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 di erent ways, by di erent agents. Also the sequence of these actions can in uence the goal achievement or its quality. This class of problems has been tackled under d...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2005 |
| País: | Brasil |
| Institución: | Universidade Federal do Rio Grande do Sul (UFRGS) |
| Repositorio: | Repositório Institucional da UFRGS |
| Idioma: | inglés |
| OAI Identifier: | oai:www.lume.ufrgs.br:10183/72566 |
| Acceso en línea: | http://hdl.handle.net/10183/72566 |
| Access Level: | acceso abierto |
| Palabra clave: | Inteligência artificial Sistemas multiagentes Insetos sociais Multiagent organization Adaptation and learning in multiagent systems Swarm intelligence Self-organization |
| Sumario: | One of the well studied issues in multi-agent systems is the standard action-selection problem where a goal task can be performed in di erent ways, by di erent agents. Also the sequence of these actions can in uence the goal achievement or its quality. This class of problems has been tackled under di erent approaches. At the high-level coordination one, the speci cation 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 speci cation, 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. |
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