Learning to negotiate optimally in non-stationary environments.
OA Location
Author(s)
Narayanan, V
Jennings, NR
Type
Conference 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.
Date Issued
2006
Citation
2006, pp.288-300
Start Page
288
End Page
300
Identifier
http://eprints.soton.ac.uk/263081/
Source
10th International Workshop on Cooperative Information Agents
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Artificial Intelligence & Image Processing
08 Information And Computing Sciences
Publication Status
Unpublished