong Ha, SunYoun...
The paper describes a decentralized peer-to-peer multi-agent learning method based on inductive logic programming and knowledge trading. The method uses first-order logic for model...