Stochastic strategy for multiagent contracts and the impact of deliberation overhead

Sunju Park; Edmund H. Durfee

Stochastic strategy for multiagent contracts and the impact of deliberation overhead

Sunju Park, Edmund H. Durfee

Department of Business Administration

Research output: Contribution to conference › Paper › peer-review

Abstract

A four-step stochastic contracting strategy has been developed. The agent models the contracting process using Markov chains (MC), computes the transition probabilities between the MC states, computes the probabilities and payoffs of success and failure of a contract, and chooses an action that maximizes its expected utility. The strategy has been shown to perform better than a static strategy and a simple stochastic strategy.

Original language	English
Pages	840
Number of pages	1
Publication status	Published - 1997
Event	Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97 - Providence, RI, USA Duration: 1997 Jul 27 → 1997 Jul 31

Conference

Conference	Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97
City	Providence, RI, USA
Period	97/7/27 → 97/7/31

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Cite this

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abstract = "A four-step stochastic contracting strategy has been developed. The agent models the contracting process using Markov chains (MC), computes the transition probabilities between the MC states, computes the probabilities and payoffs of success and failure of a contract, and chooses an action that maximizes its expected utility. The strategy has been shown to perform better than a static strategy and a simple stochastic strategy.",

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AU - Park, Sunju

AU - Durfee, Edmund H.

PY - 1997

Y1 - 1997

N2 - A four-step stochastic contracting strategy has been developed. The agent models the contracting process using Markov chains (MC), computes the transition probabilities between the MC states, computes the probabilities and payoffs of success and failure of a contract, and chooses an action that maximizes its expected utility. The strategy has been shown to perform better than a static strategy and a simple stochastic strategy.

AB - A four-step stochastic contracting strategy has been developed. The agent models the contracting process using Markov chains (MC), computes the transition probabilities between the MC states, computes the probabilities and payoffs of success and failure of a contract, and chooses an action that maximizes its expected utility. The strategy has been shown to perform better than a static strategy and a simple stochastic strategy.

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M3 - Paper

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T2 - Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97

Y2 - 27 July 1997 through 31 July 1997

ER -

Stochastic strategy for multiagent contracts and the impact of deliberation overhead

Abstract

Conference

All Science Journal Classification (ASJC) codes

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