A-Teams: An Agent Architecture for Optimization and Decision-Support
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...
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| creator | Rachlin, John Goodwin, Richard Murthy, Sesh Akkiraju, Rama Wu, Fred Kumaran, Santhosh Das, Raja |
| description | 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. |
| doi_str_mv | 10.1007/3-540-49057-4_17 |
| format | Conference Proceeding |
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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.</abstract><cop>Germany</cop><pub>Springer Berlin / Heidelberg</pub><doi>10.1007/3-540-49057-4_17</doi><oclcid>1239992730</oclcid><tpages>16</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Intelligent Agents V: Agents Theories, Architectures, and Languages, 1999, Vol.1555, p.261-276 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_1576905 |
| source | Springer Books |
| subjects | Agent Architecture Applied sciences Artificial intelligence Class Library Computer science control theory systems Constraint Satisfaction Problem Exact sciences and technology Learning and adaptive systems Software Agent Traveling Salesman Problem |
| title | A-Teams: An Agent Architecture for Optimization and Decision-Support |
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