Articles
Information (Switzerland) (20782489)15(8)
During automated negotiations, intelligent software agents act based on the preferences of their proprietors, interdicting direct preference exposure. The agent can be armed with a component of an opponent’s modeling features to reduce the uncertainty in the negotiation, but how negotiating agents with a single-peaked preference direct our attention has not been considered. Here, we first investigate the proper representation of single-peaked preferences and implementation of single-peaked agents within bidder agents using different instances of general single-peaked functions. We evaluate the modeling of single-peaked preferences and bidders in automated negotiating agents. Through experiments, we reveal that most of the opponent models can model our benchmark single-peaked agents with similar efficiencies. However, the accuracies differ among the models and in different rival batches. The perceptron-based P1 model obtained the highest accuracy, and the frequency-based model Randomdance outperformed the other competitors in most other performance measures. © 2024 by the authors.
AUT Journal of Modeling and Simulation (25882953)55(1)pp. 3-16
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. © 2023, Amirkabir University of Technology. All rights reserved.
Socio-Economic Planning Sciences (00380121)86
Increased competitions for water resources in many regions worldwide call for cooperative approaches. The competitions are complex for humans to resolve due to numerous alternatives and different or conflicting preferences of multiple stakeholders over multiple criteria, which might even oppose desirable environmental objectives. Parties also have incomplete information about the preferences of the counterparties. Electronic negotiation, empowered by intelligent agent technology, is a combination of artificial intelligence, economics, and psychology to find beneficial joint agreements in complex paradigms such as this. This study investigates a multilateral sustainable automated negotiation among intelligent agents representing stakeholders, including the legal party ‘nature’ as one of the stakeholders. It defines decision criteria and alternatives in the framework of cultural factors, elicits preferences of the stakeholders regarding the criteria without their intervention using a multi-criteria decision-making method, prunes the solution space before starting the negotiation by recognizing a general social treaty, determines the multi-issue specific treaty by learning the stakeholders, and demonstrates bidding and acceptance strategies. © 2022 Elsevier Ltd
Applied Intelligence (0924669X)52(4)pp. 3587-3603
In automatic negotiation, intelligent agents try to reach the best deal possible on behalf of their owners. In previous studies, opponent modeling of a negotiator agent has been used to tune the final bid out of a group of bids chosen by the agent’s strategy. In this research, a time-based bidding strategy has been introduced, which uses the opponent model to concede more adaptively to the opponents, thereby achieving an improved utility, social welfare, and fairness for the agent. By modeling the preference profile of the opponent during the negotiation session, this strategy sets its concession factor proportional to the model. Experiments show that in comparison to state-of-the-art agents, this agent makes better agreements in terms of individual utility and social welfare in small and medium-sized domains and can, in some cases, increase the performance up to 10%. The proposed agent successfully gets the deal up to 37% closer to best social bids in terms of distance to the Pareto frontier and the Nash point. An implementation based on the proposed strategy was used in an agent called AgreeableAgent, which participated in the international ANAC 2018 and won first place in individual utility rankings. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
