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Learning to negotiate optimally in non-stationary environments.

Learning to negotiate optimally in non-stationary environments.
Learning to negotiate optimally in non-stationary environments.
We Adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal strategy in incomplete information settings. Specifically, an agent learns the optimal strategy to play against an opponent whose strategy varies with time, assuming no prior information about its negotiation parameters. In doing so, we present a new framework for adaptive negotiation in such non-stationary environments and develop a novel learning algorithm, which is guaranteed to converge, that an agent can use to negotiate optimally over time. We have implemented our algorithm and shown that it converges quickly in a wide range of cases.
288-300
Narayanan, V.
1426d816-1ac5-4394-8a0a-ce221de6faa5
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Narayanan, V.
1426d816-1ac5-4394-8a0a-ce221de6faa5
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Narayanan, V. and Jennings, N. R. (2006) Learning to negotiate optimally in non-stationary environments. 10th International Workshop on Cooperative Information Agents, United Kingdom. pp. 288-300 .

Record type: Conference or Workshop Item (Paper)

Abstract

We Adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal strategy in incomplete information settings. Specifically, an agent learns the optimal strategy to play against an opponent whose strategy varies with time, assuming no prior information about its negotiation parameters. In doing so, we present a new framework for adaptive negotiation in such non-stationary environments and develop a novel learning algorithm, which is guaranteed to converge, that an agent can use to negotiate optimally over time. We have implemented our algorithm and shown that it converges quickly in a wide range of cases.

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Published date: 2006
Venue - Dates: 10th International Workshop on Cooperative Information Agents, United Kingdom, 2006-01-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 263081
URI: http://eprints.soton.ac.uk/id/eprint/263081
PURE UUID: 91c6dde4-7de2-4876-bff3-59e4c44da35a

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Date deposited: 09 Oct 2006
Last modified: 30 Jul 2019 19:00

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