A Multi-agent Learning Model for Service Composition
XU Wen-bo, Jian Cao, Haiyan Zhao, Lei Wang · 2012
Agent technology has gained increasing popularity in service oriented architecture (SOA) because of its features of autonomy, initiative, interactivity, persistency and adaptability. There are already a plenty of implementations which integrate SOA with multi-agent systems (MAS). The ability of learning is a significant feature of MAS. This paper proposes a learning model of the service-oriented MAS for the service composition problem. It adopts the principle of reinforcement learning and is based on the Markov game and Q-learning. The reward of the learning procedure is determined by the QoS parameters such as responding time and cost. The mechanism of multi-agent leaning for service composition is introduced. The results of experiments and case study show that our multi-agent learning approach can reach convergence efficiently and it can also accelerate the service composition process based on the knowledge continuously learned from past composition experiences.