Machine Learning Algorithms for Multi-Agent Systems

Abstract

Abstract

Multi-Agent Systems are used in a wide range of applications such as e-commerce, simulation, robotics, traffic control, manufacturing, health care, and Cloud Computing. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. Agents instead need to discover a solution on their own, using learning. The heart of the problem is how agents will learn the environment independently and then how they will cooperate to achieve the system goals. Furthermore, how the agents could coordinate and decide in order to achieve these goals. The objective of this study is to answer these commonly asked questions from the machine learning perspectives.

Multi-Agent Learning is not merely a matter of “straight” learning, but a matter involving complex patterns of social interaction and cognitive processes, which leads to complex collective functions. Many of the techniques developed in machine learning can be transferred to settings where there are multiple, interdependent, interacting learning agents, although they may require modification to account for the other agents in the environment. Furthermore, Multi-Agent Systems present a set of unique learning opportunities over and above single-learner machine learning. In a Multi-Agent System, an agent is always acting in the context of other agents, and so it must adapt its plans according to its expectations of the others.