A comparison of three information gathering strategies in DAI systems under noisy conditions

H. R. Rao; J. C. Moore; K. Nam; T. S. Raghu; A. Whinston

doi:10.1016/S0957-4174(96)00065-6

A comparison of three information gathering strategies in DAI systems under noisy conditions

H. R. Rao, J. C. Moore, K. Nam, T. S. Raghu, A. Whinston

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

This paper investigates a problem of task allocation in Distributed Artificial Intelligence (DAI) systems, where a coordinator allocates tasks among multiple agents in an optimal manner. In making the task allocation, the coordinator needs to understand the preference orders of the agents for the different task bundles. The coordinator does this by adopting an information acquisition strategy that leads to an optimal system welfare. Three different information acquisition strategies are investigated here. The strategies are compared in a noisy environment for the quality of information they provide in terms of the deviation from optimal system welfare.

Original language	English (US)
Pages (from-to)	489-505
Number of pages	17
Journal	Expert Systems With Applications
Volume	11
Issue number	4 SPEC. ISS.
DOIs	https://doi.org/10.1016/S0957-4174(96)00065-6
State	Published - 1996
Externally published	Yes

ASJC Scopus subject areas

Engineering(all)
Computer Science Applications
Artificial Intelligence

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10.1016/S0957-4174(96)00065-6

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title = "A comparison of three information gathering strategies in DAI systems under noisy conditions",

abstract = "This paper investigates a problem of task allocation in Distributed Artificial Intelligence (DAI) systems, where a coordinator allocates tasks among multiple agents in an optimal manner. In making the task allocation, the coordinator needs to understand the preference orders of the agents for the different task bundles. The coordinator does this by adopting an information acquisition strategy that leads to an optimal system welfare. Three different information acquisition strategies are investigated here. The strategies are compared in a noisy environment for the quality of information they provide in terms of the deviation from optimal system welfare.",

author = "Rao, {H. R.} and Moore, {J. C.} and K. Nam and Raghu, {T. S.} and A. Whinston",

note = "Funding Information: Acknowledgements-The authors would like to thank the conference participants at the 3rd World Congress on Expert Systems, Seoul, South Korea, 1996 for various critical comments. We would like to thank Professor J. K. Lee for his encouragement. This research has been funded by NSF under grant No. IRI9505790.",

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AU - Rao, H. R.

AU - Moore, J. C.

AU - Nam, K.

AU - Raghu, T. S.

AU - Whinston, A.

N1 - Funding Information: Acknowledgements-The authors would like to thank the conference participants at the 3rd World Congress on Expert Systems, Seoul, South Korea, 1996 for various critical comments. We would like to thank Professor J. K. Lee for his encouragement. This research has been funded by NSF under grant No. IRI9505790.

PY - 1996

Y1 - 1996

N2 - This paper investigates a problem of task allocation in Distributed Artificial Intelligence (DAI) systems, where a coordinator allocates tasks among multiple agents in an optimal manner. In making the task allocation, the coordinator needs to understand the preference orders of the agents for the different task bundles. The coordinator does this by adopting an information acquisition strategy that leads to an optimal system welfare. Three different information acquisition strategies are investigated here. The strategies are compared in a noisy environment for the quality of information they provide in terms of the deviation from optimal system welfare.

AB - This paper investigates a problem of task allocation in Distributed Artificial Intelligence (DAI) systems, where a coordinator allocates tasks among multiple agents in an optimal manner. In making the task allocation, the coordinator needs to understand the preference orders of the agents for the different task bundles. The coordinator does this by adopting an information acquisition strategy that leads to an optimal system welfare. Three different information acquisition strategies are investigated here. The strategies are compared in a noisy environment for the quality of information they provide in terms of the deviation from optimal system welfare.

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A comparison of three information gathering strategies in DAI systems under noisy conditions

Abstract

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