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Learning organizational roles for negotiated search in a multiagent system
This paper presents studies in learning a form of organizational knowledge called organizational roles in a multi-agent agent system. It attempts to demonstrate the viability and utility of self-organization in an agent-based system involving complex interactions within the agent set. We present a m...
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| Published in: | International journal of human-computer studies 1998-01, Vol.48 (1), p.51-67 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c348t-a04ce8c5d1dc5f692964ef76848dd00529cc5588a47c2f4ffebbad454800e30f3 |
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| container_end_page | 67 |
| container_issue | 1 |
| container_start_page | 51 |
| container_title | International journal of human-computer studies |
| container_volume | 48 |
| creator | PRASAD, M.V.N. LESSER, VICTOR R. LANDER, SUSAN E. |
| description | This paper presents studies in learning a form of organizational knowledge called organizational roles in a multi-agent agent system. It attempts to demonstrate the viability and utility of self-organization in an agent-based system involving complex interactions within the agent set. We present a multi-agent parametric design system called L-TEAM where a set of heterogeneous agents learn their organizational roles in negotiated search for mutually acceptable designs. We tested the system on a steam condenser design domain and empirically demonstrated its usefulness. L-TEAM produced better results than its non-learning predecessor, TEAM, which required elaborate knowledge engineering to hand-code organizational roles for its agent set. In addition, we discuss experiments with L-TEAM that highlight the importance of certain learning issues in multi-agent systems. |
| doi_str_mv | 10.1006/ijhc.1997.0160 |
| format | article |
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| fulltext | fulltext |
| identifier | ISSN: 1071-5819 |
| ispartof | International journal of human-computer studies, 1998-01, Vol.48 (1), p.51-67 |
| issn | 1071-5819 1095-9300 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_57446375 |
| source | Library & Information Science Abstracts (LISA); ScienceDirect Freedom Collection |
| subjects | Computer applications Engineering Machine learning Multiagents Organizational roles |
| title | Learning organizational roles for negotiated search in a multiagent system |
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