Run-Time Selection of Coordination Mechanisms in Multi-Agent Systems

This paper presents a framework that enables autonomous agents to dynamically select the mechanism they employ in order to coordinate their inter-related activities. Adopting this framework means coordination mechanisms move from the realm of being imposed upon the system at design time, to something that the agents select at run-time in order to fit their prevailing circumstances and their current coordination needs. Empirical analysis is used to evaluate the effect of various design alternatives for the agent's decision making mechanisms and for the coordination mechanisms themselves

Learning to select a co-ordination mechanism

This paper examines the potential and the impact of introducing learning capabilities into autonomous agents that make decisions at run-time about which mechanism to exploit in order to coordinate their activities. Specifically, the efficacy of learning is evaluated for making the decisions that are involved in determining when and how to coordinate. Our motivating hypothesis is that to deal with dynamic and unpredictable environments it is important to have agents that can learn the right situations in which to attempt to coordinate and the right method to use in those situations. This hypothesis is evaluated empirically, using reinforcement based algorithms, in a grid-world scenario in which a) an agent's prediction about the other agents in the environment is approximately correct and b) an agent can not correctly predict the others' behaviour. The results presented show when, where and why learning is effective when it comes to making a decision about selecting a coordination mechanism