A major challenge in the field of Multi-Agent Systems is to enable autonomous agents to allocate tasks efficiently. This paper extends previous work on an approach to the collec...
Christian Guttmann, Iyad Rahwan, Michael P. George...
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
This paper introduces a model for Distributed Employee Timetabling Problems (DisETPs) and proposes a general architecture for solving DisETPs by using a Multi Agent System (MAS) pa...
Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement l...
This paper addresses the issue of learning from communication among agents that work in the same domain, are capable of learning from examples, and communicate using an argumentat...