01629nas a2200241   4500000000100000000000100001008004100002260001200043653003700055653001100092653002600103653002200129653003100151100001300182700001400195700001300209245010800222856009600330300001200426490000600438520092900444022001401373       2018                            d  c12/201810aDifferential Evolution Algorithm10aAgents10aAutomated Negotiation10aDeadline Learning10aInvasive Weed Optimization1 aR Ayachi1 aH Bouhani1 aBen Amor00aAn Evolutionary Approach for Learning Opponent's Deadline and Reserve Points in Multi-Issue Negotiation  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/08/ijimai_5_3_14_pdf_17519.pdf  a131-1400 v53 aThe 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.  a1989-1660