SciELO - Scientific Electronic Library Online

 
vol.6 número3Extensive Experimental Validation of a Personalized Approach for Coping with Unfair Ratings in Reputation SystemsDeveloping a Fuzzy Multi-attribute Matching and Negotiation Mechanism for Sealed-bid Online Reverse Auctions índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Journal of theoretical and applied electronic commerce research

versión On-line ISSN 0718-1876

Resumen

JAZAYERIY, Hamid; AZMI-MURAD, Masrah; SULAIMAN, Nasir  y  IZURA UDIZIR, Nur. The Learning of an Opponent's Approximate Preferences in Bilateral Automated Negotiation. J. theor. appl. electron. commer. res. [online]. 2011, vol.6, n.3, pp. 65-84. ISSN 0718-1876.  http://dx.doi.org/10.4067/S0718-18762011000300006.

Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent's preferences. However, the agent has limited time to find an agreement while trying to protect its payoffs by keeping its preferences private. In doing so, generating offers with incomplete information about the opponent's preferences is a complex process and, therefore, learning these preferences in a short time can assist the agent to generate proper offers. In this paper, we have developed an incremental on-line learning approach by using a hybrid soft-computing technique to learn the opponent's preferences. In our learning approach, first, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, a simplified genetic algorithm is used to search the best fuzzy preferences that articulate the opponent's intention. Experimental results showed that our learning approach can estimate the opponent's preferences effectively. Moreover, results indicate that agents which use the proposed learning approach not only have more chances to reach agreements but also will be able to find agreements with greater joint utility.

Palabras llave : Bilateral negotiation; Learning preferences; Uncertain information; Genetic algorithm; E-marketplace.

        · texto en Inglés     · pdf en Inglés