An incorporated RUU model for multi-agent systems in e-commerce

2020 ◽  
Vol 33 (5) ◽  
pp. 905-921
Author(s):  
Elham Majd ◽  
Mark Hobson
Keyword(s):  
Computational Models ◽  
Multi Agent Systems ◽  
Trust Mechanism ◽  
Content Type ◽  
Agent Systems ◽  
Average Accuracy ◽  
Proposed Model ◽  
Multi Agent ◽  
Trust Models ◽  
Two Stages

PurposeThe purpose of this paper is to enhance trust in e-commerce multi-agent systems by presenting a model, called RUU, to select the most trustworthy provider agent based on learning from previous interactions and computing reliability, unreliability and uncertainty.Design/methodology/approachThe methodology comprises analyzing the most representative existing trust models, while a new concept was proposed and measured as unreliability. To make decision about the agents, RUU integrated reliability, unreliability and uncertainty components and used the TOPSIS multi-criteria decision method to select the most trustworthy provider agent. To evaluate the RUU model, the experimentation was carried out in two stages. First, the average accuracy of the model was investigated by simulating RUU in a multi-agent environment. Second, the performance of the model was compared with other related trust models.FindingsThe experimental results revealed that RUU model outperforms current models in providing accurate credibility measurements and computing an accurate trust mechanism for agents, also presenting a decision-making process to choose the most trustworthy provider agent.Research limitations/implicationsThe model presented based on different mathematical computations that take time to be calculated, which is a big limitation of computational models.Practical implicationsRUU enables an agent to make effective and sound decisions in light of the uncertainty that exists in e-commerce multi-agent environments.Originality/valueThis paper is beneficial to enhance the fulfilment of purchasing between provider and requester agents. In fact, the proposed model can ensure critical transactions performed securely in e-commerce multi-agent environments.

A multi-agent systems-based contractor pre-qualification model

2016 ◽  
Vol 23 (6) ◽  
pp. 709-726 ◽  
Author(s):  
Faikcan Kog ◽  
Hakan Yaman
Keyword(s):  
Selection Process ◽  
Construction Sector ◽  
Multi Agent Systems ◽  
Content Type ◽  
Quantitative Survey ◽  
Agent Systems ◽  
Proposed Model ◽  
Multi Agent ◽  
User Friendly ◽  
Selection Of

Purpose The selection of the contractor, as a main participant of a construction project, is the most important and challenging decision process for a client. The purpose of this paper is to propose a multi-agent systems (MAS)-based contractor pre-qualification (CP) model for the construction sector in the frame of the tender management system. Design/methodology/approach The meta-classification and analysis study of the existing literature on CP, contractor selection and criteria weighting issues, which examines the current and important CP criteria, other than price, is introduced structurally. A quantitative survey, which is carried out to estimate initial weightings of the identified criteria, is overviewed. MAS are used to model the pre-qualification process and workflows are shown in Petri nets formalism. A user-friendly prototype program is created in order to simulate the tendering process. In addition, a real case regarding the construction work in Turkey is analyzed. Findings There is a lack of non-human-driven solutions and automation in CP and in the selection problem. The proposed model simulates the pre-qualification process and provides consistent results. Research limitations/implications The meta-classification study consists of only peer-reviewed papers between 1992 and 2013 and the quantitative survey initiates the perspectives of the actors of Turkish construction sector. Only the traditional project delivery method is selected for the proposed model, that is other delivery methods such as design/build, project management, etc., are not considered. Open, selective limited and negotiated tendering processes are examined in the study and the direct supply is not considered in the scope. Practical implications The implications will help to provide an objective CP and selection process and to prevent the delays, costs and other troubles, which are caused by the false selection of a contractor. Originality/value Automation and simulation in the pre-qualification and the selection of the contractor with a non-human-driven intelligent solution ease the decision processes of clients in terms of cost, time and quality.

Neural-network-based containment control of nonlinear multi-agent systems under communication constraints

2016 ◽  
Vol 36 (2) ◽  
pp. 179-185 ◽  
Author(s):  
Chao Ma
Keyword(s):  
Neural Network ◽  
Continuous Time ◽  
Sampled Data ◽  
Multi Agent Systems ◽  
Communication Constraints ◽  
Containment Control ◽  
Content Type ◽  
Agent Systems ◽  
The Neural Network ◽  
Multi Agent

Purpose The purpose of this paper is to investigate the neural-network-based containment control of multi-agent systems with unknown nonlinear dynamics. Moreover, communication constraints are taken into account to reflect more realistic communication networks. Design/methodology/approach Based on the approximation property of the radial basis function neural networks, the control protocol for each agent is designed, where all the information is exchanged in the form of sampled data instead of ideal continuous-time communications. Findings By utilizing the Lyapunov stability theory and the Lyapunov–Krasovskii functional approach, sufficient conditions are developed to guarantee that all the followers can converge to the convex hull spanned by the stationary leaders. Originality/value As ideal continuous-time communications of the multi-agent systems are very difficult or even unavailable to achieve, the neural-network-based containment control of nonlinear multi-agent systems is solved under communication constraints. More precisely, sampled-data information is exchanged, which is more applicable and practical in the real-world applications.

Containment control of second-order multi-agent systems with directed topology and time-delays

Kybernetes ◽  
2014 ◽  
Vol 43 (8) ◽  
pp. 1248-1261 ◽  
Author(s):  
Bin Qi ◽  
Xuyang Lou ◽  
Baotong Cui
Keyword(s):  
Time Delay ◽  
Time Delays ◽  
Second Order ◽  
Multi Agent Systems ◽  
Containment Control ◽  
Content Type ◽  
Directed Topology ◽  
Agent Systems ◽  
Communication Time ◽  
Multi Agent

Purpose – The purpose of this paper is to discuss the impacts of the communication time-delays to the distributed containment control of the second-order multi-agent systems with directed topology. Design/methodology/approach – A basic theoretical analysis is first carried out for the containment control of the second-order multi-agent systems under directed topology without communication time-delay and a sufficient condition is proposed for the achievement of containment control. Based on the above result and frequency-domain analysis method, a sufficient condition is also derived for the achievement of containment control of the second-order multi-agent systems under directed topology with communication time-delays. Finally, simulation results are presented to support the effectiveness of the theoretical results. Findings – For the achievement of containment control of the second-order multi-agent systems under directed topology with communication time-delay, the control gain in the control protocols is completely dependent on the communication topology structure and the maximum of time-delay in the control protocols is dependent on the given control gain and communication topology structure. Originality/value – The paper investigates the containment control of the second-order multi-agent systems under directed topology with communication time-delays and presents a sufficient conditions for the achievement of containment control. The results and approach proposed in the paper may benefit interesting researchers.

scholarly journals A multi-agent based system with big data processing for enhanced supply chain agility

2016 ◽  
Vol 29 (5) ◽  
pp. 706-727 ◽  
Author(s):  
Mihalis Giannakis ◽  
Michalis Louis
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Semantic Web ◽  
Supply Chains ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Agent Systems ◽  
Supply Chain Agility ◽  
Multi Agent

Purpose Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

R2-IBN

2009 ◽  
pp. 144-157
Author(s):  
Lobna Hsairi ◽  
Khaled Ghédira ◽  
Adel M. Alim ◽  
Abdellatif BenAbdelhafid
Keyword(s):  
Information Management ◽  
Organizational Structures ◽  
Agent Technology ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Intelligent Software ◽  
Proposed Model ◽  
Multi Agent ◽  
Collaboration And Communication ◽  
Negotiation Problem

In the age of information proliferation, openness, open information management, interconnectivity, collaboration and communication advances, extended enterprises must be up to date to the new strategic, economic and organizational structures. Consequently, intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the information management, cooperation, collaboration and interconnectivity. Thus, within these interconnectivity and cooperation, conflicts may arise. The automated negotiation plays a key role to look for a common agreement. Argumentation theory has become an important topic in the field of Multi-Agent Systems and especially in the negotiation problem. In this chapter, first, the proposed model MAIS-E2 (Multi-Agent Information System for an Extended Enterprise) is presented. Then an argumentation based negotiation framework: Relationship-Role and Interest Based Negotiation (R2-IBN) framework is presented, and within this framework, the authors focused mainly on, argument generation module via inference rules and argument selection module via fuzzy logic.

scholarly journals Fairness in multi-agent systems

2008 ◽  
Vol 23 (2) ◽  
pp. 153-180 ◽  
Author(s):  
STEVEN DE JONG ◽  
KARL TUYLS ◽  
KATJA VERBEECK
Keyword(s):  
Behavioral Economics ◽  
Computational Models ◽  
Computer Software ◽  
Multi Agent Systems ◽  
Agent Model ◽  
Agent Systems ◽  
Multi Agent ◽  
Classical Game ◽  
Descriptive Models ◽  
Classical Game Theory

AbstractMulti-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care aboutfairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.

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