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BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information

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)


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: 16 Nov 2021 10:09 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Sim, Kwang Mong.

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