Bibliografías temáticas / Agent-Based negotiation

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Literatura académica sobre el tema "Agent-Based negotiation"

Autor: Grafiati
Publicado: 7 de diciembre de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Agent-Based negotiation"

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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
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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
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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
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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
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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 im
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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
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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 al
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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,
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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
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Tesis sobre el tema "Agent-Based negotiation"

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Wang, Xin. "A fuzzy logic based intelligent negotiation agent." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27194.

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With the evolution of electronic commerce (eCommerce) on the web and the rise of interests in intelligence of software agents, automated negotiation is becoming an increasingly popular method for an eCommerce system to be efficient; however, negotiation, which takes place in transaction, is complicated, time-consuming and costly for participants to reach an agreement. This thesis presents a model of an intelligent negotiation agent based on fuzzy logic methodology in order to alleviate the complexity of negotiation. The proposed negotiation agent model is particularly suitable to open environm
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Rahwan, Iyad. "Interest-based negotiation in multi-agent systems." Connect to thesis, 2004. http://repository.unimelb.edu.au/10187/2885.

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Software systems involving autonomous interacting software entities (or agents) present new challenges in computer science and software engineering. A particularly challenging problem is the engineering of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is concerned with negotiation: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particu
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Leung, Chun-wai David, and 梁俊偉. "An agent-based negotiation framework for supply chain management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B26651129.

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Williams, Colin Richard. "Practical strategies for agent-based negotiation in complex environments." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/348190/.

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Agent-based negotiation, whereby the negotiation is automated by software programs, can be applied to many different negotiation situations, including negotiations between friends, businesses or countries. A key benefit of agent-based negotiation over human negotiation is that it can be used to negotiate effectively in complex negotiation environments, which consist of multiple negotiation issues, time constraints, and multiple unknown opponents. While automated negotiation has been an active area of research in the past twenty years, existing work has a number of limitations. Specifically, mo
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Chaimontree, Santhana. "Multi-agent data mining with negotiation : a study in multi-agent based clustering." Thesis, University of Liverpool, 2012. http://livrepository.liverpool.ac.uk/7673/.

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Multi-Agent Data Mining (MADM) seeks to harness the general advantages offered by Multi-Agent System (MAS) with respect to the domain of data mining. The research described in this thesis is concerned with Multi-Agent Based Clustering (MABC), thus MADM to support clustering. To investigate the use of MAS technology with respect to data mining, and specifically data clustering, two approaches are proposed in this thesis. The first approach is a multi-agent based approach to clustering using a generic MADM framework whereby a collection of agents with different capabilities are allowed to collab
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Kulsumunnessa, Omme, and Zaruhi Aslanyan. "An agent based protocol for parallel negotiation of dependent resources." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1920.

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Context. Resource allocation is an important issue in order to complete a task in the field of agent system. Several protocols are available for task distribution and for efficient resource allocation among agents. Efficient task distribution and resource allocation among agents are often play important roles to obtain high performance. However, the situation becomes more complicated when the resources are dependent on each other where multiple buyers and providers of resources negotiate in parallel. Objectives. In this paper, we proposed an agent based protocol for synchronizing and allocatin
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Chattopadhyay, Dipayan. "Distributed Decision Tree Induction Using Multi-agent Based Negotiation Protocol." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406810377.

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Li, Wentao. "An agent-based negotiation model for the sourcing of construction suppliers." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B39633950.

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Li, Wentao, and 李文濤. "An agent-based negotiation model for the sourcing of construction suppliers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B39633950.

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LoPinto, Frank Anthony. "An Agent-Based Distributed Decision Support System Framework for Mediated Negotiation." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/27401.

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Implementing an e-market for limited supply perishable asset (LiSPA) products is a problem at the intersection of online purchasing and distributed decision support systems (DistDSS). In this dissertation, we introduce and define LiSPA products, provide real-world examples, develop a framework for a distributed system to implement an e-market for LiSPA products, and provide proof-of-concept for the two major components of the framework. The DistDSS framework requires customers to instantiate agents that learn their preferences and evaluate products on their behalf. Accurately eliciting and m
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Libros sobre el tema "Agent-Based negotiation"

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Aydoğan, Reyhan, Takayuki Ito, Ahmed Moustafa, Takanobu Otsuka, and Minjie Zhang, eds. Recent Advances in Agent-based Negotiation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0471-3.

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Fujita, Katsuhide, Quan Bai, Takayuki Ito, et al., eds. Modern Approaches to Agent-based Complex Automated Negotiation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51563-2.

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Fujita, Katsuhide, Takayuki Ito, Minjie Zhang, and Valentin Robu, eds. Next Frontier in Agent-Based Complex Automated Negotiation. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55525-4.

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Marsa-Maestre, Ivan, Miguel A. Lopez-Carmona, Takayuki Ito, Minjie Zhang, Quan Bai, and Katsuhide Fujita, eds. Novel Insights in Agent-based Complex Automated Negotiation. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54758-7.

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Fukuta, Naoki, Takayuki Ito, Minjie Zhang, Katsuhide Fujita, and Valentin Robu, eds. Recent Advances in Agent-based Complex Automated Negotiation. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30307-9.

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Hadfi, Rafik, Reyhan Aydoğan, Takayuki Ito, and Ryuta Arisaka, eds. Recent Advances in Agent-Based Negotiation: Applications and Competition Challenges. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0561-4.

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Ito, Takayuki, Minjie Zhang, Valentin Robu, Shaheen Fatima, Tokuro Matsuo, and Hirofumi Yamaki, eds. Innovations in Agent-Based Complex Automated Negotiations. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15612-0.

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Ito, Takayuki, Minjie Zhang, Valentin Robu, Shaheen Fatima, and Tokuro Matsuo, eds. Advances in Agent-Based Complex Automated Negotiations. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03190-8.

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Ito, Takayuki, Minjie Zhang, Valentin Robu, Shaheen Fatima, and Tokuro Matsuo, eds. New Trends in Agent-Based Complex Automated Negotiations. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24696-8.

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Zhang, Minjie, Takayuki Ito, and Valentin Robu. Advances in Agent-Based Complex Automated Negotiations. Springer, 2009.

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Capítulos de libros sobre el tema "Agent-Based negotiation"

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Buron, Cédric L. R., Zahia Guessoum, and Sylvain Ductor. "MCTS-Based Automated Negotiation Agent." In PRIMA 2019: Principles and Practice of Multi-Agent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33792-6_12.

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Al-Jaljouli, Raja, and Jemal Abawajy. "Negotiation Strategy for Mobile Agent-Based e-Negotiation." In Principles and Practice of Multi-Agent Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25920-3_10.

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Alhejaili, Abdullah, and Shaheen Fatima. "Multi-Agent Recommender System." In Recent Advances in Agent-based Negotiation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0471-3_7.

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Dirkzwager, Alexander, and Mark Hendrikx. "An Adaptive Negotiation Strategy for Real-Time Bilateral Negotiations." In Novel Insights in Agent-based Complex Automated Negotiation. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54758-7_10.

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Mansour, Khalid, and Ryszard Kowalczyk. "On Dynamic Negotiation Strategy for Concurrent Negotiation over Distinct Objects." In Novel Insights in Agent-based Complex Automated Negotiation. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54758-7_6.

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Erez, Eden Shalom, and Inon Zuckerman. "DoNA—A Domain-Based Negotiation Agent." In Studies in Computational Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30307-9_18.

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Veloso, Bruno, Benedita Malheiro, and Juan C. Burguillo. "Media Brokerage: Agent-Based SLA Negotiation." In New Contributions in Information Systems and Technologies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16486-1_56.

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Wong, Wai Yat, Dong Mei Zhang, and Mustapha Kara-Ali. "Towards an Experience Based Negotiation Agent." In Cooperative Information Agents IV - The Future of Information Agents in Cyberspace. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-45012-2_13.

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Maestre, J. M., D. Muñoz de la Peña, and E. F. Camacho. "Distributed MPC Based on Agent Negotiation." In Intelligent Systems, Control and Automation: Science and Engineering. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7006-5_29.

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Debenham, John, and Carles Sierra. "Agent-Based Negotiation in Uncertain Environments." In Research and Development in Intelligent Systems XXV. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-171-2_11.

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Actas de conferencias sobre el tema "Agent-Based negotiation"

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Rodríguez-Arias, Alejandro, Noelia Sánchez-Maroño, and Bertha Guijarro-Berdiñas. "Communication and Negotiation to Improve Agent-Based Models." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013377500003890.

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Croitoru, Madalina, Cornelius Croitoru, and Gowrishankar Ganesh. "Prediction-Based Selective Negotiation for Refining Multi-Agent Resource Allocation." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013368000003890.

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Sengupta, Ayan, Shinji Nakadai, and Yasser Mohammad. "Transfer Learning Based Adaptive Automated Negotiating Agent Framework." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/67.

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With the availability of domain specific historical negotiation data, the practical applications of machine learning techniques can prove to be increasingly effective in the field of automated negotiation. Yet a large portion of the literature focuses on domain independent negotiation and thus passes the possibility of leveraging any domain specific insights from historical data. Moreover, during sequential negotiation, utility functions may alter due to various reasons including market demand, partner agreements, weather conditions, etc. This poses a unique set of challenges and one can easil
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Bagga, Pallavi, Nicola Paoletti, Bedour Alrayes, and Kostas Stathis. "A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/42.

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We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-program
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Morar, Gabriela Andreea, Andreea Ilea, Alexandru Butoi, and Gheorghe Cosmin Silaghi. "Agent-based cloud resources negotiation." In 2012 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2012. http://dx.doi.org/10.1109/iccp.2012.6356202.

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"DESIGN AND EVALUATION OF AGENT-BASED NEGOTIATION HEURISTIC FOR ONLINE NEGOTIATIONS." In 6th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002604500920097.

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Mbarki, Mohamed, Omar Marey, Jamal Bentahar, and Khalid Sultan. "Agent Types and Adaptive Negotiation Strategies in Argumentation-Based Negotiation." In 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2014. http://dx.doi.org/10.1109/ictai.2014.79.

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Jinghui Zhang and Junzhou Luo. "Agent based automated negotiation for grid." In in Design (CSCWD). IEEE, 2008. http://dx.doi.org/10.1109/cscwd.2008.4537003.

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Comuzzi, Marco, and Chiara Francalanci. "Agent-based negotiation in cooperative processes." In the 6th international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1052220.1052236.

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Ateib, Moustapha Tahir. "Agent based negotiation in E-commerce." In 2010 International Symposium on Information Technology (ITSim 2010). IEEE, 2010. http://dx.doi.org/10.1109/itsim.2010.5561565.

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