TY - JOUR
KW - Differential Evolution Algorithm
KW - Agents
KW - Automated Negotiation
KW - Deadline Learning
KW - Invasive Weed Optimization
AU - R Ayachi
AU - H Bouhani
AU - Ben Amor
AB - The efficiency of automated multi-issue negotiation depends on the available information about the opponent. In a competitive negotiation environment, agents do not reveal their parameters to their opponents in order to avoid exploitation. Several researchers have argued that an agent's optimal strategy can be determined using the opponent's deadline and reserve points. In this paper, we propose a new learning agent, so-called Evolutionary Learning Agent (ELA), able to estimate its opponent's deadline and reserve points in bilateral multi-issue negotiation based on opponent's counter-offers (without any additional extra information). ELA reduces the learning problem to a system of non-linear equations and uses an evolutionary algorithm based on the elitism aspect to solve it. Experimental study shows that our learning agent outperforms others agents by improving its outcome in term of average and joint utility.
IS - Regular Issue
M1 - 3
N2 - The efficiency of automated multi-issue negotiation depends on the available information about the opponent. In a competitive negotiation environment, agents do not reveal their parameters to their opponents in order to avoid exploitation. Several researchers have argued that an agent's optimal strategy can be determined using the opponent's deadline and reserve points. In this paper, we propose a new learning agent, so-called Evolutionary Learning Agent (ELA), able to estimate its opponent's deadline and reserve points in bilateral multi-issue negotiation based on opponent's counter-offers (without any additional extra information). ELA reduces the learning problem to a system of non-linear equations and uses an evolutionary algorithm based on the elitism aspect to solve it. Experimental study shows that our learning agent outperforms others agents by improving its outcome in term of average and joint utility.
PY - 2018
SP - 131
EP - 140
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - An Evolutionary Approach for Learning Opponent's Deadline and Reserve Points in Multi-Issue Negotiation
UR - http://www.ijimai.org/journal/sites/default/files/files/2018/08/ijimai_5_3_14_pdf_17519.pdf
VL - 5
SN - 1989-1660
ER -