Relevant bibliographies by topics / Agent-Based negotiation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Agent-Based negotiation'

Author: Grafiati
Published: 7 December 2024

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Journal articles on the topic "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 (May 31, 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 (June 26, 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 (June 28, 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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Park, Jinsoo, Hamirahanim Abdul Rahman, Jihae Suh, and Hazami Hussin. "A Study of Integrative Bargaining Model with Argumentation-Based Negotiation." Sustainability 11, no. 23 (December 2, 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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Lempp, Frieder. "A new agent-based simulation model of bilateral negotiation." International Journal of Conflict Management 31, no. 1 (October 7, 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 (November 11, 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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Agrawal, Manish, and Kaushal Chari. "Negotiation Behaviors in Agent-Based Negotiation Support Systems." International Journal of Intelligent Information Technologies 5, no. 1 (January 2009): 1–23. http://dx.doi.org/10.4018/jiit.2009010101.

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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 (November 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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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 (July 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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Dissertations / Theses on the topic "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 environments, such as the Internet. The conventional methods, such as game theory, are incapable of handling an open environment where the information is sparse and full of uncertainty, while the fuzzy approaches are suitable to elegantly deal with this problem. The fuzzy logic based intelligent negotiation agent, presented in this thesis, is able to interact autonomously and consequently save human labor in negotiations. The aim of modeling a negotiation agent is to reach mutual agreement efficiently and intelligently. The negotiation agent is able to negotiate with other such agents, over various sets of issues, on behalf of the real-world parties they represent, i.e. it can handle multi-issue negotiation. The reasoning model of the negotiation agent has been implemented partially by using c# based on Microsoft .NET. The reliability and the flexibility of the reasoning model are finally evaluated. The results show that performance of the proposed agent model is acceptable for negotiation parties to achieve mutual benefits.
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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 particular, I focus on settings where agents have limited or uncertain information, precluding them from making optimal individual decisions. I demonstrate that this form of bounded-rationality may lead agents to sub-optimal negotiation agreements. I argue that rational dialogue based on the exchange of arguments can enable agents to overcome this problem. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs and planning knowledge, then rational dialogue over these reasons can enable agents to refine their individual decisions and consequently reach better agreements. I refer to this form of interaction as “interested-based negotiation.” (For complete abstract open document)
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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, most of the existing literature has considered time constraints in terms of the number of rounds of negotiation that take place. In contrast, in this work we consider time constraints which are based on the amount of time that has elapsed. This requires a different approach, since the time spent computing the next action has an effect on the utility of the outcome, whereas the actual number of offers exchanged does not. In addition to these time constraints, in the complex negotiation environments which we consider, there are multiple negotiation issues, and we assume that the opponents’ preferences over these issues and the behaviour of those opponents are unknown. Finally, in our environment there can be concurrent negotiations between many participants. Against this background, in this thesis we present the design of a range of practical negotiation strategies, the most advanced of which uses Gaussian process regression to coordinate its concession against its various opponents, whilst considering the behaviour of those opponents and the time constraints. In more detail, the strategy uses observations of the offers made by each opponent to predict the future concession of that opponent. By considering the discounting factor, it predicts the future time which maximises the utility of the offers, and we then use this in setting our rate of concession. Furthermore, we evaluate the negotiation agents that we have developed, which use our strategies, and show that, particularly in the more challenging scenarios, our most advanced strategy outperforms other state-of-the-art agents from the Automated Negotiating Agent Competition, which provides an international benchmark for this work. In more detail, our results show that, in one-to-one negotiation, in the highly discounted scenarios, our agent reaches outcomes which, on average, are 2.3% higher than those of the next best agent. Furthermore, using empirical game theoretic analysis we show the robustness of our strategy in a variety of tournament settings. This analysis shows that, in the highly discounted scenarios, no agent can benefit by choosing a different strategy (taken from the top four strategies in that setting) than ours. Finally, in the many-to-many negotiations, we show how our strategy is particularly effective in highly competitive scenarios, where it outperforms the state-of-the-art many-to-many negotiation strategy by up to 45%.
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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 collaborate to produce a ``best'' set of clusters. The framework supports three clustering paradigms: K-means, K-NN and divisive hierarchical clustering. A number of experiments were conducted using benchmark UCI data sets and designed to demonstrate that the proposed MADM approach can identify a best set of clusters using the following clustering metrics: F-measure, Within Group Average Distance (WGAD) and Between Group Average Distance (BGAD). The results demonstrated that the MADM framework could successfully be used to find a best cluster configuration. The second approach is an extension of the proposed initial MADM framework whereby a ``best'' cluster configuration could be found using cooperation and negotiation among agents. The novel feature of the extended framework is that it adopts a two-phase approach to clustering. Phase one is similar to the established centralised clustering approach (except that it is conducted in a decentralised manner). Phase two comprises a negotiation phase where agents ``swap'' unwanted records so as to improve a cluster configuration. A set of performatives is proposed as part of a negotiation protocol to facilitate intra-agent negotiation. It is this negotiation capability which is the central contribution of the work described in this thesis. An extensive evaluation of the extended framework was conducted using: (i) benchmark UCI data sets and (ii) a welfare benefits data set that provides an exemplar application. Evaluation of the framework clearly demonstrates that, in the majority of cases, this negotiation phase serves to produce a better cluster configuration (in terms of cohesion and separation) than that produced using a simple centralised approach.
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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 allocating dependent resources that involves parallel negotiation between multiple buyers and providers of resources. Methods. Literature survey has been conducted in the studied areas in order to position the work and gain more knowledge. Moreover, to validate the proposed protocol, a simulation study was performed. Results. The suggested protocol can handle dependent resources negotiation parallel and the result illustrates that. Moreover, the approach can avoid broadcasting of call for proposals to reduce the communication overhead, which usually occur in many other protocols. Conclusion. In the suggested protocol, we have presented a new idea of re-planning with other techniques like Information board and leveled commitment. In a simulation study, it was identified that this approach is able to deal with the dependent resources according to the simulation results.
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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 modeling customer preferences in a quick and easy manner is a major hurdle for implementing this agent-based system. A methodology is developed for this problem using conjoint analysis and neural networks. The framework also contains a model component that is addressed in this work. The model component is presented as a mediator of customer negotiation that uses the agent-based preference models mentioned above and employs a linear programming model to maximize overall satisfaction of the total market.
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Books on the topic "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. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0471-3.

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Fujita, Katsuhide, Quan Bai, Takayuki Ito, Minjie Zhang, Fenghui Ren, Reyhan Aydoğan, and Rafik Hadfi, eds. Modern Approaches to Agent-based Complex Automated Negotiation. Cham: 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. Tokyo: 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. Tokyo: 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. Cham: 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. Singapore: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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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Book chapters on the topic "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, 186–201. Cham: 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, 137–51. Berlin, Heidelberg: 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, 103–19. Singapore: 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, 163–70. Tokyo: 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, 109–24. Tokyo: 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, 261–71. Cham: 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, 575–84. Cham: 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, 131–42. Berlin, Heidelberg: 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, 465–77. Dordrecht: 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, 147–60. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-171-2_11.

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Conference papers on the topic "Agent-Based negotiation"

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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}. California: 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 easily infer that one strategy that fits all is rather impossible in such scenarios. In this work, we present a simple yet effective method of learning an end-to-end negotiation strategy from historical negotiation data. Next, we show that transfer learning based solutions are effective in designing adaptive strategies when underlying utility functions of agents change. Additionally, we also propose an online method of detecting and measuring such changes in the utility functions. Combining all three contributions we propose an adaptive automated negotiating agent framework that enables the automatic creation of transfer learning based negotiating agents capable of adapting to changes in utility functions. Finally, we present the results of an agent generated using our framework in different ANAC domains with 100 different utility functions each and show that our agent outperforms the benchmark score by domain independent agents by 6%.
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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}. California: 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-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.
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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. New York, New York, USA: 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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Doğru, Anıl, Mehmet Onur Keskin, Catholijn M. Jonker, Tim Baarslag, and Reyhan Aydoğan. "NegoLog: An Integrated Python-based Automated Negotiation Framework with Enhanced Assessment Components." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/998.

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The complexity of automated negotiation research calls for dedicated, user-friendly research frameworks that facilitate advanced analytics, comprehensive loggers, visualization tools, and auto-generated domains and preference profiles. This paper introduces NegoLog, a platform that provides advanced and customizable analysis modules to agent developers for exhaustive performance evaluation. NegoLog introduces an automated scenario and tournament generation tool in its Web-based user interface so that the agent developers can adjust the competitiveness and complexity of the negotiations. One of the key novelties of the NegoLog is an individual assessment of preference estimation models independent of the strategies.
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Yang, Li, Ma Rongguo, and Wang Longfei. "Intelligent Parking Negotiation Based on Agent Technology." In 2009 WASE International Conference on Information Engineering (ICIE). IEEE, 2009. http://dx.doi.org/10.1109/icie.2009.35.

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