Agent Negotiation Research Articles

Automated Negotiation Agents for Modeling Single-Peaked Bidders: An Experimental Comparison

Automated Negotiation Agents for Modeling Single-Peaked Bidders: An Experimental Comparison

A User Priorities Based Strategy for Three Phase Intelligent Recommendation and Negotiating Agents for Cloud Services

As the field of information technology expands, there is a huge need for cloud service providers (CSP). CSP’s vast solutions and services support Cloud, IoT, Fog, and Edge computing. In today’s competitive cloud market, customer satisfaction is critical more than ever. CSP and consumer satisfaction with service level agreement (SLA) fulfilment have always been given more attention. As a result of signing SLA and CSP agreements to supply resources in high demand, customers are now experiencing issues with resource delivery. Cloud and heterogeneous environments necessitate an intelligent recommender and negotiation agent model (IRNAM) to handle responsibilities in the current system. The Recommender system recommends CSP as per users’ priorities, which eases the filtration process. The negotiation process provided by IRNAM ensures that users’ choices are prioritized with maximum jobs to CSP. IRNAM keeps track of the most critical metrics and can reach decisions quickly and for the best possible deal. It uses an analytical concession algorithm that analyzes consumer and CSP choices to find a reliable, secure server with the simplest solution. The negotiation process uses user’s and CSP choice metrics, performance factors, evaluation measures, and success factors in the best execution time to decide. IRNAM provides a flexible and valuable way for selecting CSP and negotiating for services on the user’s terms while considering CSP satisfaction.

Coordination of Emergent Demand Changes via Value-Based Negotiation for Supply Chain Management (Student Abstract)

We propose an automated negotiation for a reinforcement learning agent to adapt the agent to unexpected situations such as demand changes in supply chain management (SCM). Existing studies that consider reinforcement learning and SCM assume a centralized environment where the coordination of chain components is hierarchical rather than through negotiations between agents. This study focused on a negotiation agent that considered the value function of reinforcement learning for SCM as its utility function in automated negotiation. We demonstrated that the proposed approach could avoid inventory shortages under increased demand requests from the terminal customer.

Obstacle negotiation and navigation control of robotic agents in a dynamic and complex terrains

Obstacle negotiation and navigation control of robotic agents in a dynamic and complex terrains

Towards Emotion-Aware Agents for Improved User Satisfaction and Partner Perception in Negotiation Dialogues

Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans by the means of language are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the role of emotion in the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. We devise ways to measure and compare different degrees of emotion expression in negotiation dialogues, consisting of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emoticon</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lexical</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contextual</i> variables. Through an extensive analysis of a large-scale dataset in chat-based negotiations, we find that incorporating emotion expression explains significantly more variance, above and beyond the demographics and personality traits of the participants. Further, our temporal analysis reveals that emotive information from both early and later stages of the negotiation contributes to this prediction, indicating the need for a continual learning model of capturing emotion for automated agents. Finally, we extend our analysis to another dataset, showing promise that our findings generalize to more complex scenarios. We conclude by discussing our insights, which will be helpful for designing adaptive negotiation agents that interact through realistic communication interfaces.

An opponent model for agent-based shared decision-making via a genetic algorithm.

Shared decision-making (SDM) has received a great deal of attention as an effective way to achieve patient-centered medical care. SDM aims to bring doctors and patients together to develop treatment plans through negotiation. However, time pressure and subjective factors such as medical illiteracy and inadequate communication skills prevent doctors and patients from accurately expressing and obtaining their opponent's preferences. This problem leads to SDM being in an incomplete information environment, which significantly reduces the efficiency of the negotiation and even leads to failure. In this study, we integrated a negotiation strategy that predicts opponent preference using a genetic algorithm with an SDM auto-negotiation model constructed based on fuzzy constraints, thereby enhancing the effectiveness of SDM by addressing the problems posed by incomplete information environments and rapidly generating treatment plans with high mutual satisfaction. A variety of negotiation scenarios are simulated in experiments and the proposed model is compared with other excellent negotiation models. The results indicated that the proposed model better adapts to multivariate scenarios and maintains higher mutual satisfaction. The agent negotiation framework supports SDM participants in accessing treatment plans that fit individual preferences, thereby increasing treatment satisfaction. Adding GA opponent preference prediction to the SDM negotiation framework can effectively improve negotiation performance in incomplete information environments.

Model of an Intelligent and Automated Negotiation Agent for the Service Level Agreement Negotiation Process in Cloud Computing

With the help of an intelligent agent, a cloud service provider (CSP) offers various services and resources to their customers over the internet. Consumers and CSPs in the cloud market are in a constant state of negotiation over their individual needs. Competition among CSPs is fierce because of the enormous number of CSPs in the market. Intelligent negotiation agents are highly demanding as a timesaving and success rate-enhancing tool. While negotiating, multiple attributes such as price, resources, reliability, CSP performance, utility value, and many more are considered. There’s increased false negotiations, faulty servers, and exorbitant fees are all issues arising because of the forever changing CSP market. An intelligent and automated negotiation agent (IANA) understands the conduct of the opponent and the needs of both parties. This need of users and CSP information is utilized in negotiations to find the best possible delivery of services between the customer and the CSP. As a result of the proposed design, negotiations are expedited, and there is a higher success rate and better oversight of service delivery.

Scalable Negotiating Agent Strategy via Multi-Issue Policy Network (Student Abstract)

Previous research on the comprehensive negotiation strategy using deep reinforcement learning (RL) has scalability issues of not performing effectively in the large-sized domains. We improve negotiation strategy via deep RL by considering an issue-based represented deep policy network to deal with multi-issue negotiation. The architecture of the proposed learning agent considers the characteristics of multi-issue negotiation domains and policy-based learning. We demonstrate that proposed method achieve equivalent or higher utility than existing negotiation agents in the large-sized domains.

Reward-Based Negotiating Agent Strategies

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.

Multilevel agent negotiation on service bindings for efficient multi-tasking in IoT environments

In Internet-of-Things (IoT) environments, various IoT resources are utilized to provide the services necessary to accomplish users’ tasks. Therefore, IoT resources must be effectively and efficiently allocated so that service providers can successfully generate their service capabilities utilizing IoT resources. Additionally, conflicting resource allocation must be avoided for services supporting multiple user tasks. Furthermore, resource allocation and conflict resolution must be performed dynamically because users perform their tasks spontaneously. To address these issues, we propose an agent-based service-binding approach for efficient resource allocation and conflict resolution through negotiations among resources, service providers, and task agents. Negotiation occurs when a task agent attempts to use a service that is currently occupied by another task agent. The task agent that uses the service is determined in the negotiation process by considering the transfer fee based on the usage price of the service and the availability of a substitute service. Negotiations can occur at the task-service or service-resource levels to support flexible service binding. To evaluate the effectiveness of our approach, we compared its success rate with that of traditional resource allocation methods in terms of allocating resources successfully for multiple services. We also analyze the execution time to measure the overhead incurred by multilevel negotiation, which is an essential component of our approach. The proposed approach showed an average success rate of 79% which is 1.17%p lower than multi-objective optimization algorithms (MOOA), but the running time of the proposed approach was 98.3 s which was 56% of the running time of MOOA.

Negotiation mechanism of carbon emission quota trading process

Carbon emission quota trading is an effective method for realizing carbon neutrality. Negotiation can help to effectively resolve the differences and conflicts among the trading parties in the carbon emission quota trading process. For researching the negotiation mechanism of carbon emission quota trade, we construct the agent negotiation model that introduces the risk appetite of negotiator and Bayesian algorithm. And then, the negotiation protocol is analyzed, including offer tactics, negotiation unity and termination condition. The experimental studies are given to simulate the negotiation of carbon emission quota trading process for demonstrating the validity of the negotiation model and tactics. This study demonstrates the impact of risk appetite on negotiation in the carbon emission quota trading. And can help to improve negotiation efficiency, achieve win-win and accurate matching among the trading parties of carbon emission quota.

Artificial Theory of Mind in contextual automated negotiations within peer-to-peer markets

With the advent of the sharing economy (eBay, Airbnb, Uber) and peer-to-peer electricity markets, negotiation agents become key to automate trading between prosumers. To be competitive, agents need to develop models of their opponents’ strategic behavior depending on contextual circumstances. However, existent approaches do not account for others’ ways of reasoning about how the context influences opponents’ strategies. In this work, negotiation agents are endowed with artificial Theory of Mind (aToM) to represent mental states of others. Those states include their strategies, beliefs, the context influencing beliefs and the beliefs they may have about the beliefs others have about themselves depending on the context. From prior knowledge that relates private and contextual states with opponent’s plausible strategies, agents build models of different orders of aToM using weighted Gaussian Processes with heteroscedastic noise and adapt their strategies. During a negotiation episode, each agent selects the order of aToM that best accounts for its opponent’s counteroffers based on Bayesian surprise. To assess the proposed approach, a benchmark Smart Grid is used, where energy prosumers negotiate on a peer-to-peer basis to compensate for their energy deficits and sell their surpluses. Results obtained highlight that low orders of aToM increase social welfare at least in a 54% despite the selfish nature of prosumers. Also, more sophisticated aToM abilities increase the individual competitiveness of agents. This is the first work in the related literature that adds aToM abilities to negotiation agents while considering the negotiation context.

Deep Reinforcement Learning Agent for Negotiation in Multi-Agent Cooperative Distributed Predictive Control

This paper proposes a novel solution for using deep neural networks with reinforcement learning as a valid option in negotiating distributed hierarchical controller agents. The proposed method is implemented in the upper layer of a hierarchical control architecture composed at its lowest levels by distributed control based on local models and negotiation processes with fuzzy logic. The advantage of the proposal is that it does not require the use of models in the negotiation, and it facilitates the minimization of any dynamic behavior index and the specification of constraints. Specifically, it uses a reinforcement learning policy gradient algorithm to achieve a consensus among the agents. The algorithm is successfully applied to a level system composed of eight interconnected tanks that are quite difficult to control due to their non-linear nature and the high interaction among their subsystems.

Dynamic Pricing Models and Negotiating Agents: Developments in Management Accounting

Linking decision systems, negotiating agents, management accounting, and computational accounting, this paper aims at exploring dynamic pricing strategies of a synthetic business-to-consumer online operation and a comparative analysis of evolving strategy-specific pricing optimization. Five price models based on market, utility, or demand information (three single and two combined), merging online and offline data, are explored over a seven-day period and with twenty selected products. A total of 17,529 website visits and 538 agent negotiations are studied (94,607 main data points) using a Python solution, with model simulation parameters and assumptions described. Findings show the combined market-utility-demand performance of dynamic pricing to be superior as an input to the negotiating agent. Contributions are threefold, pointing to (a) management accounting practice and research (dynamic pricing), (b) science and research strategy (method), and (c) accounting education (skill set).

NegoSim: A Modular and Extendable Automated Negotiation Simulation Platform Considering EUBOA

In recent years, the research community has become increasingly interested in automated negotiation. It has been used in real-world systems such as autonomous vehicle transportation systems, smart grids, and e-commerce. Considering the broad range of applications, automated negotiation is of great use and interest to engineers, developers, and industry-focused researchers. Therefore, some researchers have developed platforms for automated negotiation. However, these systems do not provide complete control over the development of various protocols or analytic tools. This paper introduces NegoSim, a new negotiation simulator that introduces a new negotiation framework called EUBOA, though it also works on the well-known BOA framework. NegoSim is completely modular, with different APIs allowing users full control over developing and modifying automated negotiation agents, different assessments, protocols, and utility spaces, as well as the ability to create or modify different GUIs. NegoSim also includes predefined agents, protocols, utility space, and analytic tools, making it an ideal platform for researchers.

Negotiation agent based on Deep reinforcement learning for multi-agent cooperative distributed predictive control

In this work, deep neural network trained by the model-free reinforcement learning method is proposed as a negotiation agent in multi-agent distributive model predictive control. The negotiation agent is implemented in the upper layer of a hierarchical control architecture based on distributed model predictive controller with pairwise negotiation fuzzy logic. The proposed is data-driven in order to achieve the minimization of any dynamic behavior index and the specification of constraints. Specifically, the deep neural network provides consensus coefficients to address the final control action applied by each agent. The proposed is successfully applied to a hydraulic system composed of eight interconnected tanks that are very difficult to control due to non-linearities nature and high interaction between its subsystems.

An Autonomous Multi-Agent Framework using Quality of Service to prevent Service Level Agreement Violations in Cloud Environment

Cloud is a specialized computing technology accommodating several million users to provide seamless services via the internet. The extension of this reverenced technology is growing abruptly with the increase in the number of users. One of the major issues with the cloud is that it receives a huge volume of workloads requesting resources to complete their executions. While executing these workloads, the cloud suffers from the issue of service level agreement (SLA) violations which impacts the performance and reputation of the cloud. Therefore, there is a requirement for an effective design that supports faster and optimal execution of workloads without any violation of SLA. To fill this gap, this article proposes an automatic multi-agent framework that ensures the minimization of the SLA violation rate in workload execution. The proposed framework includes seven major agents such as user agent, system agent, negotiator agent, coordinator agent, monitoring agent, arbitrator agent and the history agent. All these agents work cooperatively to enable the effective execution of workloads irrespective of their dynamic nature. With effective execution of workloads, the proposed model also resulted in an advantage of minimized energy consumption in data centres. The inclusion of a history agent within the framework enabled the model to predict future requirements based on the records of resource utilization. The proposed model followed the Poisson distribution to generate random numbers that are further used for evaluation purposes. The simulations of the model proved that model is more reliable in reducing SLA violations compared to the existing works. The proposed method resulted in an average SLA violation rate of 55.71% for 1200 workloads and resulted in an average energy consumption of 47.84kWh for 1500 workloads.

A User-Priorities-Based Strategy for Three-Phase Intelligent Recommendation and Negotiating Agents for Cloud Services

As the field of information technology expands, there is a huge need for cloud service providers (CSP). CSP’s vast solutions and services support Cloud, IoT, Fog, and Edge computing. In today’s competitive cloud market, customer satisfaction is critical more than ever. CSP and consumer satisfaction with service level agreement (SLA) fulfillment have always been given more attention. As a result of signing SLA and CSP agreements to supply resources in high demand, customers are now experiencing issues with resource delivery. Cloud and heterogeneous environments necessitate an intelligent recommender and negotiation agent model (IRNAM) to handle responsibilities in the current system. The Recommender system recommends CSP as per users’ priorities, which eases the filtration process. The negotiation process provided by IRNAM ensures that users’ choices are prioritized with maximum jobs to CSP. IRNAM keeps track of the most critical metrics and can reach decisions quickly and for the best possible deal. It uses an analytical concession algorithm that analyzes consumer and CSP choices to find a reliable, secure server with the simplest solution. The negotiation process uses user’s and CSP choice metrics, performance factors, evaluation measures, and success factors in the best execution time to decide. IRNAM provides a flexible and valuable way for selecting CSP and negotiating for services on the user’s terms while considering CSP satisfaction.

The Case for Negotiation Robots in simulated workplace negotiations A Theoretical Approach

Job negotiations are an anxiety-inducing and highly important form of social interaction. Most people are not sufficiently trained in negotiation strategies and often do not receive job offers that are conducive to financial stability. With the rise of virtual agents and robotics that are capable of modeling social interactions, a significant research effort has been established to create realistic simulations of negotiations. While the bulk of this research has focused on virtual agents as a medium for simulated negotiations, we propose that embodied agents can be utilized to model the inherent nuances in human interaction. In this paper, we propose a study that aims to evaluate the effectiveness of embodied agents compared to virtual agents in simulated negotiations with human participants.

The Effect of Appearance of Virtual Agents in Human-Agent Negotiation

Artificial Intelligence (AI) changed our world in various ways. People start to interact with a variety of intelligent systems frequently. As the interaction between human and AI systems increases day by day, the factors influencing their communication have become more and more important, especially in the field of human-agent negotiation. In this study, our aim is to investigate the effect of knowing your negotiation partner (i.e., opponent) with limited knowledge, particularly the effect of familiarity with the opponent during human-agent negotiation so that we can design more effective negotiation systems. As far as we are aware, this is the first study investigating this research question in human-agent negotiation settings. Accordingly, we present a human-agent negotiation framework and conduct a user experiment in which participants negotiate with an avatar whose appearance and voice are a replica of a celebrity of their choice and with an avatar whose appearance and voice are not familiar. The results of the within-subject design experiment show that human participants tend to be more collaborative when their opponent is a celebrity avatar towards whom they have a positive feeling rather than a non-celebrity avatar.