Sim, Kwang Mong (2009) BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 39 (1). pp. 198-211. ISSN 1083-4419. (doi:10.1109/TSMCB.2008.2004501) (KAR id:31929)
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Official URL: http://dx.doi.org/10.1109/TSMCB.2008.2004501 |
Abstract
Automated negotiation provides a means for resolving
differences among interacting agents. For negotiation with
complete information, this paper provides mathematical proofs
to show that an agent’s optimal strategy can be computed using
its opponent’s reserve price (RP) and deadline. The impetus of
this work is using the synergy of Bayesian learning (BL) and
genetic algorithm (GA) to determine an agent’s optimal strategy
in negotiation (N) with incomplete information. BLGAN adopts:
1) BL and a deadline-estimation process for estimating an opponent’s
RP and deadline and 2) GA for generating a proposal
at each negotiation round. Learning the RP and deadline of an
opponent enables the GA in BLGAN to reduce the size of its search
space (SP) by adaptively focusing its search on a specific region
in the space of all possible proposals. SP is dynamically defined
as a region around an agent’s proposal P at each negotiation
round. P is generated using the agent’s optimal strategy determined
using its estimations of its opponent’s RP and deadline.
Hence, the GA in BLGAN is more likely to generate proposals
that are closer to the proposal generated by the optimal strategy.
Using GA to search around a proposal generated by its current
strategy, an agent in BLGAN compensates for possible errors in
estimating its opponent’s RP and deadline. Empirical results show
that agents adopting BLGAN reached agreements successfully,
and achieved: 1) higher utilities and better combined negotiation
outcomes (CNOs) than agents that only adopt GA to generate their
proposals, 2) higher utilities than agents that adopt BL to learn
only RP, and 3) higher utilities and better CNOs than agents that
do not learn their opponents’ RPs and deadlines.
Item Type: | Article |
---|---|
DOI/Identification number: | 10.1109/TSMCB.2008.2004501 |
Uncontrolled keywords: | Automated negotiation, Bayesian learning (BL), genetic algorithms (GAs), intelligent agents, negotiation agents |
Subjects: |
Q Science > Q Science (General) > Q335 Artificial intelligence Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Kwang Mong Sim |
Date Deposited: | 24 Oct 2012 10:31 UTC |
Last Modified: | 05 Nov 2024 10:14 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/31929 (The current URI for this page, for reference purposes) |
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