مرتب سازی بر اساس: سال انتشار
(نزولی)
Information (Switzerland) (20782489) (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) (1)
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)
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) (4)
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.
Studies in Computational Intelligence (1860949X)
Determining required conditions for accepting a bid has an important role in efficient negotiations. Conceding (aggressive) acceptance strategy of the agent against a conceding (an aggressive) opponent can lead to early and high utility agreement (losing the utility) especially in domains with discount factor. The literature is more focused on bilateral than multilateral negotiations, where they assume cost or utility function of the opponents or the historical data among the negotiation sessions are known to the agents. In this study, an effective acceptance strategy is proposed which only assumes the opponents’ rationality. The strategy extends the ParsAgent last moment concession strategy by developing an opponent model using k-means. The performance of the strategy is investigated against different combination of opponents consisting state-of-the-art strategies from Automated Negotiating Agents Competition (ANAC 2015 and 2016) in trilateral tournaments in different domains. The evaluation results indicate the superiority of proposed agent against the opponents in all domains, in average. © 2021, Springer Nature Singapore Pte Ltd.
Energy Proceedings (20042965)
Due to the importance of hybrid renewable energies for green power plants, strategies are required to make the market competitive and encourage consumers to admit to using such still less available electricity compared to the power generated from fossil fuels. Promotion-based group-buying tariffs are a selling marketing tool that can be adapted for this purpose. Energy producers and consumers can express their preferences regarding hybrid renewable energies through multiple attributes and values in a conditional manner, a lexicographic representation. In this paradigm, "what to buy" and "who else might incline to buy this," is a challenging issue for a group of consumers to make a single purchase decision. To this end, an HRECS and a PLPSim method are proposed to group consumers having the most similar lexicographic preferences for purchasing the most appropriate supplier tariff. The evaluation results demonstrate that HRECS using PLPSim outperforms the existing PLPDis method regarding Normal Discounted Cumulative Gain (nDCG) as well as intra-and inter-group Davies-Bouldin dispersion. © 2021 ICAE.
Studies in Computational Intelligence (1860949X)
We propose our Pars agent, one of the ANAC 2015 winners in multilateral negotiation tournaments. In this challenge, any agreement is made through acceptances issued by all parties involved in a trilateral negotiation. Pars agent uses a hybrid bidding strategy that combines behaviors of time-dependent, random and frequency-based strategies to propose a high utility offer close to the opponents bids and to increase the possibility of early agreement. © Springer International Publishing AG 2017.
IJCAI International Joint Conference on Artificial Intelligence (10450823)
In automated bilateral multi issue negotiations, two intelligent automated agents negotiate on behalf of their owners over many issues in order to reach an agreement. Modeling the opponent can excessively boost the performance of the agents and increase the quality of the negotiation outcome. State of the art models accomplish this by considering some assumptions about the opponent which restricts their applicability in real scenarios. In this paper, a less restricted technique (POPPONENT) is proposed, where perceptron units are applied in modelling the preferences of the opponent. This model adopts a Multi Bipartite version of the Standard Gradient Descent search algorithm (MBGD) to find the best hypothesis, which is the best preference profile. In order to evaluate the accuracy and performance of this proposed opponent model, it is compared with the state of the art models available in the Genius repository and in the devised setting. The results approve the higher accuracy of POPPONENT compared to the most accurate state of the art model. Evaluating the model in the real world negotiation scenarios in the Genius framework also confirms its high accuracy in relation to the state of the art models in estimating the utility of offers. The findings here indicate that the proposed model is individually and socially efficient. The proposed MBGD method could also be adopted in similar practical areas of Artificial Intelligence.
Studies in Computational Intelligence (1860949X)
In this study, we propose BraveCat agent, one of the ANAC 2014 finalists. The main challenge of ANAC 2014 was dealing with nonlinear utility scenarios and ultra large-size domains. Since the conventional frequency and Bayesian opponent models cannot be used to model the unknown complex nonlinear utility space or preference profile of the opponent in ultra large domains, we design a new distance based opponent model to estimate the utility of a candidate bid to be sent to the opponent in each round of the negotiation. Moreover, by using iterative deepening search, BraveCat overcomes the limitations imposed by the huge amount of memory needed in the ultra large domains. It also uses a hybrid bidding strategy that combines behaviors of time dependent, random, and imitative strategies. © Springer International Publishing Switzerland 2016.
The Arabian Journal For Science And Engineering (2193567X) (2)
Delay and capacity are two important parameters in mobile ad hoc networks (MANETs). Increasing the network capacity almost leads to delay increases, as well. Many recent works have been conducted to achieve both desirable capacity and delay, simultaneously. To achieve such aim, this study proposes a new reactive routing algorithm. This algorithm modifies multi-hop Dynamic Virtual Router algorithm to overcome the performance limits of MANETs. Mobility metrics are defined to estimate the mobility degree of the nodes’ neighborhood. A new route setup process is defined; using the estimated information and a local repair mechanism is also introduced in the new proposed algorithm. In this local repair mechanism, a new route is sought between the repairing node and its next hop on the communication path. Simulation study shows that the proposed algorithm significantly improves the network performance, including throughput and delay; so that, the increasing overhead is not remarkable considering the great performance improvement of the algorithm. © 2014, King Fahd University of Petroleum and Minerals.
Expert Systems with Applications (09574174) 42(6)pp. 3268-3295
In a multi-attribute combinatorial double auction (MACDA), sellers and buyers' preferences over multiple synergetic goods are best satisfied. In recent studies in MACDA, it is typically assumed that bidders must know the desired combination (and quantity) of items and the bundle price. They do not address a package combination which is the most desirable to a bidder. This study presents a new packaging model called multi-attribute combinatorial bidding (MACBID) strategy and it is used for an agent in either sellers or buyers side of MACDA. To find the combination (and quantities) of the items and the total price which best satisfy the bidder's need, the model considers bidder's personality, multi-unit trading item set, and preferences as well as market situation. The proposed strategy is an extension to Markowitz Modern Portfolio Theory (MPT) and Five Factor Model (FFM) of Personality. We use mkNN learning algorithm and Multi-Attribute Utility Theory (MAUT) to devise a personality-based multi-attribute combinatorial bid. A test-bed (MACDATS) is developed for evaluating MACBID. This test suite provides algorithms for generating stereotypical artificial market data as well as personality, preferences and item sets of bidders. Simulation results show that the success probability of the MACBID's proposed bundle for selling and buying item sets are on average 50% higher and error in valuation of package attributes is 5% lower than other strategies. © 2014 Elsevier Ltd. All rights reserved.
International Journal Of Information Science And Management (20088302) (SPL.ISSUE1)
Nowadays, the Internet has had a rapid expansion and presence in all areas of the individuals, institutions, companies and organizations lives. This has made large extent of data available so that the users need to access their desired information or services through using search engines. To continue and promote providing services, search engines require a source of income. Sponsored search advertising is the main source of revenue for search engines. In sponsored search, limited areas of the search results page are dedicated to display advertising so that the advertiser pays the advertisement cost only per click. To assign advertising areas, an auction is run among advertisers. Several models have been proposed to hold a sponsored search auction. In this study, a new model is proposed to increase the revenue of the search engine. Evaluated using synthetic data, the results of the ad-supported implementation of the search engine model indicate improved revenue in comparison to existing models.
Computers in Human Behavior (07475632)
Recently, websites employ online guides to help the users exploring required materials and information. The guides are presented through exchanging online questions and answers. For a foreign language visitor, website tour guides not only need to provide background and justification for the argument, but also they are better to translate the interaction. This paper presents an automated and intelligent software agent that can answer the questions logically. Although people can somehow simply reason and argument in their daily life, the nature of the humans' reasoning is generally complex and nontrivial. To make the inference and reasoning automated, the agent is armed with first-order logic in artificial intelligence. This enables the agent to understand and answer questions. Implementation of the complex process and the results are shown through a simple example. In addition, to make the agent more trustable and user-friendly, the intermediary inference and justification steps are translated in the user's language. © 2014 Elsevier Ltd. All rights reserved.
Auctions have been as a competitive method of buying and selling valuable or rare items for a long time. Single-sided auctions in which participants negotiate on a single attribute (e.g. price) are very popular. Double auctions and negotiation on multiple attributes create more advantages compared to single-sided and single-attribute auctions. Nonetheless, this adds the complexity of the auction. Any auction mechanism needs to be budget balanced, Pareto optimal, individually rational, and coalition-proof. Satisfying all these properties is not so much trivial so that no multi-attribute double auction mechanism could address all these limitations. This research analyzes and compares the GM, timestamp-based and social-welfare maximization mechanisms for multi-attribute double auctions. The analysis of the simulation results shows that the algorithm proposed by Gimple and Makio satisfies more properties compared to other methods for such an auction mechanism. This multi-attribute double auction mechanism is based on game theory and behaves fairer in matching and arbitration. © 2013 IEEE.
Combinatorial auctions are auctions in which bidders bid on combinations of items, bundles, instead of on individual items. In these auctions, bidders always tend to construct and bid on the most beneficial bundles of items, while facing a substantial number of items. Since there are a huge number of items available in a combinatorial auction, deciding on which items to put in bundles is a challenge for bidders. In combinatorial auctions, bundling of items and bidding on the best possible bundles are of great importance and developing an efficient bidding strategy can increase quality of the auctions considerably. In this paper, we have proposed an efficient bidding strategy. Performance of the proposed strategy in various markets has been simulated and compared with the bidding strategies already available in the literature. The obtained results show that in comparison with the previously available bidding strategies, the proposed strategy is more beneficial to both bidders and auctioneer, especially in markets where there is a considerable difference between values of items. © 2011 IEEE.
International Journal of Human Computer Studies (10959300) 67(1)pp. 1-35
Our everyday lives and specially our commercial transactions involve complex negotiations that incorporate decision-making in a multi-issue setting under utility constraints. Negotiation as a key stage in all commercial transactions has been proliferated by applying decision support facilities that AI techniques provide. Recently, Distributed Artificial Intelligence techniques have been evolved towards multi-agent systems (MASs) where each agent is an intelligent system that solves a specific problem. Incorporating MAS into e-commerce negotiation and bargaining has brought even more potential improvement in efficiency and effectiveness of business systems by automating several of the most time consuming and repetitive stages of the buying process. In bargaining, participants with opposing interests communicate and try to find mutually beneficial agreements by exchanging compromising proposals. However, recent studies on commercial bargaining and negotiation in MASs lack a personality model. Indeed, adding personality to intelligent agents makes them more human-like and increases their flexibility. We investigate the role of personality behaviors of participants in multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. We do not aim to determine strategies that humans should use in negotiation, but to present a more human-like model to enhance the realism of rational bargaining behavior in MASs. First, this study presents a computational approach based on a heuristic bargaining protocol and a personality model, and second, considers the issue of what personality traits and behaviors should be investigated in relation to automated negotiations. We show the results obtained via the simulation on artificial stereotypes. The results suggest and model compound personality style behaviors appropriate to gain the best overall utility in the role of buyer and seller agents and with regard to social welfare and market activeness. This personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in appropriate situations in many areas involving negotiation and bargaining (e.g., commerce, business, politics, military, etc.) for conflict prevention and resolution. This model can be applied as a testbed for comparing personality models against each other based on human data in different negotiation domains. © 2008 Elsevier Ltd. All rights reserved.
Information Sciences (00200255) 179(3)pp. 248-266
In recent years, several techniques have been proposed to model electronic promotions for existing customers. However, these techniques are not applicable for new customers with no previous profile or behavior data. This study models promotions to new customers in an electronic marketplace. We introduce a multi-valued k-Nearest Neighbor (mkNN) learning capability for modeling promotions to new customers. In this modified learning algorithm, instead of a single product category, the seller sends the new customer a promotion on a variable set of m categories (where m is a variable) with the highest rank of desirability among the most similar previous customers. Previous studies consider sellers' profits in promotion and marketing models. In addition to the sellers' profits, three important factors - annoyance of customers, sellers' reputations, and customers' anonymity - are considered in this study. Without considering the customer's profile, we minimize unrelated and disliked offers to reduce the customer's annoyance and elevate the seller's reputation. The promotion models are evaluated in two separate experiments on populations with different degrees of optimism: (1) with fixed number of customers; and (2) in a fixed period of time. The evaluation is based on the parameters of customer population size and behavior as well as time interval, seller payoff, seller reputation, and the number of promotions canceled by the customers. The simulation results demonstrate that the proposed mkNN-based promotion strategies are moderately efficient with respect to all parameters for providing services in a large population. In addition, purchasing preferences of past customers, which are based on periodic promotions that a seller sends to customers, can generate future rapidly expanding demands in the market. By using these approaches, an advertising company can send acceptable promotions to customers without having specific profile information. © 2008 Elsevier Inc. All rights reserved.
Simulation and Gaming (1552826X) 39(1)pp. 83-100
Distributed Artificial Intelligence techniques have evolved toward multi-agent systems (MASs) where agents solve specific problems. Bargaining is a challenging area well-explored in both MAS and economics. To make agents more human-like and to increase their flexibility to reach an agreement, the authors investigated the role of personality behaviors of participants in a multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. The authors simulate a computational approach based on a heuristic bargaining protocol and personality model on artificial stereotypes. The results suggest compound behaviors appropriate to gain the best overall utility in the role of buyer and seller and with regard to social welfare and market activeness. This generic personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in areas involving negotiation and bargaining. © 2008 Sage Publications.
The bargaining problem in two-person games is selecting a particular point (i.e., an equilibrium) in the utility set to reach a jointly optimal result. Nonetheless, there are games with no or even more than one equilibrium, where in non-zero-sum games with multiple equilibria, all equilibria do not necessarily result in the same utility set. Each player not only should play with her/his equilibrium strategies, but also (s)he would be better to select the strategy that leads her/him to a better utility than the other equilibria. We present a BNE approach consisting of three parts: a 2-layer graph representation of the game that encompasses both strategic and extensive representations; a game reduction method where all Nash Equilibria remain unchanged; and locating the best NE strategy (which results in the best social welfare among all NEs) to start with, along with determining the first mover. The study shows the computationally correctness of the approach in well-known 2×2 and different sizes of sample 2-person games.
This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches. © 2021 Owner/Author.
The Internet of Things (IoT) enables smart Things to communicate via the Internet. Things are growing in number, and their need for multiple resources in a complementary manner engenders serious problems in resource allocation. Combinatorial Auctions (CA) are the optimal market mechanism for allocating such indivisible bundles. Since the abundance of bundles in the IoT market makes it impossible to bid on all bundles, Things express their preferences on some (and not all) bundles to make the winner determination amenable. We address the winner determination problem by proposing an allocation mechanism based on social choice methods, which operates on the number of requested resources, the number of bundles, the offered price, and the preferred weight of each bundle. These methods include Borda, Copeland, Average without Misery, Least Misery, and Hare. Finally, we demonstrate the evaluation of these methods in terms of execution time and envy-freeness among the Things. © 2023 IEEE.
For a long time, culture has been an influencing parameter in negotiations. Growth of international trades and business competitions has increased the importance of negotiations among countries and different cultures. Developing new technologies, particularly the use of artificial intelligence in electronic trading areas, has provided us with the application of intelligent agents to resolve challenges in e- negotiations. In this study, a model is developed and implemented to arm intelligent agents with time-sensitivity cultural parameter in negotiations in electronic commerce context. The seller's proposals are offered based on the estimated value of the buyers' time-sensitivity in delivering the products. It starts from the highest price which satisfies the buyer's time sensitivity. The simulations are based on the Salacuse's Cultural dataset related to five countries, Finland, Mexico, Turkey, India, and the United States of America. The negotiation algorithms were implemented in Java platform and MySQL database for both cases of with and without cultural differences in time sensitivity. The evaluation shows that the cultural-based model starts the negotiation from an offer close to the buyer's desire. This yields less number of rounds and total negotiation time period. The simulation results also show that the buyer's budget as an economic factor can be effective in the negotiation outcomes in some cases. © 2014 IEEE.
Automated negotiating agents are usually designed and implemented in a general way so that they can negotiate successfully in front of a vast variety of opponents. In the real world, most opponents are single-peaked. Gaussian agents that use such distribution function to rate the negotiation items are important sorts of such opponents. Modeling the opponents is of great importance since it enables us to adjust our next decisions accordingly. This can bring us short-time compromises, ideal eventual utility, more satisfaction, and so on. In negotiating with Gaussian opponents, the estimation of the opponent's peak point is the core. In this regard, we have paid particular attention to how accurate the existing automated agents attended in Automated Negotiating Agents Competition (ANAC) during 2010-2019 can model Gaussian bidders and showed the result of the experiments. © 2021 IEEE.
Feedback is an essential component of learning, as it helps students identify their strengths and weaknesses, and improve their performance. However, the students may not be able to understand how their work has been judged. One way to address this issue is to let the students assess and comment on the work of their peers, Peer Assessment (PA). PA has benefits such as enhancing learning outcomes, developing self-assess and critical thinking skills, and fostering collaboration. However, PA also poses some challenges such as ensuring fairness, anonymity, and reliability. In this study, we designed and implemented an anonymous electronic PA for a few classes participating EU-Iran STEM/UNITEL project. We used several digital tools to simulate a double-blind PA process, and a rubric based on the students' own criteria and weights. The grades collected from 70 students represent positive feedback. We discuss the functionality, advantages, and limitations of our approach. © 2024 IEEE.
Delay and capacity (throughput) are two important parameters to route data packets in Mobile Ad Hoc Networks (MANETs). In this paper, a new connectionless routing algorithm has been proposed to overcome the performance limit. The proposed algorithm is an extension of dynamic virtual route (DVR) algorithm. Mobility degree of nodes' neighborhood is used to calculate two mobility metrics. Mobility metrics are utilized to establish a more stable route between source and destination. Simulation study shows that the proposed algorithm can improve the network throughput and decrease average end-to-end delay significantly. © 2014 IEEE.
The 13th automated negotiation competition was held in 2 leagues (ANL2022 and SCML2022) in conjunction with the 31st IJCAI conference. The ANL for 2022 is bilateral negotiation under the SOAP protocol. Agents are allowed to learn from their previous negotiations. The agents could have 3 main BOA components: a Bidding strategy that decides which bid and when must be sent to the opponent, an Opponent model that tries to model the opponent's preferences, and an Acceptance strategy that decides whether to accept the opponent's offer or not. This paper explains our LuckyAgent2022's BOA components and its learning methods over negotiation sessions. To improve its utility over sessions, we propose SLM, a LSN Stop-Learning mechanism, to prevent overfitting by adapting it to a multi-armed bandit problem. It finds the best value for variables of a time-dependent bidding strategy for the opponent. © 2022 IEEE.
In any negotiation, one of the most important parts of the negotiator's task is deciding whether or not to accept the opponent's offer. Actually, the most challenging thing is answering this question: which offer and when must be accepted? A wide range of simple to sophisticated acceptance strategies have been proposed: simple acceptance strategies which have the constant threshold value and sophisticated strategies that notice both utility and time in order to determine acceptance thresholds. This study introduces a novel statistical acceptance strategy with considering the similarity between the opponent's offer and our previous offers, which is combined with existing usual acceptance strategies. Experiments show our strategy has advantages against the state-of-the-art acceptance strategies. © 2020 IEEE.
