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Journal articles on the topic 'Multi agent system-Supplier selection'

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Published: 6 September 2023

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1

Yang, Chang Hui. "Supplier Selection of Multi-Agent Logistic System." Key Engineering Materials 467-469 (February 2011): 614–19. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.614.

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Choosing supplier with better quick response ability becomes more and more important. In this paper, the criterion of evaluating supplier is put forward and a method of evaluating supplier is introduced. To improve the efficiency of selecting supplier, a multi-agent system of supplier selection based on evaluating supplier is developed. Recurring to the supplier’s related data collected by data-mining agent from external web-server, the weights of criteria can be confirmed. And using the system, the supplier with better QRA can be selected based on the measuring results.
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2

Jahani, Alireza, Masrah Azrifah Azmi Murad, Md Nasir bin Sulaiman, and Mohd Hasan Selamat. "An agent-based supplier selection framework." Strategic Outsourcing: An International Journal 8, no. 2/3 (June 15, 2015): 180–205. http://dx.doi.org/10.1108/so-01-2015-0003.

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Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.
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Changhui Yang. "Supplier Selection Mechanism of Logistic Enterprise based on Multi-Agent System." International Journal of Advancements in Computing Technology 4, no. 16 (September 30, 2012): 437–44. http://dx.doi.org/10.4156/ijact.vol4.issue16.51.

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4

Li, Jinghua, Miaomiao Sun, Duanfeng Han, Xiaoyuan Wu, Boxin Yang, Xuezhang Mao, and Qinghua Zhou. "Semantic multi-agent system to assist business integration: An application on supplier selection for shipbuilding yards." Computers in Industry 96 (April 2018): 10–26. http://dx.doi.org/10.1016/j.compind.2018.01.001.

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5

Kumar, Bimal Aklesh, and Priya Mohite. "Cognitive Trust Model for B2B E-Market." International Journal of E-Business Research 11, no. 4 (October 2015): 32–46. http://dx.doi.org/10.4018/ijebr.2015100103.

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In recent years, B2B E-Market has received widespread amount of research attention in the field of electronic commerce. As majority of companies are investing huge amount of money to build the infrastructure for e-collaboration. Selecting suitable trading partners in an e-market still remains a huge challenge, as the selection of supplier depends not only on cost but on other factors such as reputation, satisfaction and trust. Trust is regarded as one of the most important factors for success and to ensure customers repeat trading. One of the major challenges faced by both research and industry is to measure trustworthiness of a supplier in an e-market place. This paper proposes a cognition based model to measure trustworthiness of a supplier. The proposed model with its implementation using multi-agent systems is described in detail. Finally empirical evaluation is carried out to validate the system and future direction for research in this area.
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Musumba, George Wamamu, and Patrick Kanyi Wamuyu. "Virtual Enterprise as a Multi Agent System." International Journal of Agent Technologies and Systems 8, no. 1 (January 2016): 46–68. http://dx.doi.org/10.4018/ijats.2016010103.

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This article describes changing customer demands require that enterprises mobilize their resources to quickly develop a suitable product. This is achievable if competing enterprises collaborate to deliver the product. Each of them brings their expertise into the collaboration. This collaboration where each enterprise brings in its core competency is referred to as a virtual enterprise (VE). A construction project is implemented by a team of professionals and an alliance of companies that is formed by consultants who evaluate contractors for specific project tasks. Partners can be represented as multiple agents. Prior evidence of multi-agent system (MAS) model that facilitates formation of VEs is lacking. VE MAS ontology has been designed and used in agent interactions. The model can be used in evaluation and selection process of partners. Delegation of the process to the model, gives partners time to implement the tasks. Partner evaluation and selection problem for building construction projects is solvable if pragmatic scientific approaches are employed with appropriate mathematical models. This article proposed a VE model for evaluating and selecting right partners for building construction projects. The model was used to demonstrate the choice of the most preferred partner. Researchers have not evaluated this model but propose that once in place, it can evaluated against manual selection of potential partners using similar parameters by examining the closeness of the output.
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Yu, Chunxia, and T. N. Wong. "A multi-agent architecture for multi-product supplier selection in consideration of the synergy between products." International Journal of Production Research 53, no. 20 (February 16, 2015): 6059–82. http://dx.doi.org/10.1080/00207543.2015.1010745.

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Yu, Chunxia, T. N. Wong, and Zhi Li. "A hybrid multi-agent negotiation protocol supporting supplier selection for multiple products with synergy effect." International Journal of Production Research 55, no. 1 (May 25, 2016): 18–37. http://dx.doi.org/10.1080/00207543.2016.1189105.

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9

Han, Wei, Bing Zhang, Qianyi Wang, Jun Luo, Weizhi Ran, and Yang Xu. "A Multi-Agent Based Intelligent Training System for Unmanned Surface Vehicles." Applied Sciences 9, no. 6 (March 15, 2019): 1089. http://dx.doi.org/10.3390/app9061089.

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The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) where no historical data concerning the behavior is available. In this approach, agents are built as the physical controller of each USV and their cooperative decisions used for the USVs’ group coordination. To make our multi-agent system intelligently coordinate USVs, we built a multi-agent-based learning system. First, an agent-based data collection platform is deployed to gather competition data from agents’ observation for on-line learning tasks. Second, we design a genetic-based fuzzy rule training algorithm that is capable of optimizing agents’ coordination decisions in an accumulated manner. The simulation results of this study demonstrate that our proposed training approach is feasible and able to converge to a stable action selection policy towards efficient multi-USVs’ cooperative decision making.
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Ghadimi, Pezhman, Chao Wang, Ming K. Lim, and Cathal Heavey. "Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains." Computers & Industrial Engineering 127 (January 2019): 588–600. http://dx.doi.org/10.1016/j.cie.2018.10.050.

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11

Liu, Xiaoli. "Research on decision-making strategy of soccer robot based on multi-agent reinforcement learning." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142091696. http://dx.doi.org/10.1177/1729881420916960.

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This article studies a multi-agent reinforcement learning algorithm based on agent action prediction. In multi-agent system, the action of learning agent selection is inevitably affected by the action of other agents, so the reinforcement learning system needs to consider the joint state and joint action of multi-agent based on this. In addition, the application of this method in the cooperative strategy learning of soccer robot is studied, so that the multi-agent system can pass through the environment. To realize the division of labour and cooperation of multi-robots, the interactive learning is used to master the behaviour strategy. Combined with the characteristics of decision-making of soccer robot, this article analyses the role transformation and experience sharing of multi-agent reinforcement learning, and applies it to the local attack strategy of soccer robot, uses this algorithm to learn the action selection strategy of the main robot in the team, and uses Matlab platform for simulation verification. The experimental results prove the effectiveness of the research method, and the superiority of the proposed method is validated compared with some simple methods.
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Acampora, G., J. M. Cadenas, V. Loia, and E. M. Ballester. "A Multi-Agent Memetic System for Human-Based Knowledge Selection." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 41, no. 5 (September 2011): 946–60. http://dx.doi.org/10.1109/tsmca.2011.2109376.

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Bellakhal, Raja, and Khaled Ghédira. "A multi-agent-based negotiation system for web service selection." International Journal of Knowledge and Web Intelligence 5, no. 4 (2016): 253. http://dx.doi.org/10.1504/ijkwi.2016.084739.

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Ghédira, Khaled, and Raja Bellakhal. "A multi-agent-based negotiation system for web service selection." International Journal of Knowledge and Web Intelligence 5, no. 4 (2016): 253. http://dx.doi.org/10.1504/ijkwi.2016.10005792.

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Mazumdar, Bireshwar Dass, Swati Basak, and Neelam Modanwal. "Multi-Agent Negotiation Paradigm for Agent Selection in B2C E-Commerce." International Journal of Agent Technologies and Systems 3, no. 2 (April 2011): 33–52. http://dx.doi.org/10.4018/jats.2011040103.

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Multi agent system (MAS) model has been extensively used in the different tasks of E-Commerce such as customer relation management (CRM), negotiation and brokering. The objective of this paper is to evaluate a seller agent’s various cognitive parameters like capability, trust, and desire. After selecting a best seller agent from ordering queue, it applies negotiation strategies to find the most profitable proposal for both buyer and seller. This mechanism belongs to a semi cooperative negotiation type, and selecting a seller and buyer agent pair using mental and cognitive parameters. This work provides a logical cognitive model, logical negotiation model between buyer agent and selected seller agent.
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Naeem, Muddasar, Antonio Coronato, Zaib Ullah, Sajid Bashir, and Giovanni Paragliola. "Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning." Sensors 22, no. 21 (October 28, 2022): 8278. http://dx.doi.org/10.3390/s22218278.

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Multiple Input Multiple Output (MIMO) systems have been gaining significant attention from the research community due to their potential to improve data rates. However, a suitable scheduling mechanism is required to efficiently distribute available spectrum resources and enhance system capacity. This paper investigates the user selection problem in Multi-User MIMO (MU-MIMO) environment using the multi-agent Reinforcement learning (RL) methodology. Adopting multiple antennas’ spatial degrees of freedom, devices can serve to transmit simultaneously in every time slot. We aim to develop an optimal scheduling policy by optimally selecting a group of users to be scheduled for transmission, given the channel condition and resource blocks at the beginning of each time slot. We first formulate the MU-MIMO scheduling problem as a single-state Markov Decision Process (MDP). We achieve the optimal policy by solving the formulated MDP problem using RL. We use aggregated sum-rate of the group of users selected for transmission, and a 20% higher sum-rate performance over the conventional methods is reported.
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Raduenz, Henrique, Liselott Ericson, Victor J. De Negri, and Petter Krus. "Multi-Chamber Actuator Mode Selection through Reinforcement Learning–Simulations and Experiments." Energies 15, no. 14 (July 13, 2022): 5117. http://dx.doi.org/10.3390/en15145117.

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This paper presents the development and implementation of a reinforcement learning agent as the mode selector for a multi-chamber actuator in a load-sensing architecture. The agent selects the mode of the actuator to minimise system energy losses. The agent was trained in a simulated environment and afterwards deployed to the real system. Simulation results indicated the capability of the agent to reduce energy consumption, while maintaining the actuation performance. Experimental results showed the capability of the agent to learn via simulation and to control the real system.
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Sánchez-Rivera, Félix Andrés, Miguel Ángel Leguizamón-Páez, and Jheshua Dannand Jared Larrota-Alférez. "Multi-agent system as support process of transplant of organs." Visión electrónica 1, no. 2 (August 13, 2018): 349–63. http://dx.doi.org/10.14483/22484728.18398.

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This article is a review that leads to build a state of knowledge about Multi-Agent Systems based on Selection and Search (ISSA) applied in the search and selection of organ and tissue transplant recipients, emphasizing as a case study the heart, using Geo-location. In particular, this research analyzes technical, scientific and normative aspects of ISSA, between 2007 and 2017, in Europe (Spain) and Latin America (Colombia). A base line of systems based on Artificial Intelligence is thus obtained for the selection and search of transplant recipients, before a possible demand of the List of Persons Waiting for Donation (LED). From the above, solutions can be implemented reducing time in the allocation of organs taking into account their characteristics and compatibility: blood group, size, location criteria, among others, from one to several possible recipients. Finally, a technological solution model for Colombia is proposed.
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Mazumdar, Bireshwar Dass, and R. B. Mishra. "Customer Orientation Based Multi-Agent Negotiation for B2C e-Commerce." International Journal of Agent Technologies and Systems 2, no. 2 (April 2010): 24–48. http://dx.doi.org/10.4018/jats.2010040103.

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The Multi Agent System (MAS) model has been extensively used in different e-commerce tasks. Customer orientation is a crucial component of organizational culture and attention to information about customers’ needs should be considered a firm’s most basic value. In this paper, the authors evaluate a seller agent’s various cognitive parameters, such as capability, trust, and desire. The authors also examine customer orientation value for a pair of buyer and seller agents, such as selecting a seller and buyer agent pair using mental and cognitive parameters, customer orientation value for their mutual benefits, and the competitive negotiation factor. The authors provide a deterministic customer orientation computational model, a competitive negotiation computational model between buyer agent and seller agent, and a logical computational model for selection of the best buyer-seller agent pair. With the help of GUI intermediate, final results are shown.
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20

Ghadimi, Pezhman, Farshad Ghassemi Toosi, and Cathal Heavey. "A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain." European Journal of Operational Research 269, no. 1 (August 2018): 286–301. http://dx.doi.org/10.1016/j.ejor.2017.07.014.

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21

Khavina, I. P., Yu V. Hnusov, and O. O. Mozhaiev. "Development of multi-agent information security management system." Law and Safety 87, no. 4 (December 21, 2022): 171–83. http://dx.doi.org/10.32631/pb.2022.4.14.

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The issue of creating an information security system is very relevant in the world today. One of the urgent tasks is to solve the issues of effective protection of information from both external and internal threats through the creation and implementation of information security management systems in automated systems of enterprises, which, among other things, requires the formalization of the task of protecting information for its subsequent implementation by software and other means. Now there are security analysis systems, for example, that examine the security elements settings of workstations and servers operating systems, analyze the network topology, look for unprotected network connections, examine the settings of firewalls. The disadvantage of these systems is that they are not suitable for monitoring large volumes of network traffic. The solution to this problem is the use of monitoring tools capable of analyzing large amounts of data in real time. Therefore, a significant place in the article is given to the review of developments based on artificial intelligence technologies, namely multi-agent systems, review of information security models, threat risk assessment in automated systems. The functional architecture of the information security management system based on a multi-agent system has been proposed to search in real time for information security optimal solutions through the selection of such coalitions of protection mechanisms agents that will allow to build the optimal protection of the automated system according to the selected criteria. The model with complete overlapping of threats has been substantiated and adopted as a basis, which allows to analyze the overall situation and choose strategically important decisions directly during the organization of information security. The essence of of multi-agent systems functioning that implement a decentralized control system based on the work of autonomous agents that can be implemented programmatically has been revealed. The role of threat agents, resource agents, agents of protection mechanisms and their functional purpose have been defined. The problem of searching a set of protection mechanisms agents coalition for the current state of the automated system as a problem of optimal search by the criterion of protection cost, taking into account the value of information, has been generalized. Due to the modularity of the multi-agent system, the further work will be aimed at detailing its components and perfection.
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Liu, Zhi Yue, and Jian Wang. "A Research into an Intrusion Detection System Based on Immune Principle and Multi-Agent in WSN." Advanced Materials Research 433-440 (January 2012): 5157–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5157.

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Along with deep-going researches and practical applications of Wireless Sensor Network, security issues contained therein are raising a growing concern. Based on a systematic analysis of the current intrusion detection system and with integration between the mechanism of artificial immune system and multi-agent technology, the paper provides with a new model of an intrusion detection system based on artificial immune system and multi-agent technology, and further introduces the functions of agents in the model as well as the process of antibody selection.
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23

Zhang, Yong Mei, and Li Ma. "Feature Selection for Object Recognition by Property Related Analysis." Key Engineering Materials 480-481 (June 2011): 323–28. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.323.

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The realization of object recognition is discussed, the intrinsic relation between factors through high step association analysis is presented to mine the various features using property related analysis, the sensitive recognition features are chosen to improve the recognition efficiency. A multi-agent object recognition model(MAORM) is established, which combines concurrency research results and the specific characteristics of multi-sensor remote sensing image recognition. A majority-decision algorithm based on multi-agent is proposed. Experiment results show the system can effectively identify the bridges, wharfs, ships and so on. Compared with a single remote sensing image, the system can distinguish targets with higher recognition.
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Xia, Zheng You, and Chen Ling Gu. "The Role of Belief in the Emergence of Social Conventions in Artificial Social System." Advanced Materials Research 159 (December 2010): 210–15. http://dx.doi.org/10.4028/www.scientific.net/amr.159.210.

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The emergence of social conventions in multi-agent systems has been analyzed mainly by considering a group of homogeneous autonomous agents that can reach a global agreement using locally available information. We use novel viewpoint to consider that the process through which agents coordinate their behaviors to reduce conflict is also the process agents use to evaluate trust relations with their neighbors during local interactions. In this paper, we propose using the belief update rule called Instances of Satisfying and Dissatisfying (ISD) to study the evolution of agents' beliefs during local interactions. We also define an action selection rule called “highest cumulative belief” (HCB) to coordinate their behavior to reduce conflicts among agents in MAS (multi-agent systems). We find that the HCB can cause a group of agents to achieve the emergence of social conventions. Furthermore, we discover that if a group of agents can achieve the emergence of social conventions through ISD and HCB rules in an artificial social system, after a number of iterations this group of agents can enter the harmony state wherein each agent fully believes its neighbors.
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Benmoussa, Khadidja, Djamila Hamdadou, and Zine El Abidine Roukh. "Spatial Group Decision Support System to Facilitate Site Selection Negotiation Process, Reduce Decision-Maker Conflict, and Augment System Performance." International Journal of Organizational and Collective Intelligence 12, no. 1 (January 1, 2022): 1–28. http://dx.doi.org/10.4018/ijoci.304884.

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The selection of a suitable land requires careful thought and several criteria must be taken into account to ensure a good decision. Typically, selection processes are based on face-to-face meetings. But this strategy focuses on simple predefined searches and selections according to some basic criteria, such as land price, etc. They do not take into account spatially explicit criteria and spatial analyzes relevant to a site selection process (for example, proximity to hospital). To overcome these limitations, the proposed system relies on the integration of four key concepts: a multi-agent system, a multi-criteria method, geographic and ontological information systems. The results of the implementation provide manageability of large data storage. Despite the large number of approaches integrating geographic information systems and multicriteria analyzes, their use, in practice, is limited because they lack data semantics. As a result, the concept of ontology which introduces the semantics of the proposed approach offers many possibilities in terms of the type of terrain.
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26

Sadigh, B. Lotfi, F. Arikan, A. M. Ozbayoglu, H. O. Unver, and S. E. Kilic. "A Multi-agent System Model for Partner Selection Process in Virtual Enterprise." Procedia Computer Science 36 (2014): 367–72. http://dx.doi.org/10.1016/j.procs.2014.09.007.

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27

Basak, Swati, and Bireshwar Dass Mazumdar. "Multi-Agent Coalition Formation for Course Selection Strategies in E-Learning System." International Journal of Mathematics Trends and Technology 6, no. 1 (February 25, 2014): 36–43. http://dx.doi.org/10.14445/22315373/ijmtt-v6p503.

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28

Miao, Yongfei, Luo Zhong, Yufu Yin, Chengming Zou, and Zhenjun Luo. "Research on dynamic task allocation for multiple unmanned aerial vehicles." Transactions of the Institute of Measurement and Control 39, no. 4 (February 1, 2017): 466–74. http://dx.doi.org/10.1177/0142331217693077.

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To solve the distributed task allocation problems of search and rescue missions for multiple unmanned aerial vehicles (UAVs), this paper establishes a dynamic task allocation model under three conditions: 1) when new targets are detected, 2) when UAVs break down and 3) when unexpected threats suddenly occur. A distributed immune multi-agent algorithm (DIMAA) based on an immune multi-agent network framework is then proposed. The technologies employed by the proposed algorithm include a multi-agent system (MAS) with immune memory, neighbourhood clonal selection, neighbourhood suppression, neighbourhood crossover and self-learning operators. The DIMAA algorithm simplifies the decision-making process among agents. The simulation results show that this algorithm not only obtains the global optimum solution, but also reduces the communication load between agents.
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Mostafa, Salama A., Mohd Sharifuddin Ahmad, Aida Mustapha, and Mazin Abed Mohammed. "Formulating layered adjustable autonomy for unmanned aerial vehicles." International Journal of Intelligent Computing and Cybernetics 10, no. 4 (November 13, 2017): 430–50. http://dx.doi.org/10.1108/ijicc-02-2017-0013.

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Purpose The purpose of this paper is to propose a layered adjustable autonomy (LAA) as a dynamically adjustable autonomy model for a multi-agent system. It is mainly used to efficiently manage humans’ and agents’ shared control of autonomous systems and maintain humans’ global control over the agents. Design/methodology/approach The authors apply the LAA model in an agent-based autonomous unmanned aerial vehicle (UAV) system. The UAV system implementation consists of two parts: software and hardware. The software part represents the controller and the cognitive, and the hardware represents the computing machinery and the actuator of the UAV system. The UAV system performs three experimental scenarios of dance, surveillance and search missions. The selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant results. Findings The results of the UAV system tests prove that segregating the autonomy of a system as multi-dimensional and adjustable layers enables humans and/or agents to perform actions at convenient autonomy levels. Hence, reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents, increasing humans’ workload and exposing the system to disturbances. Originality/value The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable autonomy. Assessing the autonomy within three phases of agents run cycle (task-selection, actions-selection and actions-execution) is an original idea that aims to direct agents’ autonomy toward performance competency. The agents’ abilities are well exploited when an incompetent agent switches with a more competent one.
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Sadigh, Bahram Lotfi, Shahrzad Nikghadam, A. Murat Ozbayoglu, Hakki Ozgur Unver, Erdogan Dogdu, and S. Engin Kilic. "An ontology-based multi-agent virtual enterprise system (OMAVE): part 2: partner selection." International Journal of Computer Integrated Manufacturing 30, no. 10 (January 31, 2017): 1072–92. http://dx.doi.org/10.1080/0951192x.2017.1285424.

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31

Cabukovski, Vanco, and Roman Golubovski. "An Adaptive Course Materials Selection into a Multi-agent based e-Learning System." International Journal of Computer Applications 144, no. 8 (June 17, 2016): 4–8. http://dx.doi.org/10.5120/ijca2016910401.

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32

HASHIMOTO, R., T. MATSUMURA, Y. NOZATO, K. WATANABE, and T. ONOYE. "Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 10 (October 1, 2008): 2909–17. http://dx.doi.org/10.1093/ietfec/e91-a.10.2909.

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33

BARBUCEANU, MIHAI, and MARK S. FOX. "CAPTURING AND MODELING COORDINATION KNOWLEDGE FOR MULTI-AGENT SYSTEMS." International Journal of Cooperative Information Systems 05, no. 02n03 (June 1996): 275–314. http://dx.doi.org/10.1142/s0218843096000117.

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The agent view provides a level of abstraction at which we envisage computational systems carrying out cooperative work by interoperating globally across networks connecting people, organizations and machines. A major challenge in building such systems is coordinating the behavior of the individual agents to achieve the individual and shared goals of the participants. As part of a larger project targeted at developing an Agent Building Shell for multiagent applications, we have designed and implemented a coordination language aimed at explicitly representing, applying and capturing coordination knowledge for multiagent systems. The language provides KQML-based communication, an agent definition and execution environment, support for modeling interactions as multiple structured conversations among agents, rule-based approaches to conversation selection and execution, as well as an interactive tool for in context acquisition and debugging of cooperation knowledge. The paper presents these components in detail and then shows how the coordination language is used in the Agent Building Shell to manage content-based information distribution scenarios among agents and the coordination aspects of conflict management processes that occur when agents encounter inconsistencies. The major application of the system is the construction and integration of multiagent supply chain systems for manufacturing enterprises. This application is used throughout the paper to illustrate the introduced concepts and language constructs.
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Harmati, István, Ma Lantos, and Shahram Payandeh. "Semi-stratified Motion Planning of Multi-agent Manipulation." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 5 (September 20, 2001): 248–56. http://dx.doi.org/10.20965/jaciii.2001.p0248.

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The paper addresses a manipulation problem based on kinematic model where the equations of the motion of the system can change discontinuously depending on the actual state of the system. In this case, the configuration space is stratified. The stratified control theory using the smooth motion planning algorithm promises a powerful alternative for this manipulation problem, however, it meets also some significant difficulties. The paper presents the main steps of developing computational frame work and some improvements for stratified motion planning. The concept proposes a semi-stratified method which decomposes the manipulation problem into stratified motion planning and pure finger relocation through the special selection of the reference points. Because of the different type of computational tasks (symbolic, numerical), the realization steps are also investigated.
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Wang, Chunni, and Jun Ma. "A review and guidance for pattern selection in spatiotemporal system." International Journal of Modern Physics B 32, no. 06 (February 26, 2018): 1830003. http://dx.doi.org/10.1142/s0217979218300037.

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Pattern estimation and selection in media can give important clues to understand the collective response to external stimulus by detecting the observable variables. Both reaction–diffusion systems (RDs) and neuronal networks can be treated as multi-agent systems from molecular level, intrinsic cooperation, competition. An external stimulus or attack can cause collapse of spatial order and distribution, while appropriate noise can enhance the consensus in the spatiotemporal systems. Pattern formation and synchronization stability can bridge isolated oscillators and the network by coupling these nodes with appropriate connection types. As a result, the dynamical behaviors can be detected and discussed by developing different spatial patterns and realizing network synchronization. Indeed, the collective response of network and multi-agent system depends on the local kinetics of nodes and cells. It is better to know the standard bifurcation analysis and stability control schemes before dealing with network problems. In this review, dynamics discussion and synchronization control on low-dimensional systems, pattern formation and synchronization stability on network, wave stability in RDs and neuronal network are summarized. Finally, possible guidance is presented when some physical effects such as polarization field and electromagnetic induction are considered.
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Zhang, Li, Jie Wu Zhang, and Wei Jun Ma. "Multi-Agent Order Selection and its Optimization Method with Evolution Algorithm." Applied Mechanics and Materials 20-23 (January 2010): 1066–71. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.1066.

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The rapid changes of market and outer surroundings have caused a dynamic and highly volatile business environment for the enterprise. Manufacturing organizations are seeking efficiency gains by competing via fast time-to-market and low production cost. An Autonomous Agent Network(ANN) Based Manufacturing System model is introduced, as well as the mathematics expression for the components of AAN model. The optimization algorithm based on evolution algorithm is used to solve the optimization problem of the model. Simulation results show that the architecture model and its optimization algorithm are effective to the problem.
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37

Kog, Faikcan, and Hakan Yaman. "A multi-agent systems-based contractor pre-qualification model." Engineering, Construction and Architectural Management 23, no. 6 (November 21, 2016): 709–26. http://dx.doi.org/10.1108/ecam-01-2016-0013.

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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.
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38

Ponniah, Jonathan, and Or D. Dantsker. "Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems." Aerospace 9, no. 9 (August 31, 2022): 488. http://dx.doi.org/10.3390/aerospace9090488.

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A system is considered in which agents (UAVs) must cooperatively discover interest-points (i.e., burning trees, geographical features) evolving over a grid. The objective is to locate as many interest-points as possible in the shortest possible time frame. There are two main problems: a control problem, where agents must collectively determine the optimal action, and a communication problem, where agents must share their local states and infer a common global state. Both problems become intractable when the number of agents is large. This survey/concept paper curates a broad selection of work in the literature pointing to a possible solution; a unified control/communication architecture within the framework of reinforcement learning. Two components of this architecture are locally interactive structure in the state-space, and hierarchical multi-level clustering for system-wide communication. The former mitigates the complexity of the control problem and the latter adapts to fundamental throughput constraints in wireless networks. The challenges of applying reinforcement learning to multi-agent systems are discussed. The role of clustering is explored in multi-agent communication. Research directions are suggested to unify these components.
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39

HORLING, BRYAN, and VICTOR LESSER. "A survey of multi-agent organizational paradigms." Knowledge Engineering Review 19, no. 4 (December 2004): 281–316. http://dx.doi.org/10.1017/s0269888905000317.

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Many researchers have demonstrated that the organizational design employed by an agent system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this article we present a survey of the major organizational paradigms used in multi-agent systems. These include hierarchies, holarchies, coalitions, teams, congregations, societies, federations, markets, and matrix organizations. We will provide a description of each, discuss their advantages and disadvantages, and provide examples of how they may be instantiated and maintained. This summary will facilitate the comparative evaluation of organizational styles, allowing designers to first recognize the spectrum of possibilities, and then guiding the selection of an appropriate organizational design for a particular domain and environment.
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TZAFESTAS, ELPIDA S., SPYROS N. RAPTIS, and SPYROS G. TZAFESTAS. "MULTI-AGENT ROBOT ARCHITECTURES: THE DECOMPOSITION ISSUE AND A CASE STUDY." International Journal on Artificial Intelligence Tools 07, no. 02 (June 1998): 163–87. http://dx.doi.org/10.1142/s021821309800010x.

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In this paper, some fundamental issues of modern multi-agent robot architectures are discussed. It is argued that the multi-agent approach provides the necessary flexibility and adaptivity for such architectures, and that the primary issue in designing a multi-agent robot architecture is the selection of the granularity level, i.e., the decision on decomposing the overall desired functionality physically or across tasks. It is explained why at the various system levels different decomposition grains are needed; physical components, tasks or hybrid. This granularity decision is made on the basis of specific criteria of control localization, knowledge decoupling and interaction minimization so as to identify the decision points of the overall functionality. The above criteria lead to a dual composition-decomposition relation, which provides a good basis for system scaling. The paper specializes the discussion to a proposed neuro-fuzzy multi-agent architecture, which is then applied to design the local path planning system of an indoor mobile robot.
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Fang, Xiaohan, Jinkuan Wang, Guanru Song, Yinghua Han, Qiang Zhao, and Zhiao Cao. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling." Energies 13, no. 1 (December 25, 2019): 123. http://dx.doi.org/10.3390/en13010123.

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Residential microgrid is widely considered as a new paradigm of the home energy management system. The complexity of Microgrid Energy Scheduling (MES) is increasing with the integration of Electric Vehicles (EVs) and Renewable Generations (RGs). Moreover, it is challenging to determine optimal scheduling strategies to guarantee the efficiency of the microgrid market and to balance all market participants’ benefits. In this paper, a Multi-Agent Reinforcement Learning (MARL) approach for residential MES is proposed to promote the autonomy and fairness of microgrid market operation. First, a multi-agent based residential microgrid model including Vehicle-to-Grid (V2G) and RGs is constructed and an auction-based microgrid market is built. Then, distinguish from Single-Agent Reinforcement Learning (SARL), MARL can achieve distributed autonomous learning for each agent and realize the equilibrium of all agents’ benefits, therefore, we formulate an equilibrium-based MARL framework according to each participant’ market orientation. Finally, to guarantee the fairness and privacy of the MARL process, we proposed an improved optimal Equilibrium Selection-MARL (ES-MARL) algorithm based on two mechanisms, private negotiation and maximum average reward. Simulation results demonstrate the overall performance and efficiency of proposed MARL are superior to that of SARL. Besides, it is verified that the improved ES-MARL can get higher average profit to balance all agents.
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42

Augie, Peter, and Yulistia Yulistia. "Rancang Bangun Sistem Pendukung Keputusan Rekomendasi Agent Terbaik Dengan Metode SAW." Jurnal Teknologi Sistem Informasi 3, no. 2 (September 14, 2022): 313–21. http://dx.doi.org/10.35957/jtsi.v3i2.3046.

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Insurance at PT MAG is a general insurance company under the auspices of PT Asuransi Multi Artha Guna (Tbk). To sell insurance premiums, of course, companies need agents to increase sales. The company's goal is of course to find out which agent has the greatest impact in the form of the highest sales for the company. However, because the agents who work in PT MAG have a lot of sales data and performance data, it is difficult for the company to recommend the best agent. A Decision Support System for the Selection of the Best agents will be developed. This system was developed using the Rational Unified Process (RUP) methodology, usecase diagrams, Class Diagrams, Activity Diagrams, entity relationship diagrams, PHP programming language with Laravel Framework. The results and conclusions of creating this Decision Support System can help companies in making decisions in the recommendations of the best agent more easily and providing transparent information to employees brief.
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Sun, Yu, and Qijie He. "Computational Offloading for MEC Networks with Energy Harvesting: A Hierarchical Multi-Agent Reinforcement Learning Approach." Electronics 12, no. 6 (March 9, 2023): 1304. http://dx.doi.org/10.3390/electronics12061304.

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Multi-access edge computing (MEC) is a novel computing paradigm that leverages nearby MEC servers to augment the computational capabilities of users with limited computational resources. In this paper, we investigate the computational offloading problem in multi-user multi-server MEC systems with energy harvesting, aiming to minimize both system latency and energy consumption by optimizing task offload location selection and task offload ratio.We propose a hierarchical computational offloading strategy based on multi-agent reinforcement learning (MARL). The proposed strategy decomposes the computational offloading problem into two sub-problems: a high-level task offloading location selection problem and a low-level task offloading ratio problem. The complexity of the problem is reduced by decoupling. To address these sub-problems, we propose a computational offloading framework based on multi-agent proximal policy optimization (MAPPO), where each agent generates actions based on its observed private state to avoid the problem of action space explosion due to the increasing number of user devices. Simulation results show that the proposed HDMAPPO strategy outperforms other baseline algorithms in terms of average task latency, energy consumption, and discard rate.
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Kawamura, Masanori, and Kunikazu Kobayashi. "An Action Selection Method Using Degree of Cooperation in a Multi-agent Reinforcement Learning System." Journal of Robotics, Networking and Artificial Life 1, no. 3 (2014): 231. http://dx.doi.org/10.2991/jrnal.2014.1.3.13.

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Chen, Yuheng, Yingtao Niu, Changxing Chen, Quan Zhou, and Peng Xiang. "A Distributed Anti-Jamming Algorithm Based on Actor–Critic Countering Intelligent Malicious Jamming for WSN." Sensors 22, no. 21 (October 25, 2022): 8159. http://dx.doi.org/10.3390/s22218159.

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In this paper, in order to solve the problem of wireless sensor networks’ reliable transmission in intelligent malicious jamming, we propose a Distributed Anti-Jamming Algorithm (DAJA) based on an actor–critic algorithm for a multi-agent system. The Multi-Agent Markov Decision Process (MAMPD) is introduced to model the progress of wireless sensor networks’ anti-jamming communication, and the multi-agent system learns the intelligent jamming from the external environment by using an actor–critic algorithm. On the basis of coping with the influence of external and internal factors effectively, each sensor in networks selects the appropriate channels for transmission and finally realizes the optimal transmission of the system overall in a unit time period. In the environment of probabilistic intelligent jamming with tracking properties set in this paper, the simulation shows that the algorithm proposed can outperform the algorithm based on joint Q-learning and the conventional scheme based on orthogonal frequency hopping in terms of transmission. In addition, the proposed algorithm completes two updates of strategy evaluation and action selection in one iteration, which means that the system has higher efficiency of action selection and better adaptability to the environment through the interaction with the external environment, resulting in the better performance of transmission and convergence.
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Zhou, Yongkun, Dan Song, Bowen Ding, Bin Rao, Man Su, and Wei Wang. "Distributed Cooperative Jamming with Neighborhood Selection Strategy for Unmanned Aerial Vehicle Swarms." Electronics 11, no. 2 (January 7, 2022): 184. http://dx.doi.org/10.3390/electronics11020184.

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In system science, a swarm possesses certain characteristics which the isolated parts and the sum do not have. In order to explore emergence mechanism of a large–scale electromagnetic agents (EAs), a neighborhood selection (NS) strategy–based electromagnetic agent cellular automata (EA–CA) model is proposed in this paper. The model describes the process of agent state transition, in which a neighbor with the smallest state difference in each sector area is selected for state transition. Meanwhile, the evolution rules of the traditional CA are improved, and performance of different evolution strategies are compared. An application scenario in which the emergence of multi–jammers suppresses the radar radiation source is designed to demonstrate the effect of the EA–CA model. Experimental results show that the convergence speed of NS strategy is better than those of the traditional CA evolution rules, and the system achieves effective jamming with the target after emergence. It verifies the effectiveness and prospects of the proposed model in the application of electronic countermeasures (ECM).
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Paek, Min-Jae, Yu-Jin Na, Won-Seok Lee, Jae-Hyun Ro, and Hyoung-Kyu Song. "A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks." Applied Sciences 10, no. 15 (July 30, 2020): 5252. http://dx.doi.org/10.3390/app10155252.

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In wireless communication systems, reliability, low latency and power are essential in large scale multi-hop environment. Multi-hop based cooperative communication is an efficient way to achieve goals of wireless networks. This paper proposes a relay selection scheme for reliable transmission by selecting an optimal relay. The proposed scheme uses a signal-to-noise ratio (SNR) based Q-learning relay selection scheme to select an optimal relay in multi-hop transmission. Q-learning consists of an agent, environment, state, action and reward. When the learning is converged, the agent learns the optimal policy which is a rule of the actions that maximize the reward. In other words, the base station (BS) knows the optimal relay to select and transmit the signal. At this time, the cooperative communication scheme used in this paper is a decode-and-forward (DF) scheme in orthogonal frequency division multiplexing (OFDM) system. The Q-learning in the proposed scheme defines an environment to maximize a reward which is defined as SNR. After the learning process, the proposed scheme finds an optimal policy. Furthermore, this paper defines a reward which is based on the SNR. The simulation results show that the proposed scheme has the same bit error rate (BER) performance as the conventional relay selection scheme. However, this paper proposes an advantage of selecting fewer relays than conventional scheme when the target BER is satisfied. This can reduce the latency and the waste of resources. Therefore, the performance of the multi-hop transmission in wireless networks is enhanced.
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48

Koti, Ramesh B., and Mahabaleshwar S. Kakkasageri. "Multi Agent Assisted Safety Information Dissemination Scheme for V2V Communication in VANETs: Intelligent Agent Approach." International Journal of Intelligent Systems and Applications 13, no. 4 (August 8, 2021): 49–62. http://dx.doi.org/10.5815/ijisa.2021.04.05.

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The safety information dissemination plays a vital role in the VANET communication. It is a technique of transmitting the information at scheduled intervals or during road hazards by detecting the events using onboard system and interfaces. Information is shared between vehicles and road side units which are further used to predict vehicle collisions, road line crossings, environmental warnings, traffic data and road hazards. Interestingly the risk of lateral collisions and dense traffic for vehicles can be avoided by accomplishing fast data dissemination i.e. warning alerts by event detection. Vehicular technology which supports the safe mode of transportation is growing faster due to the deployment of new automated technology in the intelligent transportation system (ITS). The different scenarios used in vehicular communication are Vehicle to Vehicle (V-V), Vehicle to Infrastructure (V-I) and Vehicle to Internet. Some of the important characteristics of vehicular communications are the mobility, frequent changes in topology, varying transmission power of antennas, intermittent connectivity. ITS providing the solutions for most critical transportation issues and inspiring the researchers for the betterment of road safety. In this paper, we propose a multi agent based safety information dissemination scheme for vehicle to vehicle communication. The proposed algorithm performs the safety information dissemination with help of intelligent agents by optimizing the channel access techniques, message encoding and selection of intermediate nodes. Here the communication between source and destination is achieved with fever number of intermediate links by selecting the nodes in the special zone. Short interval codes which represent safety information are effectively transmitted in the intermittent nature of wireless connectivity. This proposed work describes the details of algorithm with associated network environment, multi agent functions and dissemination mechanism to illustrate the improvement in end to end delay, PDR, energy constraints etc. This method reduces the problem of broadcast storm by delivering the information to intended node. Simulation of the proposed work gives the improved results on PDR, latency and connection overhead.
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Ji, Yunfeng, Gang Wang, Qingdu Li, and Chaoli Wang. "Event-Triggered Optimal Consensus of Heterogeneous Nonlinear Multi-Agent Systems." Mathematics 10, no. 23 (December 6, 2022): 4622. http://dx.doi.org/10.3390/math10234622.

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This paper deals with optimal consensus problems of a general heterogeneous nonlinear multi-agent system. A novel filter is proposed for each agent by integrating local gradients with neighboring output information. Using this filter and introducing an appropriate auxiliary variable, the event-triggered control algorithm is obtained within the framework of the prescribed performance control. One of the remarkable properties of the proposed algorithm is that it can save resources by updating control signals only when necessary rather than periodically while achieving optimal consensus. Theoretical and simulation verifications of the algorithm without the Zeno behavior are carefully studied. Instructions are also presented for control parameter selection to keep the residual errors as small as desired.
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Euchner, Eva-Maria, and Elena Frech. "Candidate Selection and Parliamentary Activity in the EU’s Multi-Level System: Opening a Black-Box." Politics and Governance 8, no. 1 (February 13, 2020): 72–84. http://dx.doi.org/10.17645/pag.v8i1.2553.

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Members of the European Parliament (MEPs) have a multitude of parliamentary duties and, accordingly, have to prioritize some parliamentary activities over others. So far, we know comparably little about this prioritization process. Based on principal–agent theory, we argue first, that MEPs’ parliamentary activities are systematically determined by the “visibility” and usefulness of parliamentary instruments for their key principal; second, we expect the exclusiveness of candidate selection procedures of an MEP’s national party—the nomination and the final list placement—to determine her/his key principal (i.e., elites or members of national parties). Combining multi-level mixed effects linear regression models and expert interviews, we show that MEPs who are nominated and whose final list placement is decided by an exclusive circle of national party elites prioritize speeches, whereas MEPs who are nominated or whose final list placement is decided by more inclusive procedures prioritize written questions and opinions or reports. In other words, speeches seem particularly useful to communicate with national party elites, while other activities are used to serve larger groups of party members. These findings open up the black-box of the “national party principal” and illustrate how a complex principal–agent relationship stimulates very specific parliamentary activity patterns in the EU’s multi-level system.
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