Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Coordinating learning agents for multiple resource job scheduling
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Multi-robot coalition formation in real-time scenarios
Robotics and Autonomous Systems
Swarm-like Methodologies for Executing Tasks with Deadlines
Journal of Intelligent and Robotic Systems
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One of the well-studied issues in multi-agent systems is the standard action-selection and sequencing problem where a goal task can be performed in different ways, by different agents.Tasks have constraints while agents have different characteristics such as capacity, access to resources, motivations, etc. This class of problems has been tackled under different approaches. Moreover, in 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 learning and adaptation, such as those observed among social insects. The present paper tackles the process of generating, adapting, and changing multiagent 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.