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Journal articles on the topic 'Intelligent Agents'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Singh, Sushil. "Universal Artificial Intelligence for Intelligent Agents: An Approach to Super Intelligent Agents." IOSR Journal of Computer Engineering 12, no. 6 (2013): 43–48. http://dx.doi.org/10.9790/0661-1264348.

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2

Kozma, J. "Intelligent agents." IEEE Potentials 17, no. 2 (1998): 16–19. http://dx.doi.org/10.1109/45.666640.

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3

Sarma, V. V. S. "Intelligent Agents." IETE Journal of Research 42, no. 3 (1996): 105–9. http://dx.doi.org/10.1080/03772063.1996.11415911.

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4

Riecken, Doug. "Intelligent agents." Communications of the ACM 37, no. 7 (1994): 18–21. http://dx.doi.org/10.1145/176789.176790.

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5

Rodriguez, Walter, Janusz Zalewski, and Elias Kirche. "Beyond Intelligent Agents." International Journal of e-Collaboration 3, no. 2 (2007): 1–15. http://dx.doi.org/10.4018/jec.2007040101.

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6

Nadrljanski, Mila, and V. Batinica. "Intelligent media agents." International Journal of Intelligent Defence Support Systems 3, no. 1/2 (2010): 128. http://dx.doi.org/10.1504/ijidss.2010.033682.

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7

Jeapes, Ben. "NEURAL INTELLIGENT AGENTS." Online and CD-Rom Review 20, no. 5 (1996): 260–62. http://dx.doi.org/10.1108/eb024592.

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8

Sycara, K., A. Pannu, M. Willamson, Dajun Zeng, and K. Decker. "Distributed intelligent agents." IEEE Expert 11, no. 6 (1996): 36–46. http://dx.doi.org/10.1109/64.546581.

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9

Vasilakos, A. V. "Intelligent Information Agents." Computer Communications 23, no. 18 (2000): 1790. http://dx.doi.org/10.1016/s0140-3664(00)00211-5.

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10

Wong, A. "Intelligent Software Agents." Computer Communications 23, no. 7 (2000): 695–96. http://dx.doi.org/10.1016/s0140-3664(99)00186-3.

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11

HAGRAS, H., V. CALLAGHAN, and M. COLLEY. "Intelligent embedded agents." Information Sciences 171, no. 4 (2005): 289–92. http://dx.doi.org/10.1016/j.ins.2004.09.006.

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12

Valckenaers, Paul, Hadeli, Bart Saint Germain, Paul Verstraete, Jan van Belle, and Hendrik van Brussel. "FROM INTELLIGENT AGENTS TO INTELLIGENT BEINGS." IFAC Proceedings Volumes 40, no. 18 (2007): 31–36. http://dx.doi.org/10.3182/20070927-4-ro-3905.00007.

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13

Dr., R. Sabitha, and S. Karthik Dr. "EMPLOYING AGENTS IN DESCRIPTIVE MINING." International Journal of Research – Granthaalayah 4, no. 2 (2017): 111–20. https://doi.org/10.5281/zenodo.848100.

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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 environ

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14

Bryndin, Evgeny. "Functional and Harmonious Selforganization of Large Intellectual Agent Ensembles with Smart Hybrid Competencies via Wireless and Mobile Networks." International Journal of Wireless & Mobile Networks 13, no. 05 (2021): 1–13. http://dx.doi.org/10.5121/ijwmn.2021.13501.

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Artificial intelligence of large ensembles of intelligent agents in terms of computing power surpasses human intelligence. He is capable of artificial thinking and understanding. Giant ensembles of intellectual agents with artificial consciousness and intelligence are able, for the results set by the person necessary for him, to find solutions for their obtaining on the basis of professional competence and experience accumulation. The professional competence of artificial intelligence is the ability to use technologies, including computer vision, natural language processing, speech recognition

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15

Weinbaum (Weaver), David, and Viktoras Veitas. "Open ended intelligence: the individuation of intelligent agents." Journal of Experimental & Theoretical Artificial Intelligence 29, no. 2 (2016): 371–96. http://dx.doi.org/10.1080/0952813x.2016.1185748.

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16

Desouza, Kevin C. "Intelligent agents for competitive intelligence: Survey of applications." Competitive Intelligence Review 12, no. 4 (2001): 57–63. http://dx.doi.org/10.1002/cir.1032.

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17

Lizán, Francisco J. Mora. "Intelligent Buildings: Foundation for Intelligent Physical Agents." International Journal of Engineering Research and Applications 07, no. 05 (2017): 21–25. http://dx.doi.org/10.9790/9622-0705022125.

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18

Baylor, Amy. "Beyond Butlers: Intelligent Agents as Mentors." Journal of Educational Computing Research 22, no. 4 (2000): 373–82. http://dx.doi.org/10.2190/1ebd-g126-tfcy-a3k6.

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This article discusses pedagogical issues for intelligent agents to successfully serve as mentors for educational purposes. Broader issues about the nature or persona necessary for an intelligent agent as mentor are discussed, incorporating usability and human-computer interaction issues such as the anthropomorphic qualities of the agent and the social relationship between learner and agent. Overall, to be effective for learning, it is argued that there are three main requirements for agents as mentors: 1) regulated intelligence; 2) the existence of a persona; and 3) pedagogical control.

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19

Bryndin, Evgeny. "About Formation of International Ethical Digital Environment with Smart Artificial Intelligence." International Journal of Information Technology, Control and Automation 11, no. 1 (2021): 1–9. http://dx.doi.org/10.5121/ijitca.2021.11101.

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Intellectual agent ensembles allow you to create digital environment by professional images with language, behavioral and active communications, when images and communications are implemented by agents with smart artificial intelligence. Through language, behavioral and active communications, intellectual agents implement collective activities. The ethical standard through intelligent agents allows you to regulate the safe use of ensembles made of robots and digital doubles with creative communication artificial intelligence in the social sphere, industry and other professional fields. The use

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20

Wali, Abdul, Saipunidzam Mahamad, and Suziah Sulaiman. "Task Automation Intelligent Agents: A Review." Future Internet 15, no. 6 (2023): 196. http://dx.doi.org/10.3390/fi15060196.

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As technological advancements increase exponentially, mobile phones become smarter with machine learning and artificial intelligence algorithms. These advancements have allowed mobile phone users to perform most of their daily routine tasks on mobile phones; tasks performed in daily routines are called repetitive tasks and are performed manually by the users themselves. However, machine learning and artificial intelligence have enabled those tasks to be performed automatically, known as task automation. The users can perform task automation, e.g., through creating automation rules or an intell

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21

Edmonds, Bruce. "MODELING SOCIALLY INTELLIGENT AGENTS." Applied Artificial Intelligence 12, no. 7-8 (1998): 677–99. http://dx.doi.org/10.1080/088395198117587.

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22

Crawford, P. "Review: Intelligent Software Agents." Computer Bulletin 41, no. 3 (1999): 31. http://dx.doi.org/10.1093/combul/41.3.31-c.

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23

Harris, L. E. "Intelligent agents peer skyward." Computing in Science & Engineering 6, no. 5 (2004): 4–7. http://dx.doi.org/10.1109/mcse.2004.40.

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24

Nardi, Bonnie A., James R. Miller, and David J. Wright. "Collaborative, programmable intelligent agents." Communications of the ACM 41, no. 3 (1998): 96–104. http://dx.doi.org/10.1145/272287.272331.

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25

Sukhatme, Gaurav S. "Intelligent embodied autonomous agents." Robotics and Autonomous Systems 29, no. 2-3 (1999): 109–10. http://dx.doi.org/10.1016/s0921-8890(99)00045-7.

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26

Jamal, Karim, Michael Maier, and Shyam Sunder. "Simple Agents, Intelligent Markets." Computational Economics 49, no. 4 (2016): 653–75. http://dx.doi.org/10.1007/s10614-016-9582-3.

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27

Hsinchun Chen, A. Houston, J. Nunamaker, and J. Yen. "Toward intelligent meeting agents." Computer 29, no. 8 (1996): 62–70. http://dx.doi.org/10.1109/2.532047.

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28

Serenko, Alexander, and Brian Detlor. "Intelligent agents as innovations." AI & SOCIETY 18, no. 4 (2004): 364–81. http://dx.doi.org/10.1007/s00146-004-0310-5.

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29

Bryndin, Evgeniy. "Robotics by multimodal self-organizing ensembles of software and hardware agents with artificial intelligence." Research on Intelligent Manufacturing and Assembly 2, no. 1 (2024): 60–69. http://dx.doi.org/10.25082/rima.2023.01.003.

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Self-organizing ensembles of software and hardware agents with artificial intelligence model the intellectual abilities of a person's natural intelligence. The Creator endowed man with various types of intellectual abilities: generation of meanings, perception of meanings, meaningful actions and behavior, sensory reaction to meanings, emotional reaction to meanings. Based on the synergy of various intellectual abilities, a person carries out life activities. For example, Dialogue is conducted on the basis of two intellectual abilities: the generation and perception of meanings. A multimodal se

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30

ANASTASSAKIS, G., and T. PANAYIOTOPOULOS. "A SYSTEM FOR LOGIC-BASED INTELLIGENT VIRTUAL AGENTS." International Journal on Artificial Intelligence Tools 13, no. 03 (2004): 593–621. http://dx.doi.org/10.1142/s0218213004001703.

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Combination of logic-based artificial intelligence with virtual reality in intelligent agent systems is an approach not extensively sought after to date. It is our belief that significant gain is to be expected if the technical challenges involved are overcome. In this paper, we describe the mVlTAL intelligent agent system, which is our latest effort towards this direction. The system is a contemporary intelligent agent system with applications in numerous areas, including intelligent virtual environments and formal artificial intelligence research. The system focuses largely on logic-based ap

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31

Bryndin, Evgeniy. "International Standardization Safe to Use of Artificial Intelligence." Research on Intelligent Manufacturing and Assembly 4, no. 1 (2025): 185–91. https://doi.org/10.25082/rima.2025.01.005.

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Nowadays, the symbiosis of human abilities and the mastery of artificial intelligence will contribute to increased productivity and excellence in industry and social services. The use of artificial intelligence in various fields requires standardization of the safety of its knowledge and skills. International collaboration on artificial intelligence safety standardization is expanding. The UN has created a Global Advisory Body on Artificial Intelligence to support the efforts of the international community of specialists in managing intelligent systems related to the risks and safety of their

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32

Alexander, Samuel Allen. "Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents." Journal of Artificial General Intelligence 10, no. 1 (2019): 24–45. http://dx.doi.org/10.2478/jagi-2019-0003.

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Abstract Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg-Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can

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33

Anchekov, M. I., A. Z. Apshev, K. Ch Bzhikhatlov, et al. "Principles of ontophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures." BIO Web of Conferences 84 (2024): 02015. http://dx.doi.org/10.1051/bioconf/20248402015.

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The purpose of the study is to study the possibilities of multigenerational optimization of behavior control systems for agents of general artificial intelligence capable of independently solving a universal range of tasks in a real environment. The main principles of ontophylogenetic synthesis of control systems for agents of general artificial intelligence based on multi-agent neurocognitive architectures have been developed. Methods and algorithms for synthesizing the phenotypes of control systems of intelligent agents according to their genotypes are proposed. A software package for simula

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34

BAILIN, SIDNEY C., and WALT TRUSZKOWSKI. "Ontology negotiation between intelligent information agents." Knowledge Engineering Review 17, no. 1 (2002): 7–19. http://dx.doi.org/10.1017/s0269888902000292.

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This paper describes an approach to ontology negotiation between agents supporting intelligent information management. Ontologies are declarative (data-driven) expressions of an agent's “world”: the objects, operations, facts and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies.Our objective is to increase the opportunities for “strange agents” – that is, agents not necessarily developed within the same framework or with the same contextual operating assu

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35

Sabitha, R., and Karthik. "EMPLOYING AGENTS IN DESCRIPTIVE MINING." International Journal of Research -GRANTHAALAYAH 4, no. 2 (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 environ

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36

Ioannidis, Evangelos, Nikos Varsakelis, and Ioannis Antoniou. "Intelligent Agents in Co-Evolving Knowledge Networks." Mathematics 9, no. 1 (2021): 103. http://dx.doi.org/10.3390/math9010103.

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We extend the agent-based models for knowledge diffusion in networks, restricted to random mindless interactions and to “frozen” (static) networks, in order to take into account intelligent agents and network co-evolution. Intelligent agents make decisions under bounded rationality. This is the key distinction of intelligent interacting agents compared to mindless colliding molecules, involved in the usual diffusion mechanism resulting from accidental collisions. The co-evolution of link weights and knowledge levels is modeled at the local microscopic level of “agent-to-agent” interaction. Our

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37

Mackworth, Alan K. "Agents, Bodies, Constraints, Dynamics, and Evolution." AI Magazine 30, no. 1 (2009): 7. http://dx.doi.org/10.1609/aimag.v30i1.2174.

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The theme of this article is the dynamics of evolution of agents. That theme is applied to the evolution of constraint satisfaction, of agents themselves, of our models of agents, of artificial intelligence and, finally, of the Association for the Advancement of Artificial Intelligence (AAAI). The overall thesis is that constraint satisfaction is central to proactive and responsive intelligent behavior.

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38

Ji, Wen, Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, and Yiqiang Chen. "Quality-time-complexity universal intelligence measurement." International Journal of Crowd Science 2, no. 1 (2018): 18–26. http://dx.doi.org/10.1108/ijcs-01-2018-0003.

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Purpose With development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more and more autonomous and smart. Therefore, there is a growing demand to develop a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method. Design/methodology/approach This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents. Findings By observ

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39

Brinkschulte, Luisa, Stephan Schlögl, Alexander Monz, Pascal Schöttle, and Matthias Janetschek. "Perspectives on Socially Intelligent Conversational Agents." Multimodal Technologies and Interaction 6, no. 8 (2022): 62. http://dx.doi.org/10.3390/mti6080062.

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The propagation of digital assistants is consistently progressing. Manifested by an uptake of ever more human-like conversational abilities, respective technologies are moving increasingly away from their role as voice-operated task enablers and becoming rather companion-like artifacts whose interaction style is rooted in anthropomorphic behavior. One of the required characteristics in this shift from a utilitarian tool to an emotional character is the adoption of social intelligence. Although past research has recognized this need, more multi-disciplinary investigations should be devoted to t

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40

Binkley, Timothy. "Autonomous Creations: Birthing Intelligent Agents." Leonardo 31, no. 5 (1998): 333. http://dx.doi.org/10.2307/1576591.

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41

Walden, Pirkko, Christer Carlsson, and Shuhua Liu. "Industry foresight with intelligent agents." Human Systems Management 19, no. 3 (2000): 169–80. http://dx.doi.org/10.3233/hsm-2000-19304.

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Modern time managers have access to many more data sources than managers of earlier times, and better instruments and resources to deal with large amounts of data. In principle, this means that they have a better command of facts and should be able to work out better assessments of their business environment. In reality, however, information overflow and problems with the quality and reliability of information complicate the picture. We have a support system with intelligent agents to help managers conduct constantly active scanning and interpretation activities with hundreds of data sources.

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42

AMANDI, Analía, Marcelo CAMPO, Marcelo ARMENTANO, and Luis BERDUN. "Intelligent Agents for Distance Learning." Informatics in Education 2, no. 2 (2003): 161–80. http://dx.doi.org/10.15388/infedu.2003.12.

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43

Agarwal, Rakesh, Amrita Deo, and Swati Das. "Intelligent agents in E-learning." ACM SIGSOFT Software Engineering Notes 29, no. 2 (2004): 1. http://dx.doi.org/10.1145/979743.979755.

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44

Yilmaz, Levent, Tuncer Ören, and Nasser-Ghasem Aghaee. "Intelligent agents, simulation, and gaming." Simulation & Gaming 37, no. 3 (2006): 339–49. http://dx.doi.org/10.1177/1046878106289089.

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45

Locke, Chris. "INTELLIGENT AGENTS CREATE DUMB USERS (?)." Online and CD-Rom Review 21, no. 6 (1997): 369–72. http://dx.doi.org/10.1108/eb024644.

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46

Hughes, E., and M. Lewis. "Intelligent agents for radar systems." Electronics Systems and Software 3, no. 1 (2005): 39–43. http://dx.doi.org/10.1049/ess:20050107.

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47

Amer-Yahia, Sihem. "Intelligent Agents for Data Exploration." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4521–30. http://dx.doi.org/10.14778/3685800.3685913.

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Data Exploration is an incremental process that helps users express what they want through a conversation with the data. Reinforcement Learning (RL) is one of the most notable approaches to automate data exploration and several solutions have been proposed. We first summarize some RL solutions that were built for different applications. In this context, various data exploration operators are leveraged including traditional roll-up and drill-down operations and text-based operations. An RL agent is trained to generate the best policy according to a hand-crafted reward function. The benefit of t

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48

Duff, Simon, John Thangarajah, and James Harland. "MAINTENANCE GOALS IN INTELLIGENT AGENTS." Computational Intelligence 30, no. 1 (2012): 71–114. http://dx.doi.org/10.1111/coin.12000.

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49

Braynov, Sviatoslav. "Coalitions of malicious intelligent agents." International Journal of Web Engineering and Technology 1, no. 4 (2004): 397. http://dx.doi.org/10.1504/ijwet.2004.006266.

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50

Feliu, S. "Intelligent Agents and Consumer Protection." International Journal of Law and Information Technology 9, no. 3 (2001): 235–48. http://dx.doi.org/10.1093/ijlit/9.3.235.

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