MURAL - Maynooth University Research Archive Library

    Multi-agent multi-issue negotiations with incomplete information: A Genetic Algorithm based on discrete surrogate approach

    Kattan, Ahmed and Ong, Yew-Soon and Galvan-Lopez, Edgar (2013) Multi-agent multi-issue negotiations with incomplete information: A Genetic Algorithm based on discrete surrogate approach. IEEE Transactions on Evolutionary Computation. pp. 2556-2563. ISSN 1089-778X

    Download (1MB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    In this paper we present a negotiation agent based on Genetic Algorithm (GA) and Surrogate Modelling for a multi-player multi-issue negotiation model under incomplete information scenarios to solve a resource-allocation problem. We consider a multi-lateral negotiation protocol by which agents make offers sequentially in consecutive rounds until the deadline is reached. Agents' offers represent suggestions about how to divide the available resources among all agents participating in the negotiation. Each agent may “Accept” or “Reject” the offers made by its opponents through selecting the “Accept” or “Reject” option. The GA is used to explore the space of offers and surrogates used to model the behaviours of individual opponent agents for enhanced genetic evolution of offers that is agreeable upon all agents. The GA population comprises of solution individuals that are formulated as matrices where a specialised three different search operators that take the matrix representation into considerations are considered. Experimental studies of the proposed negotiation agent under different scenarios demonstrated that the negotiations by the agents completed in agreement before the deadline is reached, while at the same time, maximising profits.

    Item Type: Article
    Keywords: Space exploration; Mathematical model; Approximation methods; Genetic algorithms; Computational modeling; Equations; Sociology;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15375
    Identification Number:
    Depositing User: Edgar Galvan
    Date Deposited: 31 Jan 2022 16:39
    Journal or Publication Title: IEEE Transactions on Evolutionary Computation
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads