automated negotiation agents in modeling gaussian bidders

The purpose of automated negotiations, as a novel field of study in artificial intelligence, is focused on autonomous agents that can appear as humans’ intelligent representatives, attend negotiations with other agents, and attain acceptable outcomes. the so-called automated negotiating agents are implemented such that they can beat as many opponents as possible in different kinds of domains. like what happens in our daily negotiations, agents in automated negotiations do not reveal their preferences explicitly. numerous research studies have heretofore accentuated that an opponent model would be a great salvation to reduce this uncertainty, since it can be of much assistance in making wiser decisions in the next steps, reaching ideal eventual utility, and more satisfaction, accordingly. although most opponents in our world have single-peaked preferences, the functionality of negotiating agents in modeling single-peaked opponents has not been studied. gaussian agents are one important sort of single-peaked agents that utilize the gaussian function to ascribe the ranking of each negotiation item. the gaussian opponent’s bliss point estimation is of high importance during a negotiation. therefore, we first proposed a variety of gaussian bidding agents and then focused on how accurately automated negotiating agents competition (anac) attendees during 2010-2019 would model these bidder agents. the results of our experiments revealed that existing anac agents are performing well regarding individual utility and social welfare on average, but they are poor in modeling gaussian negotiating bidding agents.