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Journal articles on the topic 'Agent-Based negotiation'

Author: Grafiati

Published: 7 December 2024

Last updated: 19 July 2025

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1

Rajavel, Rajkumar, Sathish Kumar Ravichandran, and G. R. Kanagachidambaresan. "Agent-based cloud service negotiation architecture using similarity grouping approach." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 01 (2019): 1941015. http://dx.doi.org/10.1142/s0219691319410157.

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Challenges and issues in the field of cloud service negotiation framework optimization have been an active area of research. During service level agreement, the probability of negotiation conflict between the service consumers and providers is high. This may arise due to aggressive behavior, selfish misperception, vague preferences and uncertain goals of the negotiating participants. One of the key challenges identified in negotiation framework is optimizing the negotiation conflict among the negotiators. In order to minimize such conflicts, existing frameworks group the negotiation pairs that contain similar and non-aggressive behavioral patterns by exploiting the distance, binary, context dependent and fuzzy similarity approaches. These approaches get better success rate only if the dimensionality of negotiator attributes is low. As emerging real-time cloud service negotiation applications are characterized by negotiation attributes of high dimensionality, the existing approaches are inappropriate for these applications. In addition, the existing approaches group the negotiation pairs using distances based measure in two-dimensional negotiation attribute, whose value will vary for high-dimensional attributes. In this work, an Angle-based Similarity Grouping (ASG) approach is proposed that appropriately groups the highly cooperative negotiation pairs and thereby increases the success rate and decreases communication overhead.

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Wang, Gui He, Peng Cheng Su, Hu Li, and Wan Shan Wang. "A Multi-Agent-Based Automatic Negotiation for Distributed Manufacturing." Applied Mechanics and Materials 16-19 (October 2009): 941–45. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.941.

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The concept of agent and multi-agent was introduced. The agent structure, negotiation strategies, and the negotiation mechanism were also researched. Based multi-agent, an automated negotiation method for bilateral contracts is proposed, which can efficiently carry out multilateral negotiations with multi-attributes in distributed environments. This framework supports efficiently multilateral negotiation. In the end, the negotiation is conducted and demonstrated that multi-agent can improve negotiation efficiency by saving negotiation time and cost.

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Higa, Ryota, Katsuhide Fujita, Toki Takahashi, Takumu Shimizu, and Shinji Nakadai. "Reward-Based Negotiating Agent Strategies." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11569–77. http://dx.doi.org/10.1609/aaai.v37i10.26367.

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This study proposed a novel reward-based negotiating agent strategy using an issue-based represented deep policy network. We compared the negotiation strategies with reinforcement learning (RL) by the tournaments toward heuristics-based champion agents in multi-issue negotiation. A bilateral multi-issue negotiation in which the two agents exchange offers in turn was considered. Existing RL architectures for a negotiation strategy incorporate rich utility function that provides concrete information even though the rewards of RL are considered as generalized signals in practice. Additionally, in existing reinforcement learning architectures for negotiation strategies, both the issue-based representations of the negotiation problems and the policy network to improve the scalability of negotiation domains are yet to be considered. This study proposed a novel reward-based negotiation strategy through deep RL by considering an issue-based represented deep policy network for multi-issue negotiation. Comparative studies analyzed the significant properties of negotiation strategies with RL. The results revealed that the policy-based learning agents with issue-based representations achieved comparable or higher utility than the state-of-the-art baselines with RL and heuristics, especially in the large-sized domains. Additionally, negotiation strategies with RL based on the policy network can achieve agreements by effectively using each step.

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Takahashi, Toki, Ryota Higa, Katsuhide Fujita, and Shinji Nakadai. "VeNAS: Versatile Negotiating Agent Strategy via Deep Reinforcement Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13065–66. http://dx.doi.org/10.1609/aaai.v36i11.21669.

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Existing research in the field of automated negotiation considers a negotiation architecture in which some of the negotiation components are designed separately by reinforcement learning (RL), but comprehensive negotiation strategy design has not been achieved. In this study, we formulated an RL model based on a Markov decision process (MDP) for bilateral multi-issue negotiations. We propose a versatile negotiating agent that can effectively learn various negotiation strategies and domains through comprehensive strategies using deep RL. We show that the proposed method can achieve the same or better utility than existing negotiation agents.

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Lenar, Mateusz, and Janusz Sobecki. "Using Recommendation to Improve Negotiations in Agent-based Systems." JUCS - Journal of Universal Computer Science 13, no. (2) (2007): 267–86. https://doi.org/10.3217/jucs-013-02-0267.

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In this paper we present research works on non-intuitive and low-efficient negotiations between agents in agent based system. We find recommendation techniques as a suitable method of making negotiation smarter and more efficient. We have introduced into the negotiation thread the improvements in the form of hybrid recommendation. Hybrid recommendation is composed of three basic elements: demographic, collaborative and content-based. On the base of a negotiation algorithm we have studied how recommendation methods can improve the whole process of finding mutually acceptable agreements between agents. The proposed methodology is presented using a travel agency and its client negotiation.

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Park, Jinsoo, Hamirahanim Abdul Rahman, Jihae Suh, and Hazami Hussin. "A Study of Integrative Bargaining Model with Argumentation-Based Negotiation." Sustainability 11, no. 23 (2019): 6832. http://dx.doi.org/10.3390/su11236832.

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E-commerce is increasingly competitive and there is a constant need for new approaches and technology to facilitate exchange. Emerging techniques include the use of artificial intelligence (AI). One AI tool that has sparked interest in e-commerce is the automated negotiation agent (negotiation-agent). This study examines such agents, and proposes an offer strategy model of integrative negotiation for a negotiation-agent with a focus on negotiation agent-to-human interaction. More specifically, a new offer strategy was developed based on the integrative bargaining model, which emphasizes the importance of exchanging information among negotiators and multi-issue negotiation that includes package offers to achieve an integrative (win-win) outcome. This study incorporated an argumentation-based negotiation and the negotiation tactic of multiple equivalent simultaneous offers, which was programmed into the negotiation-agent. An experiment was conducted performing 49 negotiation-agent-to-human negotiations over three issues in online purchase tasks to demonstrate the effectiveness of the proposed strategy. Experimental results indicated that the proposed offer strategy with agent negotiation can enhance the persuasiveness of an offer and the performance of negotiation outcome (human counterpart’s perception toward negotiation process, opponent–agent and desire for future negotiation). The findings confirmed the effectiveness of the proposed design and demonstrated an innovative approach to e-commerce transactions.

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7

Lempp, Frieder. "A new agent-based simulation model of bilateral negotiation." International Journal of Conflict Management 31, no. 1 (2019): 115–48. http://dx.doi.org/10.1108/ijcma-07-2019-0118.

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Purpose The purpose of this paper is to introduce a new agent-based simulation model of bilateral negotiation based on a synthesis of established theories and empirical studies of negotiation research. The central units of the model are negotiators who pursue goals, have attributes (trust, assertiveness, cooperativeness, creativity, time, etc.) and perform actions (proposing and accepting offers, exchanging information, creating value, etc). Design/methodology/approach Methodologically, the model follows the agent-based approach to modeling. This approach is chosen because negotiations can be described as complex, non-linear systems involving autonomous agents (i.e. the negotiators), who interact with each other, pursue goals and perform actions aimed at achieving their goals. Findings This paper illustrates how the model can simulate experiments involving variables such as negotiation strategy, creativity, reservation value or time in negotiation. An example simulation is presented which investigates the main and interaction effects of negotiators’ reservation value and their time available for a negotiation. A software implementation of the model is freely accessible at https://tinyurl.com/y7oj6jo8. Research limitations/implications The model, as developed at this point, provides the basis for future research projects. One project could address the representation of emotions and their impact on the process and outcome of negotiations. Another project could extend the model by allowing negotiators to convey false information (i.e. to bluff). Yet another project could be aimed at refining the routines used for making and accepting offers with a view to allow parties to reach partial settlements during a negotiation. Practical implications Due to its broad scope and wide applicability, the model can be used by practitioners and researchers alike. As a decision-support system, the model allows users to simulate negotiation situations and estimate the likelihood of negotiation outcomes. As a research platform, it can generate simulation data in a cost- and time-effective way, allowing researchers to simulate complex, large-N studies at no cost or time. Originality/value The model presented in this paper synthesizes in a novel way a comprehensive range of concepts and theories of current negotiation research. It complements other computational models, in that it can simulate a more diverse range of negotiation strategies (distributive, integrative and compromise) and is applicable to a greater variety of negotiation scenarios.

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Mozahem, Najib Ali. "Always negotiate, sometimes cooperate: an agent-based model." International Journal of Organization Theory & Behavior 22, no. 4 (2019): 331–49. http://dx.doi.org/10.1108/ijotb-07-2018-0078.

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Purpose The purpose of this paper is to use agent-based modeling to simulate the negotiation and cooperation between agents working on tasks in an organization and to study the effects that gender differences might have on the outcome of the process. Design/methodology/approach The model used herein allows for idiosyncratic differences in terms of the propensity to negotiate/cooperate. The model also allows for multi-round negotiations/cooperation and incorporates subjective value into the negotiation process. The model is implemented in NetLogo. Findings The results clearly show that it is always beneficial to negotiate, even when backlash might result from the request. The study then extends this analysis by allowing for gender differences in both negotiation and cooperation. The results provide strong support for the hypothesis that agentic characteristics are beneficial for negotiators, while communal characteristics can be detrimental. Research limitations/implications Like all models, the model used herein made some simplifying assumptions about the negotiation and cooperation processes. In addition, the utilized model assumes that agents work individually on tasks and that negotiation takes place between two individual agents, even though negotiation can be a team-based endeavor in many cases. Practical implications The results of this study indicate that individuals need to adopt characteristics that are more agentic; this finding is particularly true for females who aim to be on a level playing field with their male counterparts. The results also indicate that negotiation is beneficial whether there is an abundance of resources or not, while cooperation is only beneficial when resources are abundant. Originality/value While past negotiation research has used simple choice games, laboratory studies and field studies, this study provides computational support for the hypothesis that higher levels of negotiation are beneficial to individuals. Additionally, unlike recent agent-based studies that have studied negotiation as a taken-for-granted automated computational process that is done by software on behalf of individuals, the present study simulates agents that have yet to decide whether they will engage in negotiation or not.

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Zhang, Ya, Jinghua Wu, and Ruiyang Cao. "Optimizing Automated Negotiation: Integrating Opponent Modeling with Reinforcement Learning for Strategy Enhancement." Mathematics 13, no. 4 (2025): 679. https://doi.org/10.3390/math13040679.

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Agent-based automated negotiation aims to enhance decision-making processes by predefining negotiation rules, strategies, and objectives to achieve mutually acceptable agreements. However, most existing research primarily focuses on modeling the formal negotiation phase, while neglecting the critical role of opponent analysis during the pre-negotiation stage. Additionally, the impact of opponent selection and classification on strategy formulation is often overlooked. To address these gaps, we propose a novel automated negotiation framework that enables the agent to use reinforcement learning, enhanced by opponent modeling, for strategy optimization during the negotiation stage. Firstly, we analyze the node and network topology characteristics within an agent-based relational network to uncover the potential strength and types of relationships between negotiating parties. Then, these analysis results are used to inform strategy adjustments through reinforcement learning, where different negotiation strategies are selected based on the opponent’s profile. Specifically, agents’ expectations are adjusted according to relationship strength, ensuring that the expectations of negotiating parties are accurately represented across varying levels of relationship strength. Meanwhile, the relationship classification results are used to adjust the discount factor within a Q-learning negotiation algorithm. Finally, we conducted a series of experiments, and comparative analysis demonstrates that our proposed model outperforms existing negotiation frameworks in terms of negotiation efficiency, utility, and fairness.

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Rajkumar Rajavel, Rajkumar Rajavel, Dinesh Komarasamy Rajkumar Rajavel, Iyapparaja Meenakshisundaram Dinesh Komarasamy, Katarina Gubiniova Iyapparaja Meenakshisundaram, and Celestine Iwendi Katarina Gubiniova. "Cognitive Fuzzy-based Behavioral Learning System for Augmenting the Automated Multi-issue Negotiation in the E-commerce Applications." 網際網路技術學刊 23, no. 6 (2022): 1335–42. http://dx.doi.org/10.53106/160792642022112306016.

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<p>Evolution of agent-based technology presents behavioral learning and sustainable negotiation challenges in e-commerce applications. In particular, the challenge of designing the negotiation strategy to incorporate sustainability in e-commerce business that can leverage the agent to reach its objectives by increasing the negotiation coordination and cooperation with the opponent agents. Therefore, the proposed research introduces the negotiation strategy sustainable solution using a cognitive fuzzy-based behavioral learning system which can change the preferences of negotiating agents according to human psychological characteristics. It will mimic the attitudes of human risk, patience and regret during the course of bilateral negotiation and also change the preference structures according to the fuzzy logic rules. As a result, the proposed negotiation strategy makes significant improvements on various parameters such as utility value, success rate, total negotiation time, and communication overhead while changing the negotiation rounds from 50 to 500. Since this system leverages the negotiation strategy of the agent by taking appropriate decisions to reach better agreement based on the interest, belief and psychological characteristics of negotiating opponents. Moreover, the usage of negotiation in the cloud-based platform can leverage the e-commerce applications to handle as many requests as possible due to its dynamic elasticity.</p> <p>&nbsp;</p>

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Agrawal, Manish, and Kaushal Chari. "Negotiation Behaviors in Agent-Based Negotiation Support Systems." International Journal of Intelligent Information Technologies 5, no. 1 (2009): 1–23. http://dx.doi.org/10.4018/jiit.2009010101.

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12

Yang, Changhui, Ruixia Yang, Tingting Xu, and Yinxia Li. "Negotiation model and tactics of manufacturing enterprise supply chain based on multi-agent." Advances in Mechanical Engineering 10, no. 7 (2018): 168781401878362. http://dx.doi.org/10.1177/1687814018783625.

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The negotiation between agent and multi-agent is an important method for solving the conflict and realizing the cooperation in the multi-agent system of manufacturing enterprise supply chain. In this article, we will take manufacturing enterprise supply chain as the research object, analyze negotiation process of multi-agent, study the negotiating model and tactics as well as the steps, and then an illustration is discussed for validating the negotiation model. The research can provide theoretical and operational methods for manufacturing enterprise supply chain management, is helpful to coordinate and control the manufacturing enterprise supply chain, and realize the efficient, flexible, and quick operation of manufacturing enterprise supply chain.

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Aydoğan, Reyhan, Özgür Kafali, Furkan Arslan, Catholijn M. Jonker, and Munindar P. Singh. "Nova : Value-based Negotiation of Norms." ACM Transactions on Intelligent Systems and Technology 12, no. 4 (2021): 1–29. http://dx.doi.org/10.1145/3465054.

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Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p -value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p -value and large effect size in particular settings) toward reaching an agreement.

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Lin, Menq-Wen, K. Robert Lai, and Ting-Jung Yu. "Fuzzy Constraint-Based Agent Negotiation." Journal of Computer Science and Technology 20, no. 3 (2005): 319–30. http://dx.doi.org/10.1007/s11390-005-0319-3.

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Bai, S., W. Raskob, and T. Müller. "Agent based model." Radioprotection 55 (May 2020): S187—S191. http://dx.doi.org/10.1051/radiopro/2020031.

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In the CONFIDENCE project, we developed an agent based model (ABM) to simulate the decision making process involving stakeholders of different interests. Our model aims to support decisions on the most suitable protection strategies in different accident phases. The intelligent agents and the models of the negotiation/voting process are described in the paper. Given five scenarios, the numerical results from the computational implementation of the ABM are visualized and analysed in order to better understand the negotiation and voting processes. Our ABM can be expanded in order to support the decision making processes of many different stakeholders of various types of risk management apart from nuclear and radiological emergency management.

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Fujita, Katsuhide, Takayuki Ito, and Mark Klein. "Common Testbed Generating Tool Based on XML for Multiple Interdependent Issues Negotiation Problems." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 1 (2011): 34–40. http://dx.doi.org/10.20965/jaciii.2011.p0034.

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Multiple interdependent issues negotiations have been widely studied since most real-world negotiation involves multiple interdependent issues. Our work focuses on negotiation with multiple interdependent issues in which agent utility functions are nonlinear. In the field of multiple issue negotiations, there are no established common testbeds for evaluating protocols. In this paper, we propose a common testbed creating tool based on XML that mainly covers the utility functions based on cube-constraints and cone-constraints. First, we propose a testbed generating tool that inputs configuration data and outputs XML formatted files that represent agent utility spaces. The current tool can produce four types of utility spaces: Random, A Single Hill, Two-Hills, and SeveralHills. These types are observed in real negotiation settings. Also we define the agent’s utility space information based on XML formats. By defining the testbed data as XMLs, users can easily read the files and change the data structure.

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Jeong, Yun-Duk. "Strategies for Successful Sports Agent Negotiations Based on Business Negotiation Theory." Korea Journal of Sport 22, no. 1 (2024): 697–709. http://dx.doi.org/10.46669/kss.2024.22.1.061.

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Garruzzo, Salvatore, and Domenico Rosaci. "Agent clustering based on semantic negotiation." ACM Transactions on Autonomous and Adaptive Systems 3, no. 2 (2008): 1–40. http://dx.doi.org/10.1145/1352789.1352792.

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Wang, Kung-Jeng, and Agustina Eunike. "Negotiation-based scheduling considering agent emotion." Expert Systems with Applications 255 (December 2024): 124905. http://dx.doi.org/10.1016/j.eswa.2024.124905.

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Choi, Samuel P. M., Jiming Liu, and Sheung-Ping Chan. "A genetic agent-based negotiation system." Computer Networks 37, no. 2 (2001): 195–204. http://dx.doi.org/10.1016/s1389-1286(01)00215-8.

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Ren, Wu Ling, and Wei Luo. "An Agent-Based Auto-Negotiation Mechanism with Semantic Web Services." Applied Mechanics and Materials 155-156 (February 2012): 411–15. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.411.

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The lack of semantic and effective negotiation mechanisms, suppliers and consumers in network manufacturing services are difficult to establish satisfactory service relationships. To solve this problem, an agent-based auto-negotiation mechanism with semantic Web Services was proposed. An agent negotiation model based on the manufacturing services ontology was established. With this model, the major procedures for agent-based auto-negotiation were described, which include how to match the services suppliers and consumers, how to process semantic similarity calculation, and how to auto-negotiation, etc.

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ENEMBRECK, FABRÍCIO, CESAR AUGUSTO TACLA, and JEAN-PAUL BARTHÈS. "LEARNING NEGOTIATION POLICIES USING ENSEMBLE-BASED DRIFT DETECTION TECHNIQUES." International Journal on Artificial Intelligence Tools 18, no. 02 (2009): 173–96. http://dx.doi.org/10.1142/s021821300900010x.

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In this work we compare drift detection techniques and we show how they can improve the performance of trade agents in multi-issue bilateral dynamic negotiations. In a dynamic negotiation the utility values and functions of trade agents can change on the fly. Intelligent trade agents must identify and take such drift in the competitors into account changing also the offer policies to improve the global utility throughout the negotiation. However, traditional learning mechanisms disregard possible changes in a competitor's offer/counter-offer policy. In that case, the agent performance may decrease drastically. In our approach, a trade agent has a staff of weighted learner agents used to predict interesting offers. The staff uses the Dynamic Weighted Majority (DWM) algorithm to adapt itself creating, deleting and adapting staff members. The results obtained with the IB3 (Instance-based) learners and IB3-DWM learners show that ensemble methods like DWM are suitable for correctly identifying changes in agent negotiations.

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Ramadhan, Fakhri Ihsan, and Meditya Wasesa. "Agent-based Truck Appointment System for Containers Pick-up Time Negotiation." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 1 (2020): 81. http://dx.doi.org/10.22146/ijccs.51274.

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Congestion in the seaports area is a common issue in many parts of the world. Fluctuating truck arrival has been identified as one of the significant determinants of congestion. In response, a truck appointment system (TAS) is introduced to manage truck arrival, particularly at peak times. In the existing TAS mechanism, the scheduling decision is centralized and disregards the concerns of trucking companies. Moreover, TAS may complicate the business operation of trucking companies that already have a constrained truck schedule. This study proposes a decentralized negotiation mechanism in TAS that allows trucking companies to adjust arrival times by utilizing the waiting time estimation provided by the terminal operator. We develop an agent-based model of a TAS in the container terminal pick-up procedure. The simulation results indicate that compared to the existing TAS mechanism, the negotiation TAS mechanism generates a shorter average truck turnaround time regardless of truck arrival rates. In terms of average net time cost, the negotiation TAS mechanism provides better value under high truck arrival rate conditions. The incentive for trucking companies to participate in the negotiations is even higher at peak times.

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KURBEL, KARL, and IOURI LOUTCHKO. "Towards multi-agent electronic marketplaces: what is there and what is missing?" Knowledge Engineering Review 18, no. 1 (2003): 33–46. http://dx.doi.org/10.1017/s0269888902000516.

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This paper gives an overview of electronic marketplaces which employ agent technology or similar techniques. Special attention is given to negotiation issues. A classification scheme for competitive negotiation depending on the type of the marketplace (business-to-business, business-to-consumer and consumer-to-consumer) and on the range of players (1:1, 1:n and n:m) is introduced. Furthermore, we distinguish between one-issue and multi-issue negotiation, and we consider crisp and fuzzy constraints on either negotiating side. Several existing electronic marketplaces and prototypes of marketplaces are discussed with respect to the classification schema. Our analysis shows that agent-based e-marketplaces are a promising area of e-commerce but they are still far away from real marketplaces. In conclusion we formulate some challenges for further research in the field of multi-agent electronic marketplaces with negotiation between agents.

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Zhang, Qingping, and Yi He. "The Machine Learning Algorithms in Supply Chain Coordination Simulation and Optimization." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3603–13. http://dx.doi.org/10.12694/scpe.v25i5.3184.

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In response to the current situation of poor adaptive learning performance in Agent production and sales negotiation and dynamic changes in negotiation environment, the author proposes a method based on machine learning algorithms. Consider the impact of conflict level, cooperation possibility, and negotiation remaining time on negotiations in a dynamic negotiation environment, and use the entropy method to determine the weights of three influencing factors and perform linear weighting. Based on the differences in current negotiation topics, a concession amplitude prediction model based on dynamic selective ensemble learning is constructed, and an optimization strategy for supply chain production and sales negotiation is proposed. The experimental results indicate that, in the adaptive negotiation strategy of a regular SVM single learning machine, the joint utility of the most successfully negotiated agents falls within the interval [0.55, 0.70], while the author’s ensemble learning strategy mainly focuses on [0.6, 0.8], the author’s strategy is relatively superior to ordinary learning strategies in terms of both the number of successfully negotiated agents and the joint utility. Compared with the single learning machine negotiation strategy, this strategy improves the success rate and joint utility of Agent adaptive learning, and ensures the benefits of both production and sales in the supply chain, achieving a mutually beneficial situation for both parties in cooperation.

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Bădică, Amelia, and Costin Bădică. "Formalizing Agent-Based English Auctions Using Finite State Process Algebra." JUCS - Journal of Universal Computer Science 14, no. (7) (2008): 1118–35. https://doi.org/10.3217/jucs-014-07-1118.

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The vision of global agent-based e-commerce environments that enable dy-namic trading between business partners requires the study and development of suitable formal modeling frameworks. In particular, negotiation is a necessary and importantactivity to allow engagement of business parties in non-trivial business relationships. In this paper we propose a formal framework based on finite state process algebra formodeling and analysis of interaction protocols in agent-based negotiations. The approach is demonstrated by applying the framework to model agent interactions in asingle-item English auction scenario.

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Wang, Gong, T. N. Wong, and Chunxia Yu. "Computational method for agent-based E-commerce negotiations with adaptive negotiation behaviors." Procedia Computer Science 4 (2011): 1834–43. http://dx.doi.org/10.1016/j.procs.2011.04.199.

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Matsumura, Koki. "Negotiation Support Agent Based on Immune Algorithm." IEEJ Transactions on Electronics, Information and Systems 119, no. 10 (1999): 1164–74. http://dx.doi.org/10.1541/ieejeiss1987.119.10_1164.

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Lang, Florian, and Freimut Bodendorf. "Automated Negotiation in Agent Based Commercial Grids." Journal of Decision Systems 15, no. 1 (2006): 55–81. http://dx.doi.org/10.3166/jds.15.55-81.

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Rabelo, Ricardo J., and L. M. Camarinha-Matos. "Negotiation in multi-agent based dynamic scheduling." Robotics and Computer-Integrated Manufacturing 11, no. 4 (1994): 303–9. http://dx.doi.org/10.1016/0736-5845(95)00007-0.

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Zarour, Nacer eddine, and Sabrina Bouzidi. "Coalition Formation for Cooperative Information Agent-Based Systems." International Journal of Computers Communications & Control 1, no. 3 (2006): 85. http://dx.doi.org/10.15837/ijccc.2006.3.2297.

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The communication technology evolution led to an increase of the carried out services’ and tasks’ number. The aim of actual research in the cooperation and particularly the negotiation between agents is to reach a coherent global state of the multiagent system by favoring agents’ synergy. In this paper, we propose a coalition formation-based negotiation model for the task allocation in the cooperative information agent-based systems. In this model, the agent that activates a negotiation seeks partners for achieving a complex task. The way that the partners take part in a coalition is done one by one according to the choice of all the coalition members. This choice is based on a multicriterion analysis. Some obtained experimental results show the suggested model performances.

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Katagami, Daisuke, Yusuke Ikeda, and Katsumi Nitta. "Behavior Generation and Evaluation of Negotiation Agent Based on Negotiation Dialogue Instances." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 7 (2010): 840–51. http://dx.doi.org/10.20965/jaciii.2010.p0840.

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This study focuses on gestures negotiation dialogs. Analyzing the situation/gesture relationship, we suggest how to enable agents to conduct adequate human-like gestures and evaluated whether an agent’s gestures could give an impression similar to those by a human being. We collected negotiation dialogs to study common human gestures. We studied gesture frequency in different situations and extracted gestures with high frequency, making an agent gesture module based on the number of characteristics. Using a questionnaire, we evaluated the impressions of gestures by human users and agents, confirming that the agent expresses the same state of mind as the human being by generating an adequately human-like gesture.

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Pinto, Tiago, Mohammad Fotouhi Ghazvini, Joao Soares, et al. "Decision Support for Negotiations among Microgrids Using a Multiagent Architecture." Energies 11, no. 10 (2018): 2526. http://dx.doi.org/10.3390/en11102526.

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This paper presents a decision support model for negotiation portfolio optimization considering the participation of players in local markets (at the microgrid level) and in external markets, namely in regional markets, wholesale negotiations and negotiations of bilateral agreements. A local internal market model for microgrids is defined, and the connection between interconnected microgrids is based on nodal pricing to enable negotiations between nearby microgrids. The market environment considering the local market setting and the interaction between integrated microgrids is modeled using a multi-agent approach. Several multi-agent systems are used to model the electricity market environment, the interaction between small players at a microgrid scale, and to accommodate the decision support features. The integration of the proposed models in this multi-agent society and interaction between these distinct specific multi-agent systems enables modeling the system as a whole and thus testing and validating the impact of the method in the outcomes of the involved players. Results show that considering the several negotiation opportunities as complementary and making use of the most appropriate markets depending on the expected prices at each moment allows players to achieve more profitable results.

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He, Xu. "Research on Supply Chain Auto-Negotiation System Model Based on Multi-Agent System and CBR." Applied Mechanics and Materials 58-60 (June 2011): 1721–26. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1721.

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Under dynamic complicated supply chain environment, negotiation efficiency among supply chain members exerts direct influence on the whole performance and competiveness of supply chain. This document combing multi-agent and CBR, puts forward a new supply chain auto-negotiation system model. It analyzes retrieve, reuse, revise and retain of supply chain negotiation case, and establishes supply chain auto-negotiation model based on multi-agent. This system model is helpful for making full use of supply chain historic negotiation experience and experts’ knowledge. It is highly intelligentized which is propitious to promote supply chain negotiation efficiency.

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Su, Peng Cheng, Gui He Wang, Jian Yu Yang, and Wan Shan Wang. "Multi-Agent Approach for E-Commerce Negotiation." Applied Mechanics and Materials 16-19 (October 2009): 183–88. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.183.

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In a dynamic environment of e-commerce, different business entities expect to interact with each other and have their transaction time include its cost minimized. In this paper, the concept of agent and multi-agent, as well as their structure, were introduced. At the same time, the negotiation protocol, negotiation strategies and the negotiation mechanism were also researched for e-commerce negotiation. The agent technology was exploited, based on which, the negotiation system was developed. This platform would improve negotiation efficiency by saving time and cost for e-commerce.

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Hassanvand, Fatemeh, Faria Nassiri-Mofakham, and Katsuhide Fujita. "Automated Negotiation Agents for Modeling Single-Peaked Bidders: An Experimental Comparison." Information 15, no. 8 (2024): 508. http://dx.doi.org/10.3390/info15080508.

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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.

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Mazumdar, Bireshwar Dass, and R. B. Mishra. "Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods." International Journal of Intelligent Information Technologies 6, no. 4 (2010): 46–70. http://dx.doi.org/10.4018/jiit.2010100104.

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The Multi agent system (MAS) model has been extensively used in the different tasks of e-commerce like customer relation management (CRM), negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.

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Klein, Matthias, Andreas Löcklin, Nasser Jazdi, and Michael Weyrich. "A negotiation based approach for agent based production scheduling." Procedia Manufacturing 17 (2018): 334–41. http://dx.doi.org/10.1016/j.promfg.2018.10.054.

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Yang, Changhui, Man Li, Yizhen Chen, Zhenfan Yang, and Lingyu Hu. "Multi-Objective Negotiation Mechanism in Manufacturing Enterprise Supply Chain Based on Multi-Agent." Journal of Global Information Management 31, no. 1 (2023): 1–22. http://dx.doi.org/10.4018/jgim.316834.

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In the operation of manufacturing enterprise supply chain, there are lots of conflicts and differences between the node enterprises because of the different demands on the price, quality, cost, and other factors. These conflicts and differences can be effectively solved by negotiation. In this paper, the authors will abstract different entities in manufacturing enterprise supply chain as agents, and present a negotiation mode, and then discuss the negotiation tactics and procedures between the purchasing agent and supplier agent. Next, a practical example will be discussed and simulated for validating the negotiation model. Application of negotiation tactics and models will be helpful for resolving differences and conflicts, and improving negotiation efficiency. That will be used for optimizing the supply chain management, maximizing the benefits, and improving the operational efficiency of manufacturing enterprise supply chain.

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Pilotti, Pablo, Ana Casali, and Carlos Chesñevar. "A belief revision approach for argumentation-based negotiation agents." International Journal of Applied Mathematics and Computer Science 25, no. 3 (2015): 455–70. http://dx.doi.org/10.1515/amcs-2015-0034.

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Abstract Negotiation is an interaction that happens in multi-agent systems when agents have conflicting objectives and must look for an acceptable agreement. A typical negotiating situation involves two agents that cannot reach their goals by themselves because they do not have some resources they need or they do not know how to use them to reach their goals. Therefore, they must start a negotiation dialogue, taking also into account that they might have incomplete or wrong beliefs about the other agent’s goals and resources. This article presents a negotiating agent model based on argumentation, which is used by the agents to reason on how to exchange resources and knowledge in order to achieve their goals. Agents that negotiate have incomplete beliefs about the others, so that the exchange of arguments gives them information that makes it possible to update their beliefs. In order to formalize their proposals in a negotiation setting, the agents must be able to generate, select and evaluate arguments associated with such offers, updating their mental state accordingly. In our approach, we will focus on an argumentation-based negotiation model between two cooperative agents. The arguments generation and interpretation process is based on belief change operations (expansions, contractions and revisions), and the selection process is a based on a strategy. This approach is presented through a high-level algorithm implemented in logic programming. We show various theoretical properties associated with this approach, which have been formalized and proved using Coq, a formal proof management system. We also illustrate, through a case study, the applicability of our approach in order to solve a slightly modified version of the well-known home improvement agents problem. Moreover, we present various simulations that allow assessing the impact of belief revision on the negotiation process.

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Chen, Siqi, and Ran Su. "An autonomous agent for negotiation with multiple communication channels using parametrized deep Q-network." Mathematical Biosciences and Engineering 19, no. 8 (2022): 7933–51. http://dx.doi.org/10.3934/mbe.2022371.

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<abstract><p>Agent-based negotiation aims at automating the negotiation process on behalf of humans to save time and effort. While successful, the current research considers communication between negotiation agents through offer exchange. In addition to the simple manner, many real-world settings tend to involve linguistic channels with which negotiators can express intentions, ask questions, and discuss plans. The information bandwidth of traditional negotiation is therefore restricted and grounded in the action space. Against this background, a negotiation agent called MCAN (multiple channel automated negotiation) is described that models the negotiation with multiple communication channels problem as a Markov decision problem with a hybrid action space. The agent employs a novel deep reinforcement learning technique to generate an efficient strategy, which can interact with different opponents, i.e., other negotiation agents or human players. Specifically, the agent leverages parametrized deep Q-networks (P-DQNs) that provides solutions for a hybrid discrete-continuous action space, thereby learning a comprehensive negotiation strategy that integrates linguistic communication skills and bidding strategies. The extensive experimental results show that the MCAN agent outperforms other agents as well as human players in terms of averaged utility. A high human perception evaluation is also reported based on a user study. Moreover, a comparative experiment shows how the P-DQNs algorithm promotes the performance of the MCAN agent.</p></abstract>

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Sanchez-Anguix, Victor, Okan Tunalı, Reyhan Aydoğan, and Vicente Julian. "Can Social Agents Efficiently Perform in Automated Negotiation?" Applied Sciences 11, no. 13 (2021): 6022. http://dx.doi.org/10.3390/app11136022.

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In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.

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Meng, Qingfeng, Jingxian Chen, and Kun Qian. "The Complexity and Simulation of Revenue Sharing Negotiation Based on Construction Stakeholders." Complexity 2018 (August 2, 2018): 1–11. http://dx.doi.org/10.1155/2018/5698170.

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This paper focuses on the complexity characteristics of a stakeholder’s revenue sharing for time compression in construction projects, such as adopting a life cycle perspective, the preferences of stakeholders, and the adaptability behaviors in the negotiation process. We build an agent-based model on revenue sharing negotiation. Considering that the agents who are in a weak position not only care about their own benefits but also compare their benefits to others, we design an experimental scenario where a contractor has fairness preference based on China’s reality. According to different sympathy and envy coefficients, we can divide the inequity aversion preference into three typical types, and we research how a contractor’s different types of inequity aversion preferences impact revenue sharing coefficient of agreements, results of successful negotiations, and efficiency in negotiations. Results are as follows: it is advantageous for a contractor to maintain a modest inequity aversion for their own earnings and the degree of sympathy preference in inequity aversion has an important impact on the time to reach consensus while the degree of jealousy preference has no obvious effect. If contractors’ sympathy preference is maintained within a moderate range, it will achieve a higher success rate of negotiations in the negotiation process; the success rate of negotiation is affected largely by the agents’ sympathy preference, though it is also influenced by the jealousy preference, but it is not very sensitive.

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Lavendelis, Egons, and Janis Grundspenkis. "Multi-Agent Auction Based Simulation Tool for an Insurance Policy Market." Applied Computer Systems 15, no. 1 (2014): 5–13. http://dx.doi.org/10.2478/acss-2014-0001.

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Abstract The paper presents a simulation tool for automated interactions between insurance companies and their clients during the travel insurance buying process. Insurance deal evaluation model using price and insured risks has been developed based on the study of the Latvian insurance market. The proposed model is used together with well-known agent auction protocols, thus providing a multi-agent negotiation protocol. It allows automating one-to-many negotiations between client and insurance companies simulating electronic insurance policy marketplace. The simulation tool has been developed using the MASITS methodology and tool, thus providing a case study for the methodology and tool for a new type of systems

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Lempp, Frieder. "Towards a general agent-based model of negotiation." Academy of Management Proceedings 2018, no. 1 (2018): 10870. http://dx.doi.org/10.5465/ambpp.2018.10870abstract.

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Jin, Y., and S. C.-Y. Lu. "Agent Based Negotiation for Collaborative Design Decision Making." CIRP Annals 53, no. 1 (2004): 121–24. http://dx.doi.org/10.1016/s0007-8506(07)60659-3.

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Maestre, J. M., D. Muñoz de la Peña, E. F. Camacho, and T. Alamo. "Distributed model predictive control based on agent negotiation." Journal of Process Control 21, no. 5 (2011): 685–97. http://dx.doi.org/10.1016/j.jprocont.2010.12.006.

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Smirnov, Alexander, Nikolay Shilov, and Alexey Kashevnik. "BTO supply chain configuration via agent-based negotiation." International Journal of Manufacturing Technology and Management 17, no. 1/2 (2009): 166. http://dx.doi.org/10.1504/ijmtm.2009.023784.

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Chen, Huijun. "Enterprise negotiation and communication management system under the guidance of the Internet of Things." PLOS ONE 18, no. 4 (2023): e0284891. http://dx.doi.org/10.1371/journal.pone.0284891.

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The multi-agent system is used to study the negotiation problem of virtual enterprises in the context of the Internet of Things (IoT) to strengthen the decision-making ability of enterprises and improve the negotiation efficiency between different enterprises. Firstly, virtual enterprises and high-tech virtual enterprises are introduced. Secondly, the virtual enterprise negotiation model is implemented using the agent technology in the IoT, including constructing the operation mode of the alliance enterprise agent and the member enterprise agent. Finally, a negotiation algorithm based on improved Bayesian theory is proposed. It is applied to virtual enterprise negotiation, and the effect of the negotiation algorithm is verified by setting an example. The results show that: (1) When one side of the enterprise adopts a risk-taking strategy, the number of negotiation rounds between the two sides increases. (2) High joint utility can be achieved when both parties to the negotiation adopt a conservative strategy. (3) The improved Bayesian algorithm can improve the negotiation efficiency of enterprises by reducing the number of negotiation rounds. This study aims to achieve efficient negotiation between the alliance and the member enterprises to improve the decision-making ability of the alliance owner enterprise.

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Qin, Ling, and Shu Lin Kan. "Production Dynamic Scheduling among Factories Based on Multi-Agent." Advanced Materials Research 466-467 (February 2012): 1386–91. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1386.

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To solve fluctuation problem in production plan and scheduling among factories, a logic framework of production dynamic scheduling among factories based on multi-agent technology was constructed. In this framework, the production dynamic scheduling multi-agent negotiation rules and mechanism among factories were established. Furthermore, the production dynamic scheduling multi-agent negotiation procedure among factories was investigated. Finally, the simulation system of production dynamic scheduling among factories based on multi-agent is demonstrated and validated by Flexsim software. It has shown the proposed method can improve the adaptability and stability of production plan and scheduling, and provide a support for optimal and dynamic production plan and scheduling among factories.

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