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Journal articles on the topic 'Systems intelligence'

To see the other types of publications on this topic, follow the link: Systems intelligence.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Sauter, Vicki L. "Competitive intelligence systems." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 36, no. 2 (June 7, 2005): 43–57. http://dx.doi.org/10.1145/1066149.1066154.

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2

Törmänen, Juha, Raimo P. Hämäläinen, and Esa Saarinen. "Systems intelligence inventory." Learning Organization 23, no. 4 (May 9, 2016): 218–31. http://dx.doi.org/10.1108/tlo-01-2016-0006.

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Purpose Systems intelligence (SI) (Saarinen and Hämäläinen, 2004) is a construct defined as a person’s ability to act intelligently within complex systems involving interaction and feedback. SI relates to our ability to act in systems and reason about systems to adaptively carry out productive actions within and with respect to systems such as organizations, family and everyday life. This paper aims to develop an inventory to measure the SI construct. Design/methodology/approach A combination of exploratory and confirmatory factor analyses were carried out using data from self-report questionnaires. Findings Eight factors labeled systemic perception, attunement, attitude, spirited discovery, reflection, wise action, positive engagement and effective responsiveness are identified as the main components of SI. SI has associations with emotional intelligence but also captures additional dimensions. People in supervisor positions are found to score higher in a number of the SI factors. Originality/value A new measure is developed to evaluate and develop our ability to succeed in systemic contexts. The new measure is suggested to be particularly applicable in organizational contexts. It is directly related to the original core disciplines of the learning organization as described by Senge (1990), in particular personal mastery and systems thinking.

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3

Allen, Louis. "Japanese Intelligence Systems." Journal of Contemporary History 22, no. 4 (October 1987): 547–62. http://dx.doi.org/10.1177/002200948702200401.

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4

Abdullayeva, O., and M. Engalichev. "Artificial intelligence systems." Значение цифровых технологий в изучении истории Узбекистана 1, no. 01 (October 26, 2022): 382–85. http://dx.doi.org/10.47689/.v1i01.13612.

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Today, artificial intelligence is becoming more and more popular in almost every aspect of our daily lives. Artificial intelligence is usually concerned withthe creation of computer systems with the capabilities of the human mind: understanding, learning, discussing, solving problems, translating, and so on. During the study of artificial intelligence, human-intelligent programming, technologies and tools for designing intelligent systems, databases, knowledge bases, database management systems, speech interfaces, computer linguistics, computer graphics, computer networks and web programming , information security in computer systems

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5

DONG, TIANSI. "MODELING HUMAN INTELLIGENCE WITH SLOW INTELLIGENCE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 22, no. 08 (December 2012): 1039–53. http://dx.doi.org/10.1142/s0218194012400141.

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Chang [S. K. Chang, A general framework for slow intelligence systems, International Journal of Software Engineering and Knowledge Engineering, 20(1) (2010) 1–18.] argues that slow intelligence is a property shared by a large number of intelligent systems, and proposes a general framework, which has quick/slow decision cycles. The aim of this paper is to show that the general framework is applicable for modeling human intelligence. We show that the quick/slow decision cycles model the procedural/declarative memory system, and collect supports from adults' second language acquisition and aphasic bilinguals. As spatial cognition is fundamental in human intelligence, we show in detail how object tracing can be simulated within slow intelligence framework. We introduce a psychological experiment on object tracing, and present two rules in psychology which govern object tracing results under certain conditions, namely, the rule of minimal spatial transformation, and the rule of categorical continuity. We review related works in spatial knowledge representation and reduce object tracing problems into the problem of object mapping. We show that the following capabilities can be simulated in the slow intelligence framework: the capability of acquiring knowledge about one scene, and the capability of mapping object between scenes. An experimental result is presented.

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6

Krapivny, Jurij N., and Aleksandr O. Kryvonos. "HYBRID INTELLIGENCE SYSTEMS ARCHITECTURE." ELECTRICAL AND COMPUTER SYSTEMS 22, no. 98 (May 25, 2016): 260–66. http://dx.doi.org/10.15276/eltecs.22.98.2016.43.

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7

Chen, Z. "ANALOGY, SYSTEMS, AND INTELLIGENCE." Cybernetics and Systems 22, no. 6 (November 1991): 611–16. http://dx.doi.org/10.1080/01969729108902302.

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8

Adlassnig, Klaus-Peter. "Artificial-intelligence-augmented systems." Artificial Intelligence in Medicine 24, no. 1 (January 2002): 1–4. http://dx.doi.org/10.1016/s0933-3657(01)00102-6.

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9

Vickery, Brian, and Alina Vickery. "Intelligence and information systems." Journal of Information Science 16, no. 1 (February 1990): 65–70. http://dx.doi.org/10.1177/016555159001600111.

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10

Tunowski, Remigiusz. "Organization Effectiveness and Business Intelligence Systems. Literature Review." Management and Business Administration. Central Europe 23, no. 4 (December 15, 2015): 55–73. http://dx.doi.org/10.7206/mba.ce.2084-3356.157.

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11

Cichocki, Andrzej, and Alexander P. Kuleshov. "Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles." Computational Intelligence and Neuroscience 2021 (February 20, 2021): 1–21. http://dx.doi.org/10.1155/2021/8893795.

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This article discusses some trends and concepts in developing a new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional, and ethical intelligence. We describe various aspects of multiple human intelligences and learning styles, which may affect a variety of AI problem domains. Using the concept of “multiple intelligences” rather than a single type of intelligence, we categorize and provide working definitions of various AGIs depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human users and each other but also to efficiently exchange knowledge and wisdom with abilities of cooperation, collaboration, and even cocreating something new and valuable and have metalearning capacities. Multiagent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent.

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12

Carvalho, Andrea V., and Miguel Esteban-Navarro. "Intelligence audit: Planning and assessment of organizational intelligence systems." Journal of Librarianship and Information Science 48, no. 1 (July 11, 2014): 47–59. http://dx.doi.org/10.1177/0961000614536198.

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13

Safdari, Reza, Jebraeil Farzi, Marjan Ghazisaeidi, Mahboobeh Mirzaee, and Azadeh Goodini. "Healthcare intelligence risk detection systems." Open Journal of Preventive Medicine 03, no. 08 (2013): 461–69. http://dx.doi.org/10.4236/ojpm.2013.38062.

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14

Hua, Qilin, Xiao Cui, Keyu Ji, Bingjun Wang, and Weiguo Hu. "Piezotronics enabled artificial intelligence systems." Journal of Physics: Materials 4, no. 2 (March 25, 2021): 022003. http://dx.doi.org/10.1088/2515-7639/abe55f.

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15

Kukshev, Vyacheslav I. "Classification of Artificial Intelligence Systems." Economic Strategies 144 (October 23, 2020): 58–67. http://dx.doi.org/10.33917/es-6.172.2020.58-67.

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The article considers the classification of Artificial Intelligence (AI) systems. The role of AI has increased significantly recently in all areas of life. The use of AI in public administration, in production, in medicine, in the military, in the social sphere, etc., raised a number of questions related to the definition of AI and classification of AI systems. Classification of AI is necessary to understand the role of AI in the digital economy. Classification becomes important in the context of intensive development of international standards for AI systems and knowledge-based systems (expert, neural, multi-agent, cyber-physical systems and systems based on the industrial Internet)

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16

Benferhat, Salem, Karim Tabia, and Moonis Ali. "Intelligence Systems: Trends and Challenges." AI Magazine 38, no. 4 (December 28, 2017): 87–88. http://dx.doi.org/10.1609/aimag.v38i4.2769.

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This Conference Report focuses on the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2017) which was held in Arras, France June 25 – 30, 2017. The conference was sponsored by the International Society of Applied Intelligence (ISAI) in cooperation with AAAI, SIGAI/ACM, and several other international organizations. This is an annual event. The first conference, IEA/AIE-1988, was organized in 1988 in Tullahoma Tennessee. Since then conference has been held internationally in many countries including Germany, Scotland, Australia, Spain, Egypt, Hungary, England, Italy, France, Japan, Poland, China, Taiwan, Netherlands, and South Korea. The conference has always been sponsored by ISAI and all conferences have been held in cooperation of AAAI. The conference provides a medium for presenting, publishing and communicating about the scientific research and technological achievements accomplished by the international community.

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17

Xu, Li Da. "Systems research on artificial intelligence." Systems Research and Behavioral Science 39, no. 3 (May 2022): 359–60. http://dx.doi.org/10.1002/sres.2839.

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18

Dzitac, Ioan, and Boldur E. Bărbat. "Artificial Intelligence + Distributed Systems = Agents." International Journal of Computers Communications & Control 4, no. 1 (March 1, 2009): 17. http://dx.doi.org/10.15837/ijccc.2009.1.2410.

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The connection with Wirth’s book goes beyond the title, albeit confining the area to modern Artificial Intelligence (AI). Whereas thirty years ago, to devise effective programs, it became necessary to enhance the classical algorithmic framework with approaches applied to limited and focused subdomains, in the context of broad-band technology and semantic web, applications - running in open, heterogeneous, dynamic and uncertain environments-current paradigms are not enough, because of the shift from programs to processes. Beside the structure as position paper, to give more weight to some basic assertions, results of recent research are abridged and commented upon in line with new paradigms. Among the conclusions: a) Nondeterministic software is unavoidable; its development entails not just new design principles but new computing paradigms. b) Agent-oriented systems, to be effectual, should merge conventional agent design with approaches employed in advanced distributed systems (where parallelism is intrinsic to the problem, not just a mean to speed up).

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19

Rejmaniak, Rafał. "Bias in Artificial Intelligence Systems." Białostockie Studia Prawnicze 26, no. 3 (September 1, 2021): 25–42. http://dx.doi.org/10.15290/bsp.2021.26.03.02.

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Abstract Artificial intelligence systems are currently deployed in many areas of human activity. Such systems are increasingly assigned tasks that involve taking decisions about people or predicting future behaviours. These decisions are commonly regarded as fairer and more objective than those taken by humans, as AI systems are thought to be resistant to such influences as emotions or subjective beliefs. In reality, using such a system does not guarantee either objectivity or fairness. This article describes the phenomenon of bias in AI systems and the role of humans in creating it. The analysis shows that AI systems, even if operating correctly from a technical standpoint, are not guaranteed to take decisions that are more objective than those of a human, but those systems can still be used to reduce social inequalities.

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20

Brock, David C., and Burton Grad. "Expert Systems: Commercializing Artificial Intelligence." IEEE Annals of the History of Computing 44, no. 1 (January 1, 2022): 5–7. http://dx.doi.org/10.1109/mahc.2022.3149612.

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21

Francesco Di Tria, Ezio Lefons, and Filippo Tangorra. "Evaluation of Business Intelligence Systems." International Journal of Information Processing and Management 4, no. 3 (May 31, 2013): 222–37. http://dx.doi.org/10.4156/ijipm.vol4.issue3.22.

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22

Rouhani, Saeed, Amir Ashrafi, Ahad Zare Ravasan, and Samira Afshari. "Business Intelligence Systems Adoption Model." Journal of Organizational and End User Computing 30, no. 2 (April 2018): 43–70. http://dx.doi.org/10.4018/joeuc.2018040103.

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Decision support and business intelligence systems have been increasingly adopted in organizations, while understanding the nature of affecting factors on such adoption decisions need receiving much academic interest. This article attempts to provide an in-depth analysis toward understanding the critical factors which affect the decision to adopt business intelligence (BI) in the context of banking and financial industry. In this regard, it examines a conceptual model that shows the impacts of different technological, organizational, and environmental factors in the decision to adopt BI by a firm. Structural equation modeling (SEM) was used for data analysis and test the relevant hypothesis. The results of this article which are derived from theoretical discussion of hypothesizes show that from nine hypothesized relationships—perceived tangible and intangible benefits, firm size, organizational readiness, strategy, industry competition and competitors absorptive capacity—affect BIS adoption in the surveyed cases.

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23

Muñoz, Andrés, Juan Carlos Augusto, Vincent Tam, and Hamid Aghajan. "Artificial intelligence for IoT systems." Journal of Ambient Intelligence and Smart Environments 12, no. 1 (January 27, 2020): 1. http://dx.doi.org/10.3233/ais-200548.

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24

Stecyk, Adam. "Business Intelligence systems in SMEs." European Journal of Service Management 27 (2018): 409–13. http://dx.doi.org/10.18276/ejsm.2018.27/2-50.

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25

Pickett, John R., and Thomas L. Case. "Expert Systems and Artificial Intelligence." Research-Technology Management 33, no. 3 (May 1990): 35–38. http://dx.doi.org/10.1080/08956308.1990.11670661.

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26

Wilson, Judith D. "Artificial Intelligence and Tutoring Systems." Journal of Research on Computing in Education 23, no. 1 (September 1990): 142–44. http://dx.doi.org/10.1080/08886504.1990.10781950.

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27

Wilson, Harold O., and Anna Marie Burford. "Artificial Intelligence and Expert Systems." Journal of Education for Business 65, no. 6 (March 1990): 275–79. http://dx.doi.org/10.1080/08832323.1990.10117441.

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28

Nguyen, Ngoc Thanh, Edward Szczerbicki, Bogdan Trawiński, and Van Du Nguyen. "Collective intelligence in information systems." Journal of Intelligent & Fuzzy Systems 37, no. 6 (December 23, 2019): 7113–15. http://dx.doi.org/10.3233/jifs-179324.

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29

IHARA, Hirokazu. "Artificial intelligence and space systems." Journal of the Japan Society for Aeronautical and Space Sciences 35, no. 407 (1987): 561–68. http://dx.doi.org/10.2322/jjsass1969.35.561.

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30

Zibar, Darko, Francesco Da Ros, Giovanni Brajato, and Uiara C. de Moura. "Toward Intelligence in Photonic Systems." Optics and Photonics News 31, no. 3 (March 1, 2020): 34. http://dx.doi.org/10.1364/opn.31.3.000034.

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31

Maddux, Cleborne. "Expert Systems and Artificial Intelligence:." Computers in the Schools 4, no. 2 (December 1987): 49–52. http://dx.doi.org/10.1300/j025v04n02_05.

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32

Atlan, H., J. P. Dupuy, and M. Koppel. "Individual Alienation and Systems Intelligence." IFAC Proceedings Volumes 20, no. 10 (September 1987): 37–40. http://dx.doi.org/10.1016/s1474-6670(17)59028-x.

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33

Mahabala, H. N. "Artificial intelligence — Methodology, systems, applications." Artificial Intelligence in Engineering 2, no. 2 (April 1987): 125. http://dx.doi.org/10.1016/0954-1810(87)90188-9.

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34

Prieto, Alberto, Joan Cabestany, and Francisco Sandoval. "Computational intelligence and bioinspired systems." Neurocomputing 70, no. 16-18 (October 2007): 2701–3. http://dx.doi.org/10.1016/j.neucom.2006.06.009.

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35

Leva, Sonia, and Emanuele Ogliari. "Computational Intelligence in Photovoltaic Systems." Applied Sciences 9, no. 9 (May 2, 2019): 1826. http://dx.doi.org/10.3390/app9091826.

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36

Palhares, R. M., Y. Yuan, and Q. Wang. "Artificial Intelligence in Industrial Systems." IEEE Transactions on Industrial Electronics 66, no. 12 (December 2019): 9636–40. http://dx.doi.org/10.1109/tie.2019.2916709.

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37

Dudewicz, Edward J. "Expert Systems and Artificial Intelligence." American Journal of Mathematical and Management Sciences 9, no. 3-4 (February 1989): 183–89. http://dx.doi.org/10.1080/01966324.1989.10737261.

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38

Chen, Tin-Chih Toly, W. Art Chaovalitwongse, and I.-Hsuan Hong. "Optimization of ambient intelligence systems." Operational Research 18, no. 3 (September 1, 2018): 575–77. http://dx.doi.org/10.1007/s12351-018-0422-1.

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39

Lu, Huimin, and Yujie Li. "Cognitive Computing for Intelligence Systems." Mobile Networks and Applications 25, no. 4 (January 17, 2020): 1434–35. http://dx.doi.org/10.1007/s11036-019-01428-y.

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40

Althoff, K. D., N. Kratz, M. M. Richter, and P. Spieker. "Planning systems and artificial intelligence." Annals of Operations Research 16, no. 1 (December 1988): 1–12. http://dx.doi.org/10.1007/bf02283732.

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41

Chen, Toly, T. Warren Liao, and Fusheng Yu. "Fuzzy Collaborative Intelligence and Systems." International Journal of Intelligent Systems 30, no. 6 (April 17, 2015): 617–19. http://dx.doi.org/10.1002/int.21720.

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42

Gantt, Susan P., and Yvonne M. Agazarian. "SYSTEMS‐CENTERED EMOTIONAL INTELLIGENCE: BEYOND INDIVIDUAL SYSTEMS TO ORGANIZATIONAL SYSTEMS." Organizational Analysis 12, no. 2 (February 2004): 147–69. http://dx.doi.org/10.1108/eb028990.

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43

Muhammad Zunnurain Hussain, Zaka Ullah, Taimoor Hassan, and Muhammad Zulkifl Hasan. "Ambient Intelligence." Lahore Garrison University Research Journal of Computer Science and Information Technology 2, no. 4 (December 31, 2018): 15–20. http://dx.doi.org/10.54692/lgurjcsit.2018.020456.

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Ambient intelligence is electronic environment which are sensitive to the presence of people. It is the light of future technologies. All AMI subsystems are interact with each other to accomplish their goal. AMI applications which are directly interact to define the environment. It shows that human environment which are not produced this process. The AMI systems should identify the various emotions of human experience. The AMI systems can also highlight the communication of the user. The AMI systems which is consists of robots and sensors.

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44

Bick, Markus, and Tyge-F. Kummer. "Ambient Intelligence." WIRTSCHAFTSINFORMATIK 52, no. 5 (August 27, 2010): 311–14. http://dx.doi.org/10.1007/s11576-010-0241-3.

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45

Iantovics, Kountchev, and Crișan. "ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence." Symmetry 11, no. 9 (September 4, 2019): 1123. http://dx.doi.org/10.3390/sym11091123.

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In this research, we define a specific type of performance of the intelligent agent-based systems (IABSs) in terms of a difficult problem-solving intelligence measure. Many studies present the successful application of intelligent cooperative multiagent systems (ICMASs) for efficient, flexible and robust solving of difficult real-life problems. Based on a comprehensive study of the scientific literature, we conclude that there is no unanimous view in the scientific literature on machine intelligence, or on what an intelligence metric must measure. Metrics presented in the scientific literature are based on diverse paradigms. In our approach, we assume that the measurement of intelligence is based on the ability to solve difficult problems. In our opinion, the measurement of intelligence in this context is important, as it allows the differentiation between ICMASs based on the degree of intelligence in problem-solving. The recent OutIntSys method presented in the scientific literature can identify systems with outlier high and outlier low intelligence from a set of studied ICMASs. In this paper, a novel universal method called ExtrIntDetect, defined on the basis of a specific series of computing processes and analyses, is proposed for the detection of the ICMASs with statistical outlier low and high problem-solving intelligence from a given set of studied ICMASs. ExtrIntDetect eliminates the disadvantage of the OutIntSys method with respect to its limited robustness. The recent symmetric MetrIntSimil metric presented in the literature is capable of measuring and comparing the intelligence of large numbers of ICMASs and based on their respective problem-solving intelligences in order to classify them into intelligence classes. Systems whose intelligence does not statistically differ are classified as belonging to the same class of intelligent systems. Systems classified in the same intelligence class are therefore able to solve difficult problems using similar levels of intelligence. One disadvantage of the symmetric MetrIntSimil lies in the fact that it is not able to detect outlier intelligence. Based on this fact, the ExtrIntDetect method could be used as an extension of the MetrIntSimil metric. To validate and evaluate the ExtrIntDetect method, an experimental evaluation study on six ICMASs is presented and discussed.

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46

Vehlken, Sebastian. "Pervasive Intelligence." Digital Culture & Society 4, no. 1 (March 1, 2018): 107–32. http://dx.doi.org/10.14361/dcs-2018-0108.

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Abstract This article seeks to situate collective or swarm robotics (SR) on a conceptual pane which on the one hand sheds light on the peculiar form of AI which is at play in such systems, whilst on the other hand it considers possible consequences of a widespread use of SR with a focus on swarms of Unmanned Aerial Systems (Swarm UAS). The leading hypothesis of this article is that Swarm Robotics create a multifold “spatial intelligence”, ranging from the dynamic morphologies of such collectives via their robust self-organization in changing environments to representations of these environments as distributed 4D-sensor systems. As is shown on the basis of some generative examples from the field of UAS, robot swarms are imagined to literally penetrate space and control it. In contrast to classical forms of surveillance or even “sousveillance”, this procedure could be called perveillance.

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47

Sadiku, Matthew N. O., Justin Foreman, and Sarhan M. Musa. "Computational Intelligence." European Scientific Journal, ESJ 14, no. 21 (July 31, 2018): 56. http://dx.doi.org/10.19044/esj.2018.v14n21p56.

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Computational intelligence (CI) refers to recreating human-like intelligence in a computing machine. It consists of a set of computing systems with the ability to learn and deal with new situations such that the systems are perceived to have some attributes of intelligence. It is efficient in solving realworld problems which require reasoning and decision-making. It produces more robust, simpler, and tractable solutions than the traditional techniques. This paper provides a brief introduction to computational intelligence.

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48

Jarrahi, Mohammad Hossein, Christoph Lutz, and Gemma Newlands. "Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation." Big Data & Society 9, no. 2 (July 2022): 205395172211428. http://dx.doi.org/10.1177/20539517221142824.

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There is little consensus on what artificial intelligence (AI) systems may or may not embrace. Although this may point to multiplicity of interpretations and backgrounds, a lack of conceptual clarity could thwart the development of common ground around the concept among researchers, practitioners and users of AI and pave the way for misinterpretation and abuse of the concept. This article argues that one of the effective ways to delineate the concept of AI is to compare and contrast it with human intelligence. In doing so, the article broaches the unique capabilities of humans and AI in relation to one another (human and machine tacit knowledge), as well as two types of AI systems: one that goes beyond human intelligence and one that is necessarily and inherently tied to it. It finally highlights how humans and AI can augment their capabilities and intelligence through synergistic human–AI interactions (i.e., human-augmented AI and augmented human intelligence), resulting in hybrid intelligence, and concludes with a future-looking research agenda.

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49

Zafrin, Mohamad Zafranudin Bin Mohamed, Subashini A/P Ganapathy, and Sajitha Smiley. "Early Childhood Education Smart Web Portal Using Intelligence Information Systems." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-ICAEIT2017 (November 30, 2018): 239–42. http://dx.doi.org/10.31142/ijtsrd19150.

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50

Rajamäki, Jyri, and Vasilis Katos. "Information Sharing Models for Early Warning Systems of Cybersecurity Intelligence." Information & Security: An International Journal 46, no. 2 (2020): 198–214. http://dx.doi.org/10.11610/isij.4614.

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