Relevant bibliographies by topics / Knowledge engineering and artificial intelligence

Academic literature on the topic 'Knowledge engineering and artificial intelligence'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Knowledge engineering and artificial intelligence"

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Forsythe, Diana E. "Engineering Knowledge: The Construction of Knowledge in Artificial Intelligence." Social Studies of Science 23, no. 3 (August 1993): 445–77. http://dx.doi.org/10.1177/0306312793023003002.

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Gale, William A. "Statistical applications of artificial intelligence and knowledge engineering." Knowledge Engineering Review 2, no. 4 (December 1987): 227–47. http://dx.doi.org/10.1017/s0269888900004136.

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AbstractKnowledge engineering (KE) has now provided some effective techniques for formalization of knowledge about goals and actions. These techniques could open new areas of research to statisticians. Experimental systems designed to assist users of statistics have been constructed in experiment design, data analysis, technique application, and technique selection. Knowledge formalization has also been used in experimental programs to assist statisticians in doing data analysis and in building consultation systems. The best-explored application of KE techniques is building consultation systems. It is now a promising area for development. Analogies with successful artificial intelligence AI applications in other fields suggest other statistical applications worth exploring. Opening new areas to research and providing new tools to users would make considerable changes in the use and production of statistical techniques. However, applying currently available KE techniques will lead to more work for statisticians, not less.
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Lu, Pengzhen, Shengyong Chen, and Yujun Zheng. "Artificial Intelligence in Civil Engineering." Mathematical Problems in Engineering 2012 (2012): 1–22. http://dx.doi.org/10.1155/2012/145974.

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Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.
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Johnson, W. Lewis. "Knowledge-Based Software Engineering." Knowledge Engineering Review 7, no. 4 (December 1992): 367–69. http://dx.doi.org/10.1017/s0269888900006482.

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The 7th Annual Knowledge-Based Software Engineering Conference was held at the McLean Hilton at Tysons Comer, in McLean, Virginia, on Sept. 20–23, 1992. This conference was sponsored by Rome Laboratory and held in cooperation with the IEEE Computer Society, ACM SIGART and SIGSOFT, and the American Association for Artificial Intelligence (AAAI).
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OSSOWSKI, SASCHA, and ANDREA OMICINI. "Coordination knowledge engineering." Knowledge Engineering Review 17, no. 4 (December 2002): 309–16. http://dx.doi.org/10.1017/s0269888903000596.

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By adopting a structured knowledge-level approach, coordination knowledge can be ascribed to groups (societies) of system components (agents) as a whole, rather than to individuals, in order to effectively rationalise complex patterns of interaction within intelligent (multi-agent) systems. Be it either explicitly represented at the symbol-level or hard-coded within specific coordination algorithms, coordination knowledge is instrumented by a wide and heterogeneous variety of coordination models, abstractions and technologies. Coordination knowledge engineering is then about eliciting, modelling and instrumenting coordination knowledge in a principled and effective manner.In this introductory article, we briefly review two well-known frameworks to conceptualise coordination, then we discuss different dimensions along which coordination models can be classified, and analyse their impact on the design of coordination mechanisms and their supporting coordination knowledge. Finally, we sketch our view on coordination knowledge engineering and introduce the different contributions to this special issue along this line.
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杨, 福义. "Knowledge Engineering Exploration in the Era of Artificial Intelligence." Artificial Intelligence and Robotics Research 10, no. 01 (2021): 9–28. http://dx.doi.org/10.12677/airr.2021.101002.

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Pan, Yunhe. "Multiple Knowledge Representation of Artificial Intelligence." Engineering 6, no. 3 (March 2020): 216–17. http://dx.doi.org/10.1016/j.eng.2019.12.011.

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Kitchen, A. "Knowledge based systems in artificial intelligence." Proceedings of the IEEE 73, no. 1 (1985): 171–72. http://dx.doi.org/10.1109/proc.1985.13127.

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Fox, John. "Methodologies for knowledge engineering." Knowledge Engineering Review 7, no. 2 (June 1992): 95–96. http://dx.doi.org/10.1017/s0269888900006214.

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Felfernig, Alexander, and Franz Wotawa. "Intelligent engineering techniques for knowledge bases." AI Communications 26, no. 1 (2013): 1–2. http://dx.doi.org/10.3233/aic-2012-0541.

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Dissertations / Theses on the topic "Knowledge engineering and artificial intelligence"

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Malmborn, Albin, and Linus Sjöberg. "Implementing Artificial intelligence." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20942.

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Den här uppsatsen har som syfte att undersöka huruvida det är möjligt att ta fram riktlinjer för vad privata verksamheter behöver ta i beaktande inför en planerad implementering av artificiell intelligens. Studien kommer belysa faktorer som hjälper företag att förstå vad som krävs inför en sådan omställning, men även de hinder som måste övervinnas för att lyckas. Studiens datainsamling har genomförts med två metoder, först en litteraturstudie sedan kvalitativa, semistrukturerade forskningsintervjuer. Dessa har sedan analyserats med vars en analysmetod som kompletterar varandra och därefter tolkats för att se mönster som kan besvara studiens frågeställning: Vad måste svenska organisationer inom den privata sektorn beakta för att lyckas implementera Artificiell intelligens i sin verksamhet? Resultatet har tagits fram genom att jämföra vetenskapliga texter och intervjuer, för att undersöka om den akademiska och praktiska synen skiljer sig åt. Studien resulterade i åtta faktorer som företag borde ta i beaktning inför en implementering av artificiell intelligens. Författarna hoppas att med den här studien kunna främja svensk utveckling inom artificiell intelligens och på så vis generera ett större nationellt mervärde och en starkare konkurrenskraft internationell.
The purpose of this paper is to investigate the possibilities to develop guidelines for businesses to take into account before an implementation of artificial intelligence. The study will highlight different factors that will help companies to understand what is required to make this kind of digital transition, it will also highlight the obstacles companies have to overcome in order to succeed. The data collection was conducted in two parts, first a literature study and then qualitative, semi-structured interviews. These were analyzed with their own analysis which supplement each other, and interpreted to identify patterns that could answer the study's main question: What must Swedish organizations in the private sector consider in order to successfully implement Artificial Intelligence in their operations?The result of the study has been produced by comparing scientific texts and interviews, to investigate whether the academic and practical views differ. The study resulted in eight factors that companies should consider before implementing artificial intelligence. The authors hope that the study will promote Swedish development in artificial intelligence and thus generate a greater national value and international competitiveness.
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Collis, Jaron Clements. "An application of artificial intelligence to quantitative problem solving in engineering." Thesis, Queen's University Belfast, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361311.

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Huang, Zan, Hsinchun Chen, Alan Yip, Gavin Ng, Fei Guo, Zhi-Kai Chen, and Mihail C. Roco. "Longitudinal patent analysis for nanoscale science and engineering: Country, institution and technology field." Kluwer, 2003. http://hdl.handle.net/10150/105834.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
Nanoscale science and engineering (NSE) and related areas have seen rapid growth in recent years. The speed and scope of development in the field have made it essential for researchers to be informed on the progress across different laboratories, companies, industries and countries. In this project, we experimented with several analysis and visualization techniques on NSE-related United States patent documents to support various knowledge tasks. This paper presents results on the basic analysis of nanotechnology patents between 1976 and 2002, content map analysis and citation network analysis. The data have been obtained on individual countries, institutions and technology fields. The top 10 countries with the largest number of nanotechnology patents are the United States, Japan, France, the United Kingdom, Taiwan, Korea, the Netherlands, Switzerland, Italy and Australia. The fastest growth in the last 5 years has been in chemical and pharmaceutical fields, followed by semiconductor devices. The results demonstrate potential of information-based discovery and visualization technologies to capture knowledge regarding nanotechnology performance, transfer of knowledge and trends of development through analyzing the patent documents.
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Farzanegan, Akbar. "Knowledge-based optimization of mineral grinding circuits." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0027/NQ50158.pdf.

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Ajit, Suraj. "Capture and maintenance of constraints in engineering design." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources. Restricted access until May 30, 2112. Online version available for University member only until May, 30 2014, 2009. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=25928.

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Hu, Jhyfang. "Towards a knowledge-based design support environment for design automation and performance evaluation." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184804.

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The increasing complexity of systems has made the design task extremely difficult without the help of an expert's knowledge. The major goal of this dissertation is to develop an intelligent software shell, termed the Knowledge-Based Design Support Environment (KBDSE), to facilitate multi-level system design and performance evaluation. KBDSE employs the technique, termed Knowledge Acquisition based on Representation (KAR), for acquiring design knowledge. With KAR, the acquired knowledge is automatically verified and transformed into a hierarchical, entity-based representation scheme, called the Frame and Rule Associated System Entity Structure (FRASES). To increase the efficiency of design reasoning, a Weight-Oriented FRASES Inference Engine (WOFIE) was developed. WOFIE supports different design methodologies (i.e., top-down, bottom-up, and hybrid) and derives all possible alternative design models parallelly. By appropriately setting up the priority of a specialization node, WOFIE is capable of emulating the design reasoning process conducted by a human expert. Design verification is accomplished by computer simulation. To facilitate performance analysis, experimental frames reflecting design objectives are automatically constructed. This automation allows the design model to be verified under various simulation circumstances without wasting labor in programming math-intensive models. Finally, the best design model is recommended by applying Multi-Criteria Decision Making (MCDM) methods on simulation results. Generally speaking, KBDSE offers designers of complex systems a mixed-level design and performance evaluation; knowledge-based design synthesis; lower cost and faster simulation; and multi-criteria design analysis. As with most expert systems, the goal of KBDSE is not to replace the human designers but to serve as an intelligent tool to increase design productivity.
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Burge, Janet E. "Software Engineering Using design RATionale." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050205-085625/.

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Dissertation (Ph.D.) -- Worcester Polytechnic Institute.
Keywords: software engineering; inference; knowledge representation; software maintenance; design rationale. Includes bibliographical references (p. 202-211).
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Tremblay, Luc 1962. "A dimensional analysis system for knowledge-aided design in electromagnetics." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23758.

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This thesis considers the dimensional analysis theory in engineering. A Knowledge-Aided Design Tool is presented which permits the solution of many aspects of Dimensional Analysis for electromagnetics. The KAD Tool was coded in the language Lisp with Allegro Common Lisp in a Microsoft-Windows environment on a PC with a 486 microprocessor. It represents 10 196 lines of code. The mathematical functions are supported by the mathematical libraries of the software MAPLE. A menu with nine choices corresponding to nine functionalities of Dimensional Analysis is offered to the user.
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Moafipoor, Shahram. "Intelligent Personal Navigator Supported by Knowledge-Based Systems for Estimating Dead Reckoning Navigation Parameters." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1262043297.

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Carbogim, Daniela Vasconcelos. "Dynamics in formal argumentation." Thesis, University of Edinburgh, 2000. http://hdl.handle.net/1842/591.

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In this thesis we are concerned with the role of formal argumentation in artificial intelligence, in particular in the field of knowledge engineering. The intuition behind argumentation is that one can reason with imperfect information by constructing and weighing up arguements intended to give support in favour or against alternative conclusions. In dynamic argumentation, such arguements may be revised and strengthened in order yo increase to decrease the acceptability of controversial positions. This thesis studies the theory, architecture, development and applications of formal arguementation systems from the procedural perspective of actually generating argumentation processes. First, the types of problems that can be tackled via the argumentation paradigm in knowledge engineering are characterised. Second, an abstract formal framework are built from an underlying set of axioms, represented here as executatble logic programs. Finally an architecture for dynamic arguementation systems is defined, and domain-specific applications are presented within different domaind, thus grounding problems with very distinctive characteristics into a similar source in argumentation. The methods and definitions desribed in this thesis have been assessed on various bases, including the reconstruction of informal arguements and of arguments captured by existing formalisms, the relation between our framework and these formalisms, and examples of dynamic argumentation applications in the safety-engineering and multi-agent domains.
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Books on the topic "Knowledge engineering and artificial intelligence"

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Wu, Yanwen. Software Engineering and Knowledge Engineering: Theory and Practice: Volume 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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NATO Advanced Research Workshop on Machine Intelligence and Knowledge Engineering for Robotic Applications (1986 Maratea, Italy). Machine intelligence and knowledge engineering for robotic applications. Berlin: Springer-Verlag, 1987.

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Mira, José, and José R. Álvarez, eds. Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b137296.

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Álvarez, José R. Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Berlin Heidelberg: Springer-Verlag., 2005.

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Janusz, Kacprzyk, and SpringerLink (Online service), eds. Rough – Granular Computing in Knowledge Discovery and Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.

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Sriram, Ram D. Intelligent Systems for Engineering: A Knowledge-based Approach. London: Springer London, 1997.

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Advances in knowledge-based and intelligent information and engineering systems. Amsterdam: IOS Press, 2012.

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Focus on information, intelligence, and knowledge. Hauppauge, N.Y: Nova Science Publishers, 2011.

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Guillet, Fabrice. Advances in Knowledge Discovery and Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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China) International Conference on Intelligent Systems Engineering (7th 2012 Beijing. Knowledge engineering and management: Proceedings of the Seventh International Conference on Intelligent Systems and Knowledge Engineering, Beijing, China, Dec 2012 (ISKE 2012). Edited by Sun Fuchun 1964-, Li Tianrui, and Li Hongbo. Berlin: Springer, 2014.

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Book chapters on the topic "Knowledge engineering and artificial intelligence"

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Retti, Johannes. "Knowledge Engineering und Expertensysteme." In Artificial Intelligence — Eine Einführung, 75–104. Wiesbaden: Vieweg+Teubner Verlag, 1986. http://dx.doi.org/10.1007/978-3-322-93997-5_5.

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Aussenac-Gilles, Nathalie, Jean Charlet, and Chantal Reynaud. "Knowledge Engineering." In A Guided Tour of Artificial Intelligence Research, 733–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-06164-7_23.

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Andersen, Tom, and Susanne C. Hartvig. "Teaching knowledge engineering: Experiences." In Artificial Intelligence in Structural Engineering, 424–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0030467.

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Groiss, Herbert, and Werner Staringer. "Knowledge Engineering mit KNOPF." In 3. Österreichische Artificial-Intelligence-Tagung, 64–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-46620-5_6.

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Lopata, Audrius, and Martas Ambraziunas. "MDA Compatible Knowledge– Based IS Engineering Approach." In Artificial Intelligence and Computational Intelligence, 230–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16530-6_28.

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Mřrík, Vladimír, and Tomáš Vlček. "Some aspects of knowledge engineering." In Advanced Topics in Artificial Intelligence, 316–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55681-8_43.

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Christiansson, Per. "Using knowledge nodes for knowledge discovery and collaboration." In Artificial Intelligence in Structural Engineering, 48–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0030442.

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Kim Roddis, W. M. "Knowledge-based assistants in collaborative engineering." In Artificial Intelligence in Structural Engineering, 320–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0030460.

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Gulyaeva, Karina A., and Irina L. Artemieva. "Ontology Models in Intelligent System Engineering: A Case of the Knowledge-Intensive Application Domain." In Artificial Intelligence, 127–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33274-7_8.

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Hofmann, Martin. "“Knowledge Engineering” und “Agenda”-Entwurf für Ein Fehlerdiagnosesystem." In Österreichische Artificial Intelligence-Tagung, 9–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-46552-9_2.

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Conference papers on the topic "Knowledge engineering and artificial intelligence"

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"SESSION: Artificial Intelligence and Knowledge Engineering." In 2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/is.2004.1344646.

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Martínez, María Vanina. "Knowledge Engineering for Intelligent Decision Support." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/736.

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Knowledge can be seen as the collection of skills and information an individual (or group) has acquired through experience, while intelligence as the ability to apply such knowledge. In many areas of Artificial Intelligence, we have been focusing for the last 40 years on the formalization and development of automated ways of finding and collecting data, as well as on the construction of models to represent that data adequately in a way that an automated system can make sense of it. However, in order to achieve real artificial intelligence we need to go beyond data and knowledge representation, and deeper into how such a system could, and would, use available knowledge in order to empower and enhance the capabilities of humans in making decisions in real-world applications. From my point of view, an AI should be able to combine automatically acquired data and knowledge together with specific domain expertise from the users that the tool is expected to help.
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Avdeenko, Tatiana V., Ekaterina S. Makarova, and Irina L. Klavsuts. "Artificial intelligence support of knowledge transformation in knowledge management systems." In 2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2016. http://dx.doi.org/10.1109/apeie.2016.7807053.

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Mattioli, Juliette, Claire Laudy, Pierre-Olivier Robic, and Hugo-Guillermo Chale-Gongora. "Body-of-Knowledge development by using Artificial Intelligence." In 2022 17th Annual System of Systems Engineering Conference (SOSE). IEEE, 2022. http://dx.doi.org/10.1109/sose55472.2022.9812677.

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Simari, Gerardo I. "From Data to Knowledge Engineering for Cybersecurity." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/896.

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Data present in a wide array of platforms that are part of today's information systems lies at the foundation of many decision making processes, as we have now come to depend on social media, videos, news, forums, chats, ads, maps, and many other data sources for our daily lives. In this article, we first discuss how such data sources are involved in threats to systems' integrity, and then how they can be leveraged along with knowledge-based tools to tackle a set of challenges in the cybersecurity domain. Finally, we present a brief discussion of our roadmap for research and development in the near future to address the set of ever-evolving cyber threats that our systems face every day.
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Nery, Marcelo, Rodrigo Santos, Wallas Santos, Vitor Lourenco, and Marcio Moreno. "Facing Digital Agriculture Challenges with Knowledge Engineering." In 2018 First International Conference on Artificial Intelligence for Industries (AI4I). IEEE, 2018. http://dx.doi.org/10.1109/ai4i.2018.8665708.

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Ongsulee, Pariwat. "Artificial intelligence, machine learning and deep learning." In 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2017. http://dx.doi.org/10.1109/ictke.2017.8259629.

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LEÓN, MAIKEL, RAFAEL BELLO, and KOEN VANHOOF. "CONSIDERING ARTIFICIAL INTELLIGENCE TECHNIQUES TO PERFORM ADAPTABLE KNOWLEDGE STRUCTURES." In Proceedings of the 4th International ISKE Conference on Intelligent Systems and Knowledge Engineering. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814295062_0014.

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Luís-Ferreira, Fernando, João Sarraipa, Jorge Calado, Joana Andrade, Daniel Rodrigues, and Ricardo Jardim Goncalves. "Artificial Intelligence Based Architecture to Support Dementia Patients." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10985.

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Abstract Artificial Intelligence is driving a revolution in the most diverse domains of computational services and user interaction. Data collected in large quantities is becoming useful for feeding intelligent systems that analyse, learn and provide insights and help decision support systems. Machine learning and the usage of algorithms are of most importance to extract features, reason over collected data so it becomes useful and preventive, exposing discoveries augmenting knowledge about systems and processes. Human driven applications, as those related with physiological assessment and user experience, are possible especially in the health domain and especially in supporting patients and the community. The work hereby described refers to different aspects where the Artificial Intelligence can help citizens and wraps a series devices and services that where developed and tested for the benefit of a special kind of citizens. The target population are those under some kind of Dementia, but the proposed solutions are also applicable to other elder citizens or even children that need to be assisted and prevented from risks.
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Sumari, Arwin Datumaya Wahyudi, and Adang Suwandi Ahmad. "Knowledge-growing system: The origin of the cognitive artificial intelligence." In 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2017. http://dx.doi.org/10.1109/iceei.2017.8312382.

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Reports on the topic "Knowledge engineering and artificial intelligence"

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Ruff, Grigory, and Tatyana Sidorina. THE DEVELOPMENT MODEL OF ENGINEERING CREATIVITY IN STUDENTS OF MILITARY INSTITUTIONS. Science and Innovation Center Publishing House, December 2020. http://dx.doi.org/10.12731/model_of_engineering_creativity.

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The troops of the national guard of the Russian Federation are equipped with modern models of weapons, special equipment, Informatization tools, engineering weapons that have artificial intelligence in their composition are being developed, " etc., which causes an increase in the requirements for the quality of professional training of future officers. The increasing complexity of military professional activities, the avalanche-like increase in information, the need to develop the ability to quickly and accurately make and implement well-known and own engineering solutions in an unpredictable military environment demonstrates that the most important tasks of modern higher education are not only providing graduates with a system of fundamental and special knowledge and skills, but also developing their professional independence, and this led to the concept of engineering and creative potential in the list of professionally important qualities of an officer. To expedite a special mechanism system compact intense clarity through cognitive visualization of the educational material, thickening of educational knowledge through encoding, consolidation and structuring Principle of cognitive visualization stems from the psychological laws in accordance with which the efficiency of absorption is increased if visibility in training does not only illustrative, but also cognitive function, which leads to active inclusion, along with the left and right hemispheres of the student in the process of assimilation of information, based on the use of logical and semantic modeling, which contributes to the development of engineering and creative potential.
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Diaz-Herrera, Jorge L. Artificial Intelligence (AI) and Ada: Integrating AI with Mainstream Software Engineering. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada286093.

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Emrich, M., A. Agarwal, B. Jairam, and N. Murthy. Potential Applications of Artificial Intelligence to the Field of Software Engineering. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada216909.

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Daniels, Matthew, Autumn Toney, Melissa Flagg, and Charles Yang. Machine Intelligence for Scientific Discovery and Engineering Invention. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/20200099.

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The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.
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Crawford, Ali, and Ido Wulkan. Federal Prize Competitions: Using Competitions to Promote Innovation in Artificial Intelligence. Center for Security and Emerging Technology, November 2021. http://dx.doi.org/10.51593/2021ca002.

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In science and technology, U.S. federal prize competitions are a way to promote innovation, advance knowledge, and solicit technological solutions to problems. In this report, the authors identify the unique advantages of such competitions over traditional R&D processes, and how these advantages might benefit artificial intelligence research.
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6

Shaneyfelt, Wendy, John T. Feddema, and Conrad D. James. A Surety Engineering Framework and Process to Address Ethical Legal and Social Issues for Artificial Intelligence. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1561812.

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Lohn, Andrew, and Micah Musser. AI and Compute: How Much Longer Can Computing Power Drive Artificial Intelligence Progress? Center for Security and Emerging Technology, January 2022. http://dx.doi.org/10.51593/2021ca009.

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Between 2012 and 2018, the amount of computing power used by record-breaking artificial intelligence models doubled every 3.4 months. Even with money pouring into the AI field, this trendline is unsustainable. Because of cost, hardware availability and engineering difficulties, the next decade of AI can't rely exclusively on applying more and more computing power to drive further progress.
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Voisin, Nathalie, Andrew Bennett, Yilin Fang, Grey Nearing, Bart Nijssen, and Yuhan Rao. A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769651.

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9

Hwang, Tim. Shaping the Terrain of AI Competition. Center for Security and Emerging Technology, June 2020. http://dx.doi.org/10.51593/20190029.

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How should democracies effectively compete against authoritarian regimes in the AI space? This report offers a “terrain strategy” for the United States to leverage the malleability of artificial intelligence to offset authoritarians' structural advantages in engineering and deploying AI.
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Langenkamp, Max, and Melissa Flagg. AI Hubs: Europe and CANZUK. Center for Security and Emerging Technology, April 2021. http://dx.doi.org/10.51593/20200061.

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U.S. policymakers need to understand the landscape of artificial intelligence talent and investment as AI becomes increasingly important to national and economic security. This knowledge is critical as leaders develop new alliances and work to curb China’s growing influence. As an initial effort, an earlier CSET report, “AI Hubs in the United States,” examined the domestic AI ecosystem by mapping where U.S. AI talent is produced, where it is concentrated, and where AI private equity funding goes. Given the global nature of the AI ecosystem and the importance of international talent flows, this paper looks for the centers of AI talent and investment in regions and countries that are key U.S. partners: Europe and the CANZUK countries (Canada, Australia, New Zealand, and the United Kingdom).
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