A Swarm Based Approach for Task Allocation in Dynamic Agents Organizations

• Main Authors: de Oliveira, Denise, Ferreira Jr, Paulo R., Bazzan, Ana L. C.
• Format: Conference Proceeding
• Language: English
• Subjects: Biological system modeling; Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Cooperation and coordination; Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Multi-agent systems; Environmental management; Insects; Multiagent systems; Permission; Problem-solving; Scheduling; Standards organizations; Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms; Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms > Scheduling algorithms; Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making
• DOI: 10.5555/1018411.1018906

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.