Abstract
The effectiveness of an agent architecture is measured by its successful application to real problems. In this paper, we describe an agent architecture, A-Teams, that we have successfully used to develop real-world optimization and decision support applications. In an A-Team, an asynchronous team of agents shares a population of solutions and evolves an optimized set of solutions. Each agent embodies its own algorithm for creating, improving or eliminating a solution. Through sharing of the population of solutions, cooperative behavior between agents emerges and tends to result in better solutions than any one agent could produce. Since agents in an A-Team are autonomous and asynchronous, the architecture is both scalable and robust. In order to make the architecture easier to use and more widely available, we have developed an A-Team class library that provides a foundation for creating A-Team based decision-support systems.
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Rachlin, J. et al. (1999). A-Teams: An Agent Architecture for Optimization and Decision-Support. In: Müller, J.P., Rao, A.S., Singh, M.P. (eds) Intelligent Agents V: Agents Theories, Architectures, and Languages. ATAL 1998. Lecture Notes in Computer Science, vol 1555. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49057-4_17
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DOI: https://doi.org/10.1007/3-540-49057-4_17
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