Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, anytime a new task is faced, learning must be restarted from scratch. Recently, ...
Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in large-scale systems. In this work, we develop a supervision fr...
Chongjie Zhang, Sherief Abdallah, Victor R. Lesser
Abstract. The aim of this paper is to show how the use of social networks may help users to behave as modelers they trust. Users are guided in this respect within the context of an...
Recent advances in termination analysis have yielded new methods and tools that are highly automatic. However, when they fail, even experts have difficulty understanding why and de...
Abstract. In this paper we investigate the possibility of an automatic construction of conceptual taxonomies and evaluate the achievable results. The hierarchy is performed by Ward...