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Journal articles on the topic 'Agent based framework'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2023

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1

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 (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 thes

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2

Lukasz, Faber. "Agent-based Data Integration Framework." Computer Science 15, no. 4 (2014): 389. http://dx.doi.org/10.7494/csci.2014.15.4.389.

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Helmy, Tarek, and Ahmed Al-Nazer. "AN AGENT-BASED IMAGE DESCRIPTION FRAMEWORK." ERJ. Engineering Research Journal 34, no. 2 (2011): 135–48. http://dx.doi.org/10.21608/erjm.2011.67267.

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Helmy, Tarek, and Ahmed Al-Nazer. "AN AGENT-BASED IMAGE DESCRIPTION FRAMEWORK." ERJ. Engineering Research Journal 35, no. 2 (2012): 135–48. http://dx.doi.org/10.21608/erjm.2012.67265.

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Sindhuja, K., A. V. Monisha, and S. Padmavathi. "Performance Analysis of Agent Based Framework." Procedia Computer Science 47 (2015): 37–44. http://dx.doi.org/10.1016/j.procs.2015.03.181.

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M. Abdelmohsen, Sherif. "A BIM-based framework for outdoor circulation rule checking." Revista de Arquitectura 18, no. 26 (2014): 20. http://dx.doi.org/10.5354/0719-5427.2012.32537.

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This paper introduces a BIM-based framework for outdoor circulation rule checking from a geometric modeling perspective. The paper extracts rules and patterns of circulation and interaction based on site planning standards and codes. It then identifies high-level operators and 3D low-level operations involved in applicable circulation rules, and suggest methods for implementation. Mechanisms of integration between high-level operators and low-level operations are defined for different domains of rule checking, based on three types of datasets: 1) BIM objects in outdoor settings, includin

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Yang, Chizhao, Jared Strader, and Yu Gu. "A Scalable Framework for Map Matching Based Cooperative Localization." Sensors 21, no. 19 (2021): 6400. http://dx.doi.org/10.3390/s21196400.

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Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estim

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Jäger, Georg. "Replacing Rules by Neural Networks A Framework for Agent-Based Modelling." Big Data and Cognitive Computing 3, no. 4 (2019): 51. http://dx.doi.org/10.3390/bdcc3040051.

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Agent-based modelling is a successful technique in many different fields of science. As a bottom-up method, it is able to simulate complex behaviour based on simple rules and show results at both micro and macro scales. However, developing agent-based models is not always straightforward. The most difficult step is defining the rules for the agent behaviour, since one often has to rely on many simplifications and assumptions in order to describe the complicated decision making processes. In this paper, we investigate the idea of building a framework for agent-based modelling that relies on an

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Abed, Mourad, Benaissa Ezzeddine, Abdellatif Benabdelhafid, and Janusz Grabara. "An Agent-Based Framework for Supply Chain Cooperation." Applied Mechanics and Materials 309 (February 2013): 185–94. http://dx.doi.org/10.4028/www.scientific.net/amm.309.185.

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The importance of cooperation in the supply chain has led scholars to suggest diverse approaches for problems in the collaboration process. Cooperation refers to the integration of different parts of an organization or different organizations in supply chain to accomplish a collective set of tasks and to achieve mutual benefits. The use of modern information technology (IT) decision support systems is fast becoming an indispensable tool for designing and managing complex supply chain systems today. This study describes a model of intelligent framework for cooperation in the supply chain enviro

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Chen, Chun, Li Ning, Rong Zhou, Yong Zhang, Chan Zhou, and Shengzhong Feng. "A GNN-Based Variable Partition Framework for DCOPs." Wireless Communications and Mobile Computing 2022 (May 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/8003887.

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Many problems of the Internet of Things (IoT), such as radio frequency allocation and sensor network, can be regarded as constraint optimal problems (COPs), which can be formulated as graphical representations. The scale of graph is large, which is hard to implement, and the information shared by all the variables is unsafe for all the variables running in an agent. On the other hand, supercomputers are playing a significant and growing role in various fields of large-scale processing tasks. When countering this scene, the supercomputers can accelerate to complete the task according to the dis

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Itazuro, Syo, Takahiro Uchiya, Tetsuo Kinoshita, and Ichi Takumi. "Development Support of Learning Agent on Repository-based Agent Framework." International Journal of Software Science and Computational Intelligence 4, no. 3 (2012): 62–79. http://dx.doi.org/10.4018/jssci.2012070104.

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Agent-oriented computing is a technique for generating an agent that operates autonomously according to behavioral knowledge. Moreover, an agent can have a characteristic called “learning” skill. More efficient operation of an agent can be expected by support an agent designer who designs and develops an agent equipped with “learning” skill. The authors propose a design support mechanism of a learning agent on a repository-based agent framework called the DASH framework. The proposed mechanism enables an agent designer to design and implement the learning agent without great expertise, thereby

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12

Li, Howard, Fakhreddine Karray, Otman Basir, and Insop Song. "Multi-Agent Based Control of a Heterogeneous System." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 2 (2006): 161–67. http://dx.doi.org/10.20965/jaciii.2006.p0161.

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In this study, the Coordinated Hybrid Agent (CHA) framework for the control of Multi-Agent Systems (MASs) is applied for a heterogeneous multi-agent system. The system consists of a mobile robot, an overhead crane, and a robot manipulator. The final goal for this project is to implement coordination tasks for the system. In this framework, the control of the MAS is regarded as a decentralized control and coordination of agents. A coordination rule base is developed for the intelligent coordination control layer. Experiments show that the framework is able to model the heterogeneous multi-agent

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Singh, Karandeep, Chang-Won Ahn, Euihyun Paik, Jang Won Bae, and Chun-Hee Lee. "A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling." Artificial Life 24, no. 2 (2018): 128–48. http://dx.doi.org/10.1162/artl_a_00260.

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Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or “soft,” aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model

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Bentahar, Jamal, Francesca Toni, John‐Jules Ch Meyer, and Jihad Labban. "A security framework for agent‐based systems." International Journal of Web Information Systems 3, no. 4 (2007): 341–62. http://dx.doi.org/10.1108/17440080710848125.

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15

Mellouli, Sehl, and Faouzi Bouslama. "Multi-agent based framework for e-government." Electronic Government, an International Journal 6, no. 2 (2009): 177. http://dx.doi.org/10.1504/eg.2009.024441.

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16

Julka, Nirupam, Rajagopalan Srinivasan, and I. Karimi. "Agent-based supply chain management—1: framework." Computers & Chemical Engineering 26, no. 12 (2002): 1755–69. http://dx.doi.org/10.1016/s0098-1354(02)00150-3.

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17

Hutchinson, Matthew J., and Bert Veenendaal. "An agent-based framework for intelligent geocoding." Applied Geomatics 5, no. 1 (2011): 33–44. http://dx.doi.org/10.1007/s12518-011-0063-z.

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18

Khedro, Taha, Michael R. Genesereth, and Paul M. Teicholz. "Agent-based framework for integrated facility engineering." Engineering with Computers 9, no. 2 (1993): 94–107. http://dx.doi.org/10.1007/bf01199048.

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19

Vahdati, Ali. "Agents.jl: agent-based modeling framework in Julia." Journal of Open Source Software 4, no. 42 (2019): 1611. http://dx.doi.org/10.21105/joss.01611.

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20

K., Venkateshwaran, Anu Malviya, Utkarsha Dikshit, and S. Venkatesan. "Security Framework for Agent-Based Cloud Computing." International Journal of Interactive Multimedia and Artificial Intelligence 3, no. 3 (2015): 35. http://dx.doi.org/10.9781/ijimai.2015.334.

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21

Martinez Lastra, Jose L., and Armando W. Colombo. "Engineering framework for agent-based manufacturing control." Engineering Applications of Artificial Intelligence 19, no. 6 (2006): 625–40. http://dx.doi.org/10.1016/j.engappai.2005.12.015.

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22

Czarnowski, Ireneusz, and Piotr Jędrzejowicz. "An agent-based framework for distributed learning." Engineering Applications of Artificial Intelligence 24, no. 1 (2011): 93–102. http://dx.doi.org/10.1016/j.engappai.2010.07.003.

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23

Chaimontree, Santhana, Katie Atkinson, and Frans Coenen. "A framework for Multi-Agent Based Clustering." Autonomous Agents and Multi-Agent Systems 25, no. 3 (2011): 425–46. http://dx.doi.org/10.1007/s10458-011-9187-0.

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24

López, Fabiola López y., Michael Luck, and Mark d’Inverno. "A normative framework for agent-based systems." Computational & Mathematical Organization Theory 12, no. 2-3 (2006): 227–50. http://dx.doi.org/10.1007/s10588-006-9545-7.

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25

M. Ezzat, Heba. "Behavioral agent-based framework for interacting financial markets." Review of Economics and Political Science 5, no. 2 (2020): 94–115. http://dx.doi.org/10.1108/reps-03-2019-0037.

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Purpose This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored. Design/methodology/approach The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step

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26

Kamel, Ahmed. "An agent-based framework for agricultural knowledge based systems." IFAC Proceedings Volumes 37, no. 2 (2004): 6–10. http://dx.doi.org/10.1016/s1474-6670(17)38681-0.

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27

Cheaib, Nader, Samir Otmane, and Malik Mallem. "A Collaborative Multi-Agent Framework for Internet-Based Teleoperation Systems." International Journal of Agent Technologies and Systems 5, no. 2 (2013): 31–54. http://dx.doi.org/10.4018/jats.2013040102.

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This paper presents a conceptual model of an agent (called Collaborator Agent) intended to design collaborative software architectures based on multi-agent systems. The authors’ model combines astutely two research areas: Multi-Agent Systems (MAS) and Computer Supported Cooperative Work (CSCW). The particularity of their approach is the division of the collaborative process into three spaces according to Ellis' 3C model: communication, coordination and production. In their work, the authors extend the 3C model by adding a fourth space: collaboration. Hence, the authors present a model based on

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28

Aydoğan, Reyhan, Özgür Kafali, Furkan Arslan, Catholijn M. Jonker, and Munindar P. Singh. "Nova : Value-based Negotiation of Norms." ACM Transactions on Intelligent Systems and Technology 12, no. 4 (2021): 1–29. http://dx.doi.org/10.1145/3465054.

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Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent r

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29

Sano, Yoshihito, and Naoki Fukuta. "A GPU-Based Programming Framework for Highly-Scalable Multi-Agent Traffic Simulations." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (2014): 581–89. http://dx.doi.org/10.20965/jaciii.2014.p0581.

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Highly detailed reproducibility of multi-agent simulations is strongly demanded. To realize such highly reproducible multi-agent simulations, it is important to make each agent respond to its dynamically changing environment as well as scale the simulation to cover important phenomena that could be produced. In this paper, we present a programming framework to realize highly scalable execution of them as well as detailed behaviors of agents. The framework can help simulation developers utilize many GPGPU-based parallel cores in their simulation programs by using the proposed OpenCL-based multi

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30

Lopez-Jimenez, Jorge, Nicanor Quijano, and Alain Vande Wouwer. "An Agent-Based Crop Model Framework for Heterogeneous Soils." Agronomy 11, no. 1 (2021): 85. http://dx.doi.org/10.3390/agronomy11010085.

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Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilitie

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Abbasi, Khurrum Mustafa, Tamim Ahmed Khan, and Irfan ul Haq. "Framework for Integrated Use of Agent-Based and Ambient-Oriented Modeling." Mathematics 10, no. 21 (2022): 4157. http://dx.doi.org/10.3390/math10214157.

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Agent-based modeling (ABM) is a flexible and simulation-friendly modeling approach. Ambient-oriented modeling is effective for systems containing ambient and spatial representations. In this paper we propose a framework for the integrated use of agent-based modeling and ambient-oriented modeling. We analyze both agents and ambient in detail. We also compare both modeling approaches as well and analyze their similarities and differences. The integrated implementation provides a new link between mathematical modeling and simulations. The model developed using this framework has four parts. The f

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Jang, Myeong-Wuk, and Gul Agha. "Agent framework services to reduce agent communication overhead in large-scale agent-based simulations." Simulation Modelling Practice and Theory 14, no. 6 (2006): 679–94. http://dx.doi.org/10.1016/j.simpat.2005.10.002.

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33

Smeureanu, Ion, Gheorghe Ruxanda, Andreea Diosteanu, Camelia Delcea, and Liviu Adrian Cotfas. "INTELLIGENT AGENTS AND RISK BASED MODEL FOR SUPPLY CHAIN MANAGEMENT." Technological and Economic Development of Economy 18, no. 3 (2012): 452–69. http://dx.doi.org/10.3846/20294913.2012.702696.

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This paper presents a software agent based framework's architecture for boosting performance in supply chain management applications. The framework is based on agent interaction and semantic web service composition. The purpose of such a platform is to develop flexible business applications for SCM transactions modeling, in collaborative and distributed economic systems. The interaction between agents is limited by a cybernetic model that takes into account several constraints one of the main being bankruptcy risk potential of the peer partner company.

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CODDINGTON, ALEXANDRA M., and MICHAEL LUCK. "A MOTIVATION-BASED PLANNING AND EXECUTION FRAMEWORK." International Journal on Artificial Intelligence Tools 13, no. 01 (2004): 5–25. http://dx.doi.org/10.1142/s0218213004001399.

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AI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domain-independent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agen

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Dong, Gao, Xu Xin, Zhang Beike, Ma Xin, and Wu Chongguang. "A Framework for Agent-based Chemical Process Modeling." Journal of Applied Sciences 13, no. 17 (2013): 3490–96. http://dx.doi.org/10.3923/jas.2013.3490.3496.

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36

Junyi Li, Lulu Sun, Shenglan Liu, and Ruizhong Wei. "Web Applications Testing Framework based on Multi-Agent." International Journal of Advancements in Computing Technology 3, no. 10 (2011): 403–12. http://dx.doi.org/10.4156/ijact.vol3.issue10.50.

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37

Abedinzadeh, Sadra, and Samira Sadaoui. "A Rough Sets-based Agent Trust Management Framework." International Journal of Intelligent Systems and Applications 5, no. 4 (2013): 1–19. http://dx.doi.org/10.5815/ijisa.2013.04.01.

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38

Zhao, Meng Yi, You Mou Li, and Meng Guo. "Network Agent Based on VISA Framework for Instruments." Applied Mechanics and Materials 519-520 (February 2014): 1197–202. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1197.

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With the rapid development of computer networks,more and more measurements need to relay on the network.But there are only a small amount of instruments with network interface.To access the network,we design a network agent for the instruments with different interfaces. We connect the host with the instrument through the network agent.The agent communicates with the host by using HiSLIP(High-Speed LAN Instrument Protocol ).The client soft front panel controls the instrument based on the VISA(Virtual Instrument Software Architecture) and HiSLIP.We use the Labwindow/CVI to design the soft panel.

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Lin, Feng-Yu, Yeali S. Sun, and Meng Chang Chen. "Distributed Agent-based IP Location System Framework Design." International Journal of Online Engineering (iJOE) 9, S7 (2013): 4. http://dx.doi.org/10.3991/ijoe.v9is7.3185.

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Jiang, Li Zhu, Chang Liu, Ze Xue Zhu, and Hai Tao Jia. "Network Management Framework Research Based on Mobile Agent." Advanced Materials Research 791-793 (September 2013): 1626–29. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1626.

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With the expansion of Internet scale unceasingly,how to improve the scalability and high efficiency of network management system has become an important subject of internet network management. This paper first discusses the advantages and disadvantages of SNMP network management. Then it creates a dynamic and scalable network management framework by introducing mobile agent technology into the SNMP network management. The framework is simple and easy, and can ensure interoperability with SNMP.

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Cheliotis, Kostas. "ABMU: An Agent-Based Modelling Framework for Unity3D." SoftwareX 15 (July 2021): 100771. http://dx.doi.org/10.1016/j.softx.2021.100771.

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Debenham, John. "An Agent-Based Framework for Emergent Process Management." International Journal of Intelligent Information Technologies 2, no. 2 (2006): 30–48. http://dx.doi.org/10.4018/jiit.2006040103.

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Zhang, Zili, Chengqi Zhang, and Shichao Zhang. "An agent-based hybrid framework for database mining." Applied Artificial Intelligence 17, no. 5-6 (2003): 383–98. http://dx.doi.org/10.1080/713827179.

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Heragu, S. S., R. J. Graves, Byung-In Kim, and A. St Onge. "Intelligent agent based framework for manufacturing systems control." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 32, no. 5 (2002): 560–73. http://dx.doi.org/10.1109/tsmca.2002.804788.

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Adacal, M., and A. B. Bener. "Mobile Web services: a new agent-based framework." IEEE Internet Computing 10, no. 3 (2006): 58–65. http://dx.doi.org/10.1109/mic.2006.59.

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Sahli, Nabil, Nafaa Jabeur, and Mohamad Badra. "Agent-based Framework for Sensor-to-Sensor Personalization." Procedia Computer Science 19 (2013): 197–205. http://dx.doi.org/10.1016/j.procs.2013.06.030.

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Yujun, Zhu, Xu Zhongwei, and Mei Meng. "Agent-based Simulation Framework for Safety Critical System." Procedia Engineering 29 (2012): 1060–65. http://dx.doi.org/10.1016/j.proeng.2012.01.089.

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Li, Yinsheng, Weiming Shen, and Hamada Ghenniwa. "Agent-Based Web Services Framework and Development Environment." Computational Intelligence 20, no. 4 (2004): 678–92. http://dx.doi.org/10.1111/j.0824-7935.2004.00260.x.

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Casella, Giovanni, Vincenzo Deufemia, Viviana Mascardi, Gennaro Costagliola, and Maurizio Martelli. "An agent-based framework for sketched symbol interpretation." Journal of Visual Languages & Computing 19, no. 2 (2008): 225–57. http://dx.doi.org/10.1016/j.jvlc.2007.04.002.

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Cloutier, Louis, Bernard Espinasse, and Pierre Lefrancois. "Networked enterprise integration: An agent-based coordination framework." IFAC Proceedings Volumes 32, no. 2 (1999): 133–38. http://dx.doi.org/10.1016/s1474-6670(17)56025-5.

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