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Negotiation as a metaphor for distributed problem solving
Open accessAbstract
Abstract We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation. We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another. The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area. From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with planner, conniver, hearsay-ii , and pup 6 are used to demonstrate that negotiation—the two-way transfer of information—is a natural extension to the transfer of control mechanisms used in earlier problem-solving systems.
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Cites
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
Erman, L. D., Hayes-Roth, F., Lesser, V. R., & Reddy, D. R. (1980) · ACM Computing Surveys · doi:10.1145/356810.356816 · Cited by 1,357
Cited by
The interdisciplinary study of coordination
Malone, T. W., & Crowston, K. (1994) · ACM Computing Surveys · doi:10.1145/174666.174668 · Cited by 3,504
Multiagent systems: algorithmic, game-theoretic, and logical foundations
(2009) · Choice Reviews Online · doi:10.5860/choice.46-5662 · Cited by 1,756
ALLIANCE: an architecture for fault tolerant multirobot cooperation
Parker, L. E. (1998) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.681242 · Cited by 1,312
Practical verification of decision-making in agent-based autonomous systems
Dennis, L. A., Fisher, M., Lincoln, N. K., Lisitsa, A., & Veres, S. M. (2016) · Automated Software Engineering · doi:10.1007/s10515-014-0168-9 · Cited by 102
A new model for manufacturing supply chain networks: A multiagent approach
Chan, F. T. S., & Chan, H. K. (2004) · Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture · doi:10.1243/095440504323055551 · Cited by 47
Metadata
- Authors
- Davis, R., & Smith, R. G.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1983-01
- Volume
- 20
- Issue
- 1
- Pages
- 63-109
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2068394020
- Cited by
- 1,230
- References
- 1
- FWCI
- 9.26
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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