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Journal articles on the topic 'Agent mining'

Author: Grafiati

Published: 2 February 2023

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1

Cao, Longbing, Vladimir Gorodetsky, and Pericles A. Mitkas. "Agent Mining: The Synergy of Agents and Data Mining." IEEE Intelligent Systems 24, no. 3 (May 2009): 64–72. http://dx.doi.org/10.1109/mis.2009.45.

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2

M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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3

Sabitha, R., and Karthik. "EMPLOYING AGENTS IN DESCRIPTIVE MINING." International Journal of Research -GRANTHAALAYAH 4, no. 2 (February 29, 2016): 111–20. http://dx.doi.org/10.29121/granthaalayah.v4.i2.2016.2821.

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Agent technology and Data Mining have emerged as two of the prominent areas in information sciences. An effort has been activated towards the interaction and integration between agent technology and data mining which is referred to as “AGENT MINING”. Data Mining is the process of extracting interesting information or patterns from large volumes of data. Agents comprise a powerful technology for the analysis, design and implementation of autonomous intelligent systems that can handle distributed problem-solving, cooperation, coordination, communication, and organization in a multiplayer environment. This agent uses information technology to find trends and patterns in an abundance of information from many different sources. The user can sort through this information in order to find whatever information they are seeking. Intelligent agents are today accepted as powerful tools for data mining in a distributed environment. The interaction and integration between agent and mining has potential to not only strengthen either side, but generate new techniques for developing more powerful intelligence and intelligent information processing systems. This paper discusses how agents are used in the various descriptive models of Data Mining. The various challenges and methodologies are analyzed and it clearly indicates the need for and the promising potential of agent mining for the mutual enhancement of both fields and for the creation of super-intelligent systems. Even though many researchers have been committed, more efforts are required to develop techniques and systems in practical perspectives.

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Yan, Jiaqi, Daning Hu, Stephen S. Liao, and Huaiqing Wang. "Mining Agents’ Goals in Agent-Oriented Business Processes." ACM Transactions on Management Information Systems 5, no. 4 (March 21, 2015): 1–22. http://dx.doi.org/10.1145/2629448.

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P, Rohini, and Sree Lakshmi.P. "Agent-Driven Distributed Data Mining." International Journal of Science and Engineering Applications 2, no. 5 (May 1, 2013): 103–9. http://dx.doi.org/10.7753/ijsea0205.1003.

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6

Boylu, Fidan, Haldun Aytug, and Gary J. Koehler. "Data mining with agent gaming." Information Technology and Management 11, no. 1 (January 20, 2010): 1–6. http://dx.doi.org/10.1007/s10799-010-0064-3.

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7

Cao, Longbing, Gerhard Weiss, and Philip S. Yu. "A brief introduction to agent mining." Autonomous Agents and Multi-Agent Systems 25, no. 3 (May 9, 2012): 419–24. http://dx.doi.org/10.1007/s10458-011-9191-4.

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8

Devasekhar, V., and P. Natarajan. "Multi-agent based data mining aggregation approaches using machine learning techniques." International Journal of Engineering & Technology 7, no. 3 (June 23, 2018): 1136. http://dx.doi.org/10.14419/ijet.v7i3.9631.

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Data Mining is an extraction of important knowledge from the various databases using different kinds of approaches. In the multi agent, distributed mining the knowledge aggregation is one of challenging task. This paper tries to optimize the problem of aggregation and boils down into the solution, which is derived based on the machine learning statistical features of each agents. However, in this paper a novel optimization algorithm called Multi-Agent Based Data Mining Aggregation (MABDA) is used for present day’s scenarios. The MBADA algorithm has agents which collect extracted knowledge and summarizes the various levels of agent’s cluster data into an aggregation with maximum accuracies. To prove the effectiveness of the proposed algorithm, the experimental results are compared with relatively existing methods.

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Symeonidis, Andreas L., Kyriakos C. Chatzidimitriou, Ioannis N. Athanasiadis, and Pericles A. Mitkas. "Data mining for agent reasoning: A synergy for training intelligent agents." Engineering Applications of Artificial Intelligence 20, no. 8 (December 2007): 1097–111. http://dx.doi.org/10.1016/j.engappai.2007.02.009.

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10

Sherief, Abdallah. "Mining Dynamics: Using Data Mining Techniques to Analyze Multi-agent Learning." Journal of Intelligent Systems 26, no. 4 (September 26, 2017): 613–24. http://dx.doi.org/10.1515/jisys-2016-0136.

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AbstractAnalyzing the learning dynamics in multi-agent systems (MASs) has received growing attention in recent years. Theoretical analysis of the dynamics was only possible in simple domains and simple algorithms. When one or more of these restrictions do not apply, theoretical analysis becomes prohibitively difficult, and researchers rely on experimental analysis instead. In experimental analysis, researchers have used some global performance metric(s) as a rough approximation to the internal dynamics of the adaptive MAS. For example, if the overall payoff improved over time and eventually appeared to stabilize, then the learning dynamics were assumed to be stable as well. In this paper, we promote a middle ground between the thorough theoretical analysis and the high-level experimental analysis. We introduce the concept of mining dynamics and propose data-mining-based methodologies to analyze multi-agent learning dynamics. Using our methodologies, researchers can identify clusters of learning parameter values that lead to similar performance, and discover frequent sequences in agent dynamics. We verify the potential of our approach using the well-known iterated prisoner’s dilemma (with multiple states) domain.

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11

Tour, Andrei, Artem Polyvyanyy, and Anna Kalenkova. "Agent System Mining: Vision, Benefits, and Challenges." IEEE Access 9 (2021): 99480–94. http://dx.doi.org/10.1109/access.2021.3095464.

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12

Lombardo, Gianfranco, Paolo Fornacciari, Monica Mordonini, Michele Tomaiuolo, and Agostino Poggi. "A Multi-Agent Architecture for Data Analysis." Future Internet 11, no. 2 (February 18, 2019): 49. http://dx.doi.org/10.3390/fi11020049.

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ActoDatA (Actor Data Analysis) is an actor-based software library for the development of distributed data mining applications. It provides a multi-agent architecture with a set of predefined and configurable agents performing the typical tasks of data mining applications. In particular, its architecture can manage different users’ applications; it maintains a high level of execution quality by distributing the agents of the applications on a dynamic set of computational nodes. Moreover, it provides reports about the analysis results and the collected data, which can be accessed through either a web browser or a dedicated mobile APP. After an introduction about the actor model and the software framework used for implementing the software library, this article underlines the main features of ActoDatA and presents its experimentation in some well-known data analysis domains.

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13

Wang, Lei, Zhikang Song, Xin Huang, Wenjun Xu, and Zhengbang Chen. "Study on the Influence of Pressure Reduction and Chemical Injection on Hydrate Decomposition." Processes 10, no. 12 (November 29, 2022): 2543. http://dx.doi.org/10.3390/pr10122543.

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This study simulated seabed high pressure and low temperature conditions to synthesize natural gas hydrates, multi-stage depressurization mode mining hydrates as the blank group, and then carried out experimental research on the decomposition and mining efficiency of hydrates by depressurization and injection of different alcohols, inorganic salts, and different chemical agent concentrations. According to the experimental results, the chemical agent with the best decomposition efficiency is preferred; the results show that: the depressurization and injection of a certain mass concentration of chemical agents to exploit natural gas hydrate is more effective than pure depressurization to increase the instantaneous gas production rate. This is because depressurization combined with chemical injection can destroy the hydrate phase balance while effectively reducing the energy required for hydrate decomposition, thereby greatly improving the hydrate decomposition efficiency. Among them, depressurization and injection of 30% ethylene glycol has the best performance in alcohols; the decomposition efficiency is increased by 52.0%, and the mining efficiency is increased by 68.2% within 2 h. Depressurization and injection of 15% calcium chloride has the best performance in inorganic salts; the decomposition efficiency is increased by 46.3%, and the mining efficiency is increased by 61.1% within 2 h. In the actual mining process, the appropriate concentration of chemical agents should be used to avoid polluting the environment.

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G.S, Bhamra, Verma A.K, and Patel R.B. "Agent Enabled Mining of Distributed Protein Data Banks." International Journal in Foundations of Computer Science & Technology 5, no. 3 (May 30, 2015): 25–45. http://dx.doi.org/10.5121/ijfcst.2015.5303.

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15

Benítez, I. J., J. L. Díez, and P. Albertos. "Applying Dynamic Data Mining on Multi-Agent Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 1857–62. http://dx.doi.org/10.3182/20080706-5-kr-1001.00317.

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16

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

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17

de Freitas, N. C. A., P. P. Reboucas Filho, C. D. G. de Moura, and M. P. S. Silva. "AgentGeo: Multi-Agent System of Satellite Images Mining." IEEE Latin America Transactions 14, no. 3 (March 2016): 1343–51. http://dx.doi.org/10.1109/tla.2016.7459619.

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18

Ito, Sohei, Dominik Vymětal, Roman Šperka, and Michal Halaška. "Process mining of a multi-agent business simulator." Computational and Mathematical Organization Theory 24, no. 4 (April 4, 2018): 500–531. http://dx.doi.org/10.1007/s10588-018-9268-6.

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19

Mesbahi, Nadjib, Okba Kazar, Saber Benharzallah, Merouane Zoubeidi, and Samir Bourekkache. "Multi-Agents Approach for Data Mining Based k-Means for Improving the Decision Process in the ERP Systems." International Journal of Decision Support System Technology 7, no. 2 (April 2015): 1–14. http://dx.doi.org/10.4018/ijdsst.2015040101.

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Today the enterprise resource planning (ERP) became a tool that enables uniform and consistent management of information system (IS) of the company with a large single database. In addition, Data Mining is a technology whose purpose is to promote information and knowledge extraction from a large database. In this paper, an agent-based multi-layered approach for data mining based k-Means through the ERP to extract hidden knowledge in the ERP database is proposed. To achieve this, the authors call the paradigm of multi-agent system to distribute the complexity of several autonomous entities called agents, whose goal is to group records or observations on similar objects classes using the k-means technique that is dedicated the task of clustering. This will help business decision-makers to take good decisions and provide a very good response time by the use of multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and agents comply with the specifications FIPA.

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20

Nie, Xing Xin, Cunrui Bai, and Jingjing Zhang. "Simulation Research on the Effectiveness of a Multiagent Mine Safety Supervision System and Its Verification." Mathematical Problems in Engineering 2019 (December 31, 2019): 1–18. http://dx.doi.org/10.1155/2019/8457124.

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A Computable Mine Safety Supervision (CMSS) model is constructed based on agent-based modeling and simulation (ABMS) technology and the conservation of resources (COR). This model aims to solve the mining safety problems involved with illegal mining operations and burnout among mining supervisors, in China. The model includes several types of agents: supervision agents, decision support agents, functional coordination agents, and miner agents, and it uses the Netlogo simulation platform to simulate the influence of reward and punishment on agent behavior. The simulation determines the decision support degree to gauge the influence of functional coordination and miner behavior on the burnout rate of supervision agents. We analyze the macroscopic emergence law of the simulation results. The results show the following: (1) Job Situation Adaptability (JSA) ∈ [−6.02, 2.64] ∪ [16.9, 21.93], which uses a reward strategy to guide miners to choose safe behavior and (2) JSA ∈ [2.64, 16.9], which uses a punishment strategy to restrict unsafe behavior. The decision support coefficient Sc has the greatest influence on the supervision agent’s job burnout. The functional coordination coefficient Fc has the second highest influence on job burnout and the processing effectiveness coefficient Ec has the least influence. According to the simulation results, suggestions for improving the mine safety supervision system are put forward and an improved safety management decision-making basis for reducing mine accidents is provided.

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21

Mazumdar, Bireshwar Dass, and R. B. Mishra. "Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods." International Journal of Intelligent Information Technologies 6, no. 4 (October 2010): 46–70. http://dx.doi.org/10.4018/jiit.2010100104.

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The Multi agent system (MAS) model has been extensively used in the different tasks of e-commerce like customer relation management (CRM), negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.

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22

Fang, Zhao Lin, and Chen Liang Liu. "Recommendation System Model Based on Web Usage Mining and Market Mechanism." Applied Mechanics and Materials 608-609 (October 2014): 412–19. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.412.

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Firstly, the paper proposes the framework of recommendation system based on web usage mining and market mechanism. Then, the paper introduces the system structure. And the paper introduces each Agent of the system including the functions of management Agent, interface Agent, online-recommendation Agent and filter Agent. And the paper gives the operation mechanism, resource allocation mechanism, customization return mechanism and customization bidding strategy,

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23

Wan, Li Yong, Jian Xin Chen, and Dong Juan Gu. "An Information Mining Model of Intelligent Collaboration Based on Agent Technology." Advanced Materials Research 998-999 (July 2014): 1096–99. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1096.

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Information can be collected search engine on the Web, but the current search engines can only interpret the characteristic structure of web page data from the perspective of syntax, is lack of semantic understanding, and thus cannot find the desired information quickly and accurately. In order to solve these problems, this paper proposes an information mining model of intelligent Collaboration based on agent technology. By analyzing the information mining process, more to understand the mechanism of Collaboration mining, thus can help users find faster and better desired information.

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24

Morejón, Reinier, Marx Viana, and Carlos Lucena. "An Approach to Generate Software Agents for Health Data Mining." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1579–89. http://dx.doi.org/10.1142/s0218194017400125.

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Data mining is a hot topic that attracts researchers of different areas, such as database, machine learning, and agent-oriented software engineering. As a consequence of the growth of data volume, there is an increasing need to obtain knowledge from these large datasets that are very difficult to handle and process with traditional methods. Software agents can play a significant role performing data mining processes in ways that are more efficient. For instance, they can work to perform selection, extraction, preprocessing, and integration of data as well as parallel, distributed, or multisource mining. This paper proposes a framework based on multiagent systems to apply data mining techniques to health datasets. Last but not least, the usage scenarios that we use are datasets for hypothyroidism and diabetes and we run two different mining processes in parallel in each database.

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25

Liu, Yang Bin, Liang Shi, Bei Zhan Wang, Yuan Qin Wu, and Pan Hong Wang. "An New Agent Based Distributed Adaptive Intrusion Detection System." Advanced Materials Research 532-533 (June 2012): 624–29. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.624.

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In order to overcome the excessive dependence among the traditional intrusion detection system components, high rate false-alarm phenomenon caused by multiple alarms to the same invasion, inability to adaptively replace mining algorithm when testing environment has changed and other issues, this paper puts forward an Agent based distributed adaptive intrusion detection system, which employs Joint Detection mechanism for mining algorithm module, and Dynamic Election algorithm for the recovery mechanism, thereby improving the system adaptive ability to the external change.

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26

Cohen, Miri Weiss, and Vitor Nazário Coelho. "Open-Pit Mining Operational Planning using Multi Agent Systems." Procedia Computer Science 192 (2021): 1677–86. http://dx.doi.org/10.1016/j.procs.2021.08.172.

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27

Clavel, Chloe, and Zoraida Callejas. "Sentiment Analysis: From Opinion Mining to Human-Agent Interaction." IEEE Transactions on Affective Computing 7, no. 1 (January 1, 2016): 74–93. http://dx.doi.org/10.1109/taffc.2015.2444846.

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28

Wu, Shaofei, Mingqing Wang, and Yuntao Zou. "Research on internet information mining based on agent algorithm." Future Generation Computer Systems 86 (September 2018): 598–602. http://dx.doi.org/10.1016/j.future.2018.04.040.

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29

Yu, Yan, and Jian Hua Wang. "Research on Network Examination System Model Based on Multi-AGENT." Advanced Materials Research 566 (September 2012): 685–90. http://dx.doi.org/10.4028/www.scientific.net/amr.566.685.

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With the development of artificial intelligence, Multi-agent technology is applied more and more in reality. In line with multi-user intelligent network examination test system, this paper proposes multi-agent-based general examination system model constituted by multiple mutually independent but collaborated AGENTs. In addition to fulfill their respective responsibilities, every single sub-Agent communicates among each other to gain information and complete missions in a collaborative form. Also, dedicated data mining AGENT is deployed in the system to conduct intelligent analysis and processing on various data, thereby providing decision support for teachers’ teaching and student’s study duties. And this paper presents a new clue for establishing intelligent network examination system under distributed environment.

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30

Saoud, Manel Saad, Abdelhak Boubetra, and Safa Attia. "A Multi-Agent Based Modeling and Simulation Data Management and Analysis System for the Hospital Emergency Department." International Journal of Healthcare Information Systems and Informatics 12, no. 3 (July 2017): 21–36. http://dx.doi.org/10.4018/ijhisi.2017070102.

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In the last decades, multi-agent based modeling and simulation systems have become more increasingly used to model the dynamic and the complex healthcare systems which contain many variabilities and uncertainties such as the hospital emergency departments (ED). Modeling and creating virtual societies almost identical and similar to the reality are considered as the strongest advantages of these agents systems. However, during the dynamic development of the artificial societies, a massive volume of data, which generally contains non-express and shrouded information and even knowledge, is involved. Therefore, dealing with this data, to study and to analyze the unclear relationships and the emerging phenomena, is a well-known weakness and bottleneck that the multi-agent systems is suffering from. In conjunction, data mining techniques are the most powerful tools that can help simulation experts to tackle this issue. This paper presents an ongoing research that combines the multi-agent based modeling and simulation systems and data mining techniques to develop a decision support system to improve the operation of the emergency department.

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31

Tangod, Kiran, and Gururaj Kulkarni. "Secure Communication through MultiAgent System-Based Diabetes Diagnosing and Classification." Journal of Intelligent Systems 29, no. 1 (June 29, 2018): 703–18. http://dx.doi.org/10.1515/jisys-2017-0353.

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Abstract The main objective of the research is to provide a multi-agent data mining system for diagnosing diabetes. Here, we use multi-agents for diagnosing diabetes such as user agent, connection agent, updation agent, and security agent, in which each agent performs their own task under the coordination of the connection agent. For secure communication, the user symptoms are encrypted with the help of Elliptic Curve Cryptography and Optimal Advanced Encryption Standard. In Optimal Advanced Encryption Standard algorithm, the key values are optimally selected by means of differential evaluation algorithm. After receiving the encrypted data, the suggested method needs to find the diabetes level of the user through multiple kernel support vector machine algorithm. Based on that, the agent prescribes the drugs for the corresponding user. The performance of the proposed technique is evaluated by classification accuracy, sensitivity, specificity, precision, recall, execution time and memory value. The proposed method will be implemented in JAVA platform.

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32

Wiedermann, Jirí. "A High Level Model of a Conscious Embodied Agent." International Journal of Software Science and Computational Intelligence 2, no. 3 (July 2010): 62–78. http://dx.doi.org/10.4018/jssci.2010070105.

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In this paper, the author describes a simple yet cognitively powerful architecture of an embodied conscious agent. The architecture incorporates a mechanism for mining, representing, processing and exploiting semantic knowledge. This mechanism is based on two complementary internal world models which are built automatically. One model (based on artificial mirror neurons) is used for mining and capturing the syntax of the recognized part of the environment while the second one (based on neural nets) for its semantics. Jointly, the models support algorithmic processes underlying phenomena similar in important aspects to higher cognitive functions such as imitation learning and the development of communication, language, thinking, and consciousness.

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33

Hou, Su Xia. "Determination the Component of Foaming Agent and its Natural Degradation Characteristics." Advanced Materials Research 641-642 (January 2013): 351–54. http://dx.doi.org/10.4028/www.scientific.net/amr.641-642.351.

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A lot of foaming agents are used during the mining mineral processing. The foaming agent is hard to degrade and remains in the wastewater and chemical oxygen demand (COD) is far exceeded the standard in mine water. In this paper, the component of foaming agent made in Guangxi province was determined and analyzed through experiments. At the same time, the effect and the contribution of foaming agent to the COD materials in the water were also been studied. The degradation performances of foaming agent in different conditions such as pH, initial density and temperature, with aeration or without aeration, with ultraviolet ray or without ultraviolet ray were studied particularly. The result of this study provided the scientific basis in choosing the appropriate new environmental foaming agent and reduced the content of COD in the flotation wastewater.

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34

Sun, Xiao Yan, Long Li, and Ping Ping Liu. "Research on Fault Diagnosis for Power Transmission Based on Mass Data Mining." Applied Mechanics and Materials 271-272 (December 2012): 1623–27. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1623.

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A Multi-Agent based transmission fault diagnosis system is researched in this paper. Many data digging analysis methods are employed, combined with data warehouse, OLAP and Multi-Agent technology. An intelligent decision supporting system for monitoring transmission network data is built. Data digging method is used to intelligently analyze and process fault data in the data warehouse, and Agent technology is used to realize data collection, pretreatment, inquiry, knowledge Automatic extraction, mining and other functions, which makes the whole mining process intellectual and intelligent. It aids transmission management with decision-making, thus to make the monitoring and repair of power grid fault more timely and accurate.

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35

Qureshi, Imran, Shaik Mohammed Imran, and G. Rama Murthy. "Multi Agent Based Cloud Security Model for Association Rule Mining." International Journal of Applied Engineering Research 10, no. 24 (December 30, 2015): 44422. http://dx.doi.org/10.37622/ijaer/10.24.2015.44422-44426.

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36

Sridhar, S. "Improving diagnostic accuracy using agent-based distributed data mining system." Informatics for Health and Social Care 38, no. 3 (September 7, 2012): 182–95. http://dx.doi.org/10.3109/17538157.2012.716110.

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37

Patel, Darshana, and J. S. "Jade Agent Framework for Distributed Data Mining and Pattern Analysis." International Journal of Computer Applications 178, no. 1 (November 15, 2017): 19–23. http://dx.doi.org/10.5120/ijca2017915714.

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38

Kang, Eun-Young. "A Mining-based Healthcare Multi-Agent System in Ubiquitous Environments." Journal of the Korea Academia-Industrial cooperation Society 10, no. 9 (September 30, 2009): 2354–60. http://dx.doi.org/10.5762/kais.2009.10.9.2354.

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39

AL-Malaise, Abdullah, Areej Malibari, and Mona Alkhozae. "Students Performance Prediction System Using Multi Agent Data Mining Technique." International Journal of Data Mining & Knowledge Management Process 4, no. 5 (September 30, 2014): 01–20. http://dx.doi.org/10.5121/ijdkp.2014.4501.

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40

Bhamra, G. S., A. K. Verma, and R. B. Patel. "Agent Based Frameworks for Distributed Association Rule Mining: An Analysis." International Journal in Foundations of Computer Science & Technology 5, no. 1 (January 31, 2015): 11–22. http://dx.doi.org/10.5121/ijfcst.2015.5102.

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41

Rana, Ajay, and Rajesh Pandey. "Data mining tools in agent-based systems: A comparative analysis." Asian Journal of Multidimensional Research 10, no. 10 (2021): 108–15. http://dx.doi.org/10.5958/2278-4853.2021.00844.2.

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42

Juan, Li, and Chen Xingguo. "Research on Application of Multi-Agent System Information Mining Technology." Modern Computer Technology and Application 2, no. 3 (2020): 73–77. http://dx.doi.org/10.35534/mcta.0203012c.

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43

Fu, Tak Chung, and Chung Leung Lui. "Agent-oriented network intrusion detection system using data mining approaches." International Journal of Agent-Oriented Software Engineering 1, no. 2 (2007): 158. http://dx.doi.org/10.1504/ijaose.2007.014403.

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44

Raja, A. Saleem, and E. George Dharma Prakash Raja. "MADPARM: Mobile Agent based Distributed and Parallel Association Rule Mining." International Journal of Engineering Trends and Technology 49, no. 6 (July 25, 2017): 375–82. http://dx.doi.org/10.14445/22315381/ijett-v49p257.

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45

Zerrougui, Salim, Farid Mokhati, and Mourad Badri. "Toward a new aspect-mining approach for multi-agent systems." Journal of Systems and Software 98 (December 2014): 9–24. http://dx.doi.org/10.1016/j.jss.2014.08.030.

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46

Anandan, R. "Machine Condition Monitoring Software Agent Using JADE and Data Mining." Journal of The Institution of Engineers (India): Series B 96, no. 1 (July 23, 2014): 61–67. http://dx.doi.org/10.1007/s40031-014-0140-x.

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47

Zhong, Ning, and Shinichi Motomura. "Agent-Enriched Data Mining: A Case Study in Brain Informatics." IEEE Intelligent Systems 24, no. 3 (May 2009): 38–45. http://dx.doi.org/10.1109/mis.2009.46.

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48

Zhang, Minjie, Xijin Tang, Quan Bai, and Jifa Gu. "Expert discovery and knowledge mining in complex multi-agent systems." Journal of Systems Science and Systems Engineering 16, no. 2 (May 26, 2007): 222–34. http://dx.doi.org/10.1007/s11518-007-5043-9.

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Bosse, Stefan, and Uwe Engel. "Real-Time Human-In-The-Loop Simulation with Mobile Agents, Chat Bots, and Crowd Sensing for Smart Cities." Sensors 19, no. 20 (October 9, 2019): 4356. http://dx.doi.org/10.3390/s19204356.

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Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods.

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Czelusniak, Dani Juliano, Osiris Canciglieri Junior, and Rui Francisco Martins Marçal. "Building Agent Software with JADE Framework." Applied Mechanics and Materials 667 (October 2014): 165–70. http://dx.doi.org/10.4028/www.scientific.net/amm.667.165.

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Abstract:

Currently, a special type of computer system’s that has its roots in the foundations of Artificial Intelligence disciplines, takes shape and strength. They are the "Agents Software Systems". Thus, the goal of this paper is to present this kind of software. As a result, shows that the these new tools to build software, allows better scalability of computerized software solutions, at the same time that can assists processes where the mining of information happens in heterogeneous environments.

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