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A neural network-based model to learn agent's utility function


Jazayeriy, Hamid and Azmi Murad, Masrah Azrifah and Sulaiman, Md. Nasir and Udzir, Nur Izura (2008) A neural network-based model to learn agent's utility function. In: 3rd International Symposium on Information Technology (ITSim'08), 26-28 Aug. 2008, Kuala Lumpur, Malaysia. .


Learning opponents’ preferences has a great impact on the success of negotiation, specially, when there is partial information about opponents. This incomplete information can be effectively utilized by intelligent agents equipped with adaptive capacities to learn opponents’ preferences during negotiation. This paper present a neural network based model, named ANUE, to estimate negotiators’ utility function. ANUE’s structure is inspired from mathematical interpretation of utility function. We have also presented eight test cases to evaluate ANUE’s performance where test cases cover all possible form of incomplete information concerning utility function. As a future work, we evaluate ANUE with proposed test cases.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1109/ITSIM.2008.4631653
Publisher: IEEE
Keywords: Adaptive systems; Computer science; Electronic mail; Information technology; Intelligent agent Intelligent systems; Learning systems; Neural networks; Software agents; Testing
Depositing User: Erni Suraya Abdul Aziz
Date Deposited: 27 Mar 2015 08:56
Last Modified: 28 May 2019 07:35
Altmetrics: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4631653
URI: http://psasir.upm.edu.my/id/eprint/33085
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