Action Selection and Sequencing in Multiagent Systems: An Approximate Algorithm Based on Swarm Intelligence

One of the well studied issues in multiagent systems is the action-selection and sequencing problem where a goal is decomposed in tasks that can be performed in different ways and/or by different agents. This problem has been tackled under different approaches. In particular, for open, dynamic environments agents must be able to adapt to the changing organizational goals, available resources, their relationships to another agents, and so on. This problem is a key one in multi-agent systems and relates to models of adaptation, such as those observed among social insects. This paper shows how mechanisms from Swarm Intelligence are used to solve the action-selection and sequencing problem in dynamically changing environments that can have large number of agents and tasks.