A swarm based approach to adapt the structural dimension of agents' organizations

One of the well studied issues in multi-agent systems is the standard action-selection problem where a goal task can be performed in different ways, by different agents. Also the sequence of these actions can influence the goal achievement or its quality. This class of problems has been tackled under different approaches. At the high-level coordination one, the specification of the organizational issues is crucial. However, in dynamic environments, agents must be able to adapt to the changing organizational goals, available resources, their relationships to the presence of another agents, and so on. This problem is a key one in multi-agent systems and relates to models of learning and adaptation, such as those observed among social insects. The present paper tackles the process of generating, adapting, and changing multi-agent organization dynamically at system runtime, using a swarm inspired approach. This approach is used here mainly for task allocation with low need of pre-planning and specification, and no need of explicit coordination. The results of our approach and another quantitative one are compared here and it is shown that in dynamic domains, the agents adapt to changes in the organization, just as social insects do.