Autonomous Negotiation Research Articles

DeResolver: A Decentralized Conflict Resolution Framework with Autonomous Negotiation for Smart City Services

As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and fixed priorities of different services, which is challenging due to the inconsistent and private objectives of the services. Also, the centralized solutions miss opportunities to more effectively resolve conflicts according to their spatiotemporal locality of the conflicts. To address this issue, we design a decentralized negotiation and conflict resolution framework named DeResolver, which allows services to resolve conflicts by communicating and negotiating with each other to reach a Pareto-optimal agreement autonomously and efficiently. Our design features a two-step self-supervised learning-based algorithm to predict acceptable proposals and their rankings of each opponent through the negotiation. Our design is evaluated with a smart city case study of three services: intelligent traffic light control, pedestrian service, and environmental control. In this case study, a data-driven evaluation is conducted using a large dataset consisting of the GPS locations of 246 surveillance cameras and an automatic traffic monitoring system with more than 3 million records per day to extract real-world vehicle routes. The evaluation results show that our solution achieves much more balanced results, i.e., only increasing the average waiting time of vehicles, the measurement metric of intelligent traffic light control service, by 6.8% while reducing the weighted sum of air pollutant emission, measured for environment control service, by 12.1%, and the pedestrian waiting time, the measurement metric of pedestrian service, by 33.1%, compared to priority-based solution.

Dynamic Posture Prediction Model in Informationized Combat Environment

The several local wars that broke out at the turn of the century have shown to the world that the rapid development of high and new technology with information technology as the core is effectively promoting the pace of world military reform, and its most essential feature is information dominance. In local wars under modern high-tech conditions, rapid response and multiarms joint and coordinated operations have become the key factors to win, which requires multiarms, multiweapon platforms, and each combat unit software to work autonomously and dynamically. This research mainly discusses the dynamic posture prediction model in the informationized combat environment. It designs the organization of command elements, related data (products and tools), competency, personnel matching checklists, and activities in joint operational planning and relies on activities to model the binding of groups, capabilities, and related data. Finally, it forms process segments through the combined design of activities and then sets the final state and background knowledge constraints on the process segments to construct the process knowledge, turning each combat unit into an agent unit, so that the combat software unit has the performance characteristics of the agent’s autonomy, reactivity, and sociality. At the same time, it has good communication performance and autonomous negotiation ability, so it can realize the autonomous connection, communication, and cooperative work among multiagent systems. During the process execution or business processing, the seat staff can input the goal and background of the activity according to the current operating environment. It relies on the background constraints of the process knowledge to quickly match the corresponding process knowledge, insert the process fragments in the process knowledge into the original process, realize the rapid and efficient decomposition of the process redefinition, and effectively ensure the orderly development of the combat command. Techniques and methods for automating integration testing of combat systems are provided. These technologies are further applied to the process of combat system integration tests to improve the efficiency of combat system integration. In addition, through the research on the integration testing technology of combat systems, the problems of low generality of integration testing software and many structural repetitive codes can also be solved and the existing integration testing technology can be further improved. In the research, the time-consuming of the script interpretation module accounts for a relatively low proportion of about 5% of the simulation. The article embodies the platform to explore the realization of multiagent-based dynamic cooperative operations, to achieve the purpose of simulating the battlefield situation, rehearsing the combat plan, and evaluating the strike effect. This study will improve the efficiency of overall combat system integration.

Asset Administration Shell - manufacturing processes energy optimization

The paper deals with the issue of Asset Administration Shell (AAS), especially from the point of view of production process optimization. From the authors' point of view, Industry 4.0 brings the main advantages especially in decentralization and thus the independence of individual production machines, which will enable greater variability of production and better response to failures. The content is a description of the parts of the AAS that are closely related to production optimization, these are active submodels for autonomous negotiation, optimization (evaluation criteria), predictive maintenance and resilience. In the case of the implementation of the mentioned submodules, a robust production solution will be implemented, which is suitable for discrete piece production with direct links to the customer, who can freely configure his product. This paper provides an overview of optimization in the use of AAS and their main functions - submodules and their possible implementation and benefits for production systems.

Retracted] Stakeholders’ Viewpoints toward Blockchain Integration within IoT‐Based Smart Cities

This study intends to assess the development of IoT‐based smart cities industry and the possibilities of blockchain integration from the perspective of industry stakeholders as the vision for a modern, integrated smart city future is predicated upon intelligence and the relationship between data‐rich connections and human activities. Although this ideal of an interconnected urban landscape is currently being tested and actively used by consumers spanning a range of connected nodes and service solutions, the scalability, interoperability, and security of this emergent cyber‐physical ideal has yet to be adequately resolved. This study used an exploratory study design following a mixed method design approach. A structured questionnaire survey (quantitative) and interviews (qualitative) were conducted for collecting data. IBM SPSS was used for the analysis of the data, which computed descriptive statistics, cross‐tabulation, Pearson correlation, and ANOVA for quantitative data and thematic analysis for qualitative data. Through an empirical assessment of the perceptions and expertise of 122 stakeholders from within the worldwide IoT smart city industry, conceptual support for blockchain integration into the IoT solution was acquired, highlighting the solution‐oriented, system‐centered advantages of a decentralised, autonomous data management backbone that could be applied to future IoT‐based smart city solutions. To meet the broad and diversified needs of the smart city and its future evolution, this study has confirmed that a commitment to decentralisation and blockchain intermediary data management is critical to scalable, secure, and autonomous negotiations of the IoT‐enabled smart city networks.

A decentralized peer-to-peer control scheme for heating and cooling trading in distributed energy systems

With the increasing penetration of distributed energy resources, integrating renewable generations into energy systems is a significant trend for smart and cleaner energy systems. To this end, advanced energy management has become of great importance. Conventional control of distributed energy resources relies on a central operator, which is responsible for the energy flow from producers to consumers and regulates money transactions. As this operator or the bulk grid is a monopoly, not every user can freely connect to a distributed energy resource, and distributed energy resource s can neither compete on price and services nor decide the price of the energy they want to sell to the users. To facilitate the utilization of locally-produced energy, and balance the supply and demand, a novel decentralized competitive energy system is proposed. Through this highly automated and fully decentralized multi-energy management approach, different parties on the peer-to-peer network can conduct money transaction at the machine level without interference of the central operator. An integrated multi-layer system architecture of the competitive energy system is elaborated, including system operation mechanism, device bidding strategy, and a hardware device Energy Router. The underlying protocol for money transactions among devices is IOTA, a peer-to-peer network supporting the data and value transfer for machine economy. The proposed energy network can facilitate autonomous negotiation and execution of transactions among machines without central operator’s intervention, and prevent monopolies, as well as promote easy admission of new distributed energy resources. Furthermore, a case study of a decentralized competitive heating system is presented to demonstrate the proposed architecture, and computer simulations were conducted to verify its rationality and potential value. The simulation results indicate that the peer-to-peer heating system outperforms conventional central heating systems in terms of both user cost and system efficiency, as it encourages end users to consume locally-produced energy. The proposed decentralized solution can save 61% operation cost in heating seasons.

Agent-Based Optimization of Maintenance Planning for Railway Vehicles

In practice, maintenance of rail vehicles is based on reactive and preventive maintenance strategies. Condition-oriented maintenance approaches are only slowly finding their way into the market. When researching the state of the art, it is noticeable that the majority of the approaches presented is considering individual components - the system focus necessary for maintenance optimization is not taken into account.
 Depending on the target system (number of components) and planning period, a complex optimization problem (OP) results. The OP is an NP-heavy problem for which the use of Genetic Algortihms can deliver suitable solutions for small search spaces. When applying it on a complex system with a larger solution space, this heuristical approach alone is not sufficient for the analytical optimization of a system representing a locomotive.
 Therefore, in this paper agent-based distributed problem solving is applied to analytically optimize the maintenance of the target system. Therefore, a multi-agent system (MAS) based on the O-MaSE-model will be developed, which captures the configuration of a target system and formulates the overall OP using the fictional data from a drivetrain of a shunting locomotive as an example. Following the principle of co-evolutionary problem solving, the overall problem is divided into smaller subproblems (SP). These SP have the right size to be solved by an own agent using genetic algorithms. In addition to, the solution focuses on the autonomous negotiation of an acceptable solution for the entire system by the SP agents.

Autonomous negotiation for resource allocation of multiple sectors in the TFT-LCD manufacturing industry

In a global TFT-LCD manufacturing enterprise, a central planning sector promises orders to maximize profit, while the production sector is responsible for minimizing costs and meeting delivery deadline. The two sectors frequently struggle with different preferences. The contradictory objectives raise conflict between the two sectors when developing an entire resource allocation plan for the enterprise. This study presents a novel negotiation framework and develops a mutually acceptable resource allocation plan via autonomous negotiation between sectors with different preferences. A mathematical model considering the major characters of the TFT-LCD industry is formulated. Individual sectors can employ preferred negotiation tactics to achieve their objectives with an acceptable level of trade-off. This study examines various negotiation tactics and compares the proposed decentralized model with a centralized solution. Negotiation experiments demonstrate a good resource allocation plan over a short time and the conflict between sectors can be resolved efficiently. The proposed autonomous negotiation facilitates smart operations management.

Towards autonomous creation of service chains on cloud markets

Today, cloud services, such as virtual machines, are traded directly at fixed prices between consumers and providers on platforms, e.g. Amazon EC2. The recent development of Amazon's EC2 spot market shows that dynamic cloud markets are gaining popularity. Hence, autonomous multi-round bilateral negotiations, also known as bazaar negotiations, are a promising approach for trading cloud services on future cloud markets. They play a vital role for composing service chains. Based on a formal description we describe such service chains and derive different negotiation types. We implement them in a simulation environment and evaluate our approach by executing different market scenarios. Therefore, we developed three negotiation strategies for cloud resellers. Our simulation results show that cloud resellers as well as their negotiation strategies have a significant impact on the resource allocation of cloud markets. Very high as well as very low mark-ups reduce the profit of a reseller.

Research on New Model of Business English Negotiation Talent Cultivation Based on Flipped Classroom Mode

The new teaching model of flipping classrooms has been widely used in business English negotiation talents in recent years. With the valuable time in the classroom, students can concentrate more on active project-based learning, jointly study and solve the challenges of localization or globalization and other real world problems, thus gaining a deeper understanding. Teachers no longer occupy classroom time to teach information, which requires students to complete autonomous learning before class. They can watch video lectures, listen to podcasts, read enhanced e-books, and discuss with other students on the Internet, and access the required materials at any time. Teachers can also have more time to communicate with everyone. This model subverts the traditional teaching mode and is loved by teachers and students. Through the application of flipped classroom in business English negotiation course teaching, this paper draws a conclusion that flipped classroom is very helpful to the smooth development of business English negotiation classroom activities and to the improvement of learners’ autonomous learning ability and business negotiation skills in the process of training business English negotiation talents.

Business Level Objectives of Customer for Autonomous Cloud Service Negotiation

Business Level Objectives of Customer for Autonomous Cloud Service Negotiation

Coalitional Control for Self-Organizing Agents

Coalitional control is concerned with the management of multi-agent systems where cooperation cannot be taken for granted (due to, e.g., market competition, logistics). This paper proposes a model predictive control (MPC) framework aimed at large-scale dynamically-coupled systems whose individual components, possessing a limited model of the system, are controlled independently, pursuing possibly competing objectives. The emergence of cooperating clusters of controllers is contemplated through an autonomous negotiation protocol, based on the characterization as a coalitional game of the benefit derived by a broader feedback and the alignment of the individual objectives. Specific mechanisms for the cooperative benefit redistribution that relax the cognitive requirements of the game are employed to compensate for possible local cost increases due to cooperation. As a result, the structure of the overall MPC feedback can be adapted online to the degree of interaction between different parts of the system, while satisfying the individual interests of the agents. A wide-area control application for the power grid with the objective of minimizing frequency deviations and undesired inter-area power transfers is used as study case.

Value Co-creative Manufacturing System for Mass Customization: Concept of Smart Factory and Operation Method Using Autonomous Negotiation Mechanism

This paper proposes smart manufacturing systems for tailor-made rubber runninng shoes production. With the development of information and communication technology, manufacturing machines and products embedded with intelligent sensing devices can connect to each other, and acquire the current conditions and changes of each machine and product. Drastic changes in production fields based on the IoT (Internet of Things) technology have been occurred, and then several countries and companies have been tackling the development of smart manufacturing system such as Industrie 4.0 and Industrial Internet Consortium (IIC). This paper focuses a concept of value co-creation for improving users’ delights, and describes the constructed smart factory model system based on Cyber-Physical Systems (CPS). The current conditions and changes of the simulated production machines and products can be acquired using RF-ID systems. Our smart factory is implemented using a multi-agent system and managed in accordance with the mechanism of the autonomous negotiation such as a combinatorial auction mechanism.

Applying Saaty’s Multicriterial Decision Making Approach in Grid Resource Management

In this paper we consider combining ontologically represented information with Saaty’s Analytic Hierarchy Process (AHP) to facilitate decision support for Grid users. The context for the proposal is provided by the Agents in Grid project (AiG), which aims at development of an agent-based infrastructure for efficient resource management in the Grid. In the AiG project, agents form teams, managers of which negotiate with clients and workers terms of potential collaboration. Here, we focus on the scenario, in which the user is searching for resources to execute a task, while the resources and the expert domain knowledge are organized in an ontology. Taking into account the complex nature of resource description and domain knowledge, multicriterial assessment of how accurate is the user description of her needs, and how it can be extended/refined, plays a crucial role. For instance, it should help the user to choose optimal algorithm and/or resource to solve her problem. Furthermore, ontologically described contract proposals, that are the results of autonomous negotiations, require multicriterial assessment. The AHP method is based on pairwise comparisons of criteria, and relies on the judgment of a panel of experts. In the context of the AiG project, we show on the example of the AHP method, how multicriteria group decision making can be used to support user in resource selection and assessment of contract proposals. DOI: http://dx.doi.org/10.5755/j01.itc.43.1.4587

A Framework of Multi Objectives Negotiation for Dynamic Supply Chain Model

Trends of globalization and advances in Information Technology (IT) have created opportunity in collaborative manufacturing across national borders. A dynamic supply chain utilizes these advances to enable more flexibility in business cooperation. This research proposes a concurrent decision making framework for a three echelons dynamic supply chain model. The dynamic supply chain is formed by autonomous negotiation among agents based on multi agents approach. Instead of generating negotiation aspects (such as amount, price and due date) arbitrary, this framework proposes to utilize the information available at operational level of an organization in order to generate realistic negotiation aspect. The effectiveness of the proposed model is demonstrated by various case studies.

Interplay of Consent and Norms in the Autonomous Negotiation in Dispute Settlement

Interplay of Consent and Norms in the Autonomous Negotiation in Dispute Settlement

TuBaFrost 4: Access rules and incentives for a European tumour bank

When designing infrastructure for a networked virtual tumour bank (samples remain at the collector institutes and sample data are collected in a searchable central database), it is apparent that this can only function properly after developing an adequate set of rules for use and access. These rules must include sufficient incentives for the tissue sample collectors to remain active within the network and maintain sufficient sample levels in the local bank. These requirements resulted in a key TuBaFrost rule, stating that the custodianship of the samples remains under the authority of the local collector. As a consequence, the samples and the decision to issue the samples to a requestor are not transferred to a large organisation but instead remain with the collector, thus allowing autonomous negotiation between collector and requestor, potential co-authorship in publications or compensation for collection and processing costs. Furthermore, it realises a streamlined cost effective network, ensuring tissue visibility and accessibility thereby improving the availability of large amounts of samples of highly specific or rare tumour types as well as providing contact opportunities for collaboration between scientists with cutting edge technology and tissue collectors. With this general purpose in mind, the rules and responsibilities for collectors, requestors and central office were generated.

Towards Autonomous and Automatic Evaluation and Negotiation in Agent-Mediated Internet Marketplaces

In an agent-mediated B2C Internet marketplace, agents, mobile or stationary, can act on behalf of customers to complete tasks involved in buying and selling products. In this paper, we first present a fuzzy-set based two-phase evaluation model allowing mobile agents to evaluate and filter online e-shops, and evaluate offers autonomously and automatically at the request from a consumer. The fuzzy evaluation criterion takes into account not only the attributes of offers, but also the reputation of corresponding e-shops. }Meanwhile, the two-phase operations can help reduce the scale of mobile agents dispatched to e-shops and thus reduce the network workload. Based on the same fuzzy evaluation criterion, an auction-like negotiation model is presented, where the consumer agent can autonomously determine the initial offer and negotiate with multiple shops simultaneously. The final best offer is determined not only by the attributes of the offers, but also the attributes of the e-shops as well as the preference the consumer specified. Finally, our models presented in this paper have been implemented in a prototype system where we conducted some experiments to evaluate their performance.

Going undercover [supply chain management

It is perceived that multiagents dedicated to business are likely to revolutionize commercial practices in the years to come. One of the ways they would do this is by completely changing the nature of the supply chain through autonomous coordination and negotiation. If multiagents represent the different activities of the supply chain, this would improve coordination mainly because of the decentralized nature of the multiagent-oriented approach. The main idea is to automate the supply chain so that managers are not just left with number crunching, and are free to take decisions where technology is not sufficient.

Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping

Comparative shopping is a promising web service in the field of mobile commerce. This paper aims to propose a context-aware comparative shopping. Multi-agent intelligent architecture is adopted to implement the autonomous negotiation mechanism between buyers and sellers. To automatically estimate user preferences to determine the best purchase, case-based reasoning and negotiation mechanism are utilized. We developed a prototype system and experiment to show the possibility of the mechanism proposed in this paper. We found that our mechanism with multi-agents yields more pay-off, total sales, and wins than the system without those features.

The equity of mediated divorce agreements

AbstractEmpirical studies come to contradictory conclusions about the dispute resolution forum women can expect to be most equitable in arrangements associated with separation and divorce. Structured telephone interviews were conducted with 302 divorced individuals who not only mediated their separation and divorce issues but also used other methods such as lawyer negotiation, judicial assistance, and autonomous negotiation. Mediation took place in both public and private arenas. Although mediation does not exacerbate the financial woes of women after divorce any more than any of the other dispute resolution techniques, it may not protect women either.