Journal articles: '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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de Màntaras, Ramon Lopéz. "ECAI'90: Summary Speech on Knowledge Engineering." AI Communications 4, no. 1 (1991): 26–29. http://dx.doi.org/10.3233/aic-1991-4105.

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ZHENG, ZIJIAN, and WEI LI. "A HYBRID KNOWLEDGE ENGINEERING DEVELOPMENT ENVIRONMENT (KEDE)." International Journal on Artificial Intelligence Tools 01, no. 04 (December 1992): 463–502. http://dx.doi.org/10.1142/s0218213092000028.

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This paper presents a brief overview of knowledge-based system building tools. Then a hybrid knowledge engineering development environment called KEDE is described as a powerful toolkit for large AI problems. It provides five kinds of knowledge representations: extended frames, semantic nets, procedural knowledge, object-oriented technique, and predicate logic. Correspondingly, it supports: procedure-oriented, data-oriented, object-oriented, and logic-oriented programming. KEDE gains a very powerful inheritance mechanism using frames. It further provides an automatic retrieval technique for processing implicit knowledge, a demon mechanism for firing functions, a message-sending mechanism for activating methods, and two inference engines for backward, forward, and even mixed reasoning. All these facilities are tightly integrated and formed an entirety. KEDE has been implemented in Common Lisp on Sun workstations.

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Sokolov, I. A. "Theory and practice in artificial intelligence." Вестник Российской академии наук 89, no. 4 (April 24, 2019): 365–70. http://dx.doi.org/10.31857/s0869-5873894365-370.

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Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engineering. Increasing interest in Artificial Intelligence in recent years is related to the development of promising new technologies based on specific methods like knowledge discovery (or machine learning), natural language processing, autonomous unmanned intelligent systems, and hybrid human-machine intelligence.

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Beenish Zahra. "ARTIFICIAL INTELLIGENCE AND CYC." Lahore Garrison University Research Journal of Computer Science and Information Technology 1, no. 4 (December 29, 2017): 29–36. http://dx.doi.org/10.54692/lgurjcsit.2017.010412.

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Since 1984, it is enormous work going on for the accomplishing of the project Cyc (‗Saik‘). This work is based on knowledge of ―Artificial Intelligence‖ which is developed by the Cycorpcompany and by Douglas Lenat at MCC. It‘s a Microelectronics and Computer Technology Corporation (MCC) part for so long. The dominant aim of Cycorp to develop this system is to just clarify anything in semantical determination rather than syntactically determination of words commands by the machine in which Cyc is installed to do some job. The other objective was in the building of Cyc is to codify, in a form which is usable by the machine, where knowledge‘s millions piece that composes common sense of a normal human or the common sense made in the human brain. Cyc presents a proprietary schema of knowledge representation that utilized first-order relationships. The relationships that presents between first-order logic (FOL) and first-order theory (FOT). After a long time, in1986, Cyc’s developer (Douglas Lenat) estimate that the total effort required to complete Cyc project would be 250,000 rules and 350 man-years. In 1994, Cyc Project was the reason behind creating independency into Cycorp, in Austin, Texas. As it is a common phrase that "Every tree is a plant" and "Plants die eventually" so that why by the mean of this some knowledge representing pieces which are in the database are like trees and plants like structures. The engine is known as an inference engine, able to draw the obvious results and answer the questions correctly on asking that whether trees die. The Knowledge Base (KB) system, which is included in Cyc, contains more than one million humans like assertions, rules or commonsense ideas. These ideas, rules, and human-defined assertions are describing or formatted in the language known as CycL. They are based on the predication of calculus and many otherhuman-based sciences, which has syntax similar to that of the language ―LISP‖. Though some extend the work on the Cyc project continues as a ―Knowledge Engineering‖, which represents some facts about the world, and implementing effective mechanisms which are derived after reaching the basic level conclusion on that knowledge. As Cyc include the firstorder logic and first order theory, which exist in some relationship; so it definitely uses and handle some other branches for human-interaction like mathematics, philosophy, and linguistics. However, increasingly, the other aim of Cycorp while developing Cyc is involvingan ability, which is given to the Cyc system that it can communicate with end users, by use of CycL, processing of natural language, and can assist with the knowledge formation process through the machine learning.

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Pinto, David, Beatriz Beltrán, and Vivek Singh. "Recent advances in language & knowledge engineering." Journal of Intelligent & Fuzzy Systems 42, no. 5 (March 31, 2022): 4299–305. http://dx.doi.org/10.3233/jifs-219220.

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Language & Knowledge Engineering is essential for the successfully development of artificial intelligence. The technologies proposed in international forums are meant to improve all areas of our daily life whether it is related to production industries, social communities, government, education, or something else. We consider very important to reveal the recent advances Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering because they are the base for the society of tomorrow. Thus, the aim of this special issue of Journal of Intelligent and Fuzzy Systems is to present a collection of papers that cover recent research results on the two wide topics: language and knowledge engineering. Even if the special issue is structured into these two general topics, we have covered specific themes such as the following ones: Natural Language Processing, Knowledge engineering, Pattern recognition, Artificial Intelligence and Language, Information Processing, Machine Learning Applied to Text Processing, Image and Text Classification, Multimodal data analysis, sentiment analysis, etc.

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Alhakeem, Mohammed Ridha H., and Dirja Nur Ilham. "Application of Artificial Intelligence in Mechanical Engineering." Brilliance: Research of Artificial Intelligence 2, no. 3 (September 13, 2022): 177–81. http://dx.doi.org/10.47709/brilliance.v2i3.1719.

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The use of artificial intelligence (AI) is becoming more prevalent across many industries. Examples include intelligently based control, intelligently based mechanical systems, pattern recognition-based systems, and knowledge processing. Method/Statistical Analysis: In this paper, an extensive review was conducted on the applications of ANN in intelligent mechanical engineering systems, including fault diagnosis in machines, mechanical structure analysis, and geometry modelling of mechanical structures, mechanical design, and its optimization. Findings: The adaptation of artificial neural networks (ANN), particularly in the field of mechanical engineering, is still in its early stages of development. This paper highlights the different ways artificial neural networks (ANNs) are used in intelligent-based systems, as well as the potential for reducing costs and time and obtaining more efficient systems for mechanical-based design and defect detection. Application/Improvements: This work will be improved in the future by adding more AI applications to the design of mechanically based systems.

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Ali, Awatef K., and MagdiSadek Mostafa Mahmoud. "Methodologies and Applications of Artificial Intelligence in Systems Engineering." International Journal of Robotics and Control Systems 2, no. 1 (March 4, 2022): 201–29. http://dx.doi.org/10.31763/ijrcs.v2i1.532.

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This paper presents an overview of the methodologies and applications of artificially intelligent systems (AIS) in different engineering disciplines with the objective of unifying the basic information and outlining the main features. These are knowledge-based systems (KBS), artificial neural networks (ANN), and fuzzy logic and systems (FLS). To illustrate the concepts, merits, and demerits, a typical application is given from each methodology. The relationship between ANN and FLS is emphasized. Two recent developments are finally presented: one is intelligent and autonomous systems (IAS) with particular emphasis on intelligent vehicle and highway systems, and the other is the very large scale integration (VLSI) systems design, verification, and testing.

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Chergui, Meriyem, and Aziza Chakir. "IT Governance Knowledge: From Repositories to Artificial Intelligence Solutions." Journal of Engineering Science and Technology Review 13, no. 5 (2020): 67–76. http://dx.doi.org/10.25103/jestr.135.09.

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Engineering sciences are nowadays interested in companies’ knowledge, either individual or collective one, in order to implement processing and management systems allowing better decisions as well as replacing humans with intelligent agents for technical and business reasons. Knowledge management (KM) is an important pillar to achieve organization best performance by deploying technics and know-how of brilliant and competent profile. It crosses several components namely: strategy processes, information systems and decision systems. IT Governance is the knowledge allowing the strategic alignment of IT with business so that the maximum value of the company is achieved by the development and effective control of information, responsibility, performance and risk management. This knowledge is mainly capitalized in good practice guidelines written by experts in the field. They make them available to companies and auditors for certification or performance improvement. However, the use of this knowledge in the right way is both expensive and complicated. This article addresses the issues related to the design and implementation of an intelligent knowledge management system for IT governance within the company normative and technical expectations.

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Barash, Guy, Mauricio Castillo-Effen, Niyati Chhaya, Peter Clark, Huáscar Espinoza, Eitan Farchi, Christopher Geib, et al. "Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence." AI Magazine 40, no. 3 (September 30, 2019): 67–78. http://dx.doi.org/10.1609/aimag.v40i3.4981.

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The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

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Bolger, Fergus. "Cognitive expertise research and knowledge engineering." Knowledge Engineering Review 10, no. 1 (March 1995): 3–19. http://dx.doi.org/10.1017/s0269888900007232.

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AbstractThis paper is a review of research into cognitive expertise. The review is organized in terms of a simple model of the knowledge and cognitive processes that might be expected to be enhanced in experts relative to non-experts. This focus on cognitive competence underlying expert performance permits the identification of skills and knowledge that we might wish to capture and model in expert systems. The competence perspective also indicates areas of weakness in human experts. In these areas, we might wish to support or replace the expert with, for example, a normative system rather than attempting to model his or her knowledge.

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Geyer, Philipp, Christian Koch, and Pieter Pauwels. "Fusing data, engineering knowledge and artificial intelligence for the built environment." Advanced Engineering Informatics 48 (April 2021): 101242. http://dx.doi.org/10.1016/j.aei.2020.101242.

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Fox, John, Matthew South, Omar Khan, Catriona Kennedy, Peter Ashby, and John Bechtel. "OpenClinical.net: Artificial intelligence and knowledge engineering at the point of care." BMJ Health & Care Informatics 27, no. 2 (July 2020): e100141. http://dx.doi.org/10.1136/bmjhci-2020-100141.

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ObjectiveOpenClinical.net is a way of disseminating clinical guidelines to improve quality of care whose distinctive feature is to combine the benefits of clinical guidelines and other human-readable material with the power of artificial intelligence to give patient-specific recommendations. A key objective is to empower healthcare professionals to author, share, critique, trial and revise these ‘executable’ models of best practice.DesignOpenClinical.net Alpha (www.openclinical.net) is an operational publishing platform that uses a class of artificial intelligence techniques called knowledge engineering to capture human expertise in decision-making, care planning and other cognitive skills in an intuitive but formal language called PROforma.3 PROforma models can be executed by a computer to yield patient-specific recommendations, explain the reasons and provide supporting evidence on demand.ResultsPROforma has been validated in a wide range of applications in diverse clinical settings and specialties, with trials published in high impact peer-reviewed journals. Trials have included patient workup and risk assessment; decision support (eg, diagnosis, test and treatment selection, prescribing); adaptive care pathways and care planning. The OpenClinical software platform presently supports authoring, testing, sharing and maintenance. OpenClinical’s open-access, open-source repository Repertoire currently carries approximately 50+ diverse examples (https://openclinical.net/index.php?id=69).ConclusionOpenClinical.net is a showcase for a PROforma-based approach to improving care quality, safety, efficiency and better patient experience in many kinds of routine clinical practice. This human-centred approach to artificial intelligence will help to ensure that it is developed and used responsibly and in ways that are consistent with professional priorities and public expectations.

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Choi, Ben. "Knowledge Engineering the Web." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 68–76. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1016.

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This paper focuses on the largest source of human knowledge: The Web. It presents the state of the art and patented technologies on search engine, automatic organization of webpages, and knowledge-based automatic webpage summarization. For the patented search engine technology, it describes new methods to present search results to the users and through browsers to allow the users to customize and organize webpages. For the patented classification technology, it describes new methods to automatically organize webpages into categories. For the knowledge-based summarization technology, it presents new technics for computers to "read" webpages and then to "write" a summary by creating new sentences to describe the contents of the webpages. These search engine, classification, and summarization technologies build a strong framework for knowledge engineering the Web.

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Wang, Bin, Jian Jun Chen, and Jie Tao. "Overview of Intelligent Design on Mold." Advanced Materials Research 459 (January 2012): 394–97. http://dx.doi.org/10.4028/www.scientific.net/amr.459.394.

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Applying Artificial Intelligence technology on mold design can help realize the design automation of mold. Because knowledge-based engineering is an effective intelligent design method, the paper systematically introduced the application and development of knowledge representation, knowledge reasoning, knowledge acquisition and other key technologies of knowledge engineering technology used in mold design field. Finally, the development tendency of mold design based on artificial intelligence was analyzed in detail

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LAI, LIEN F., CHAO-CHIN WU, NIEN-LIN HSUEH, LIANG-TSUNG HUANG, and SHIOW-FEN HWANG. "AN ARTIFICIAL INTELLIGENCE APPROACH TO COURSE TIMETABLING." International Journal on Artificial Intelligence Tools 17, no. 01 (February 2008): 223–40. http://dx.doi.org/10.1142/s0218213008003868.

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Course Timetabling is a complex problem that cannot be dealt with by using only a few general principles. The various actors (the administrator, the chairman, the instructor and the student) have their own objectives, and these objectives usually conflict. The complexity of the relationships among time slots, classes, classrooms, and instructors makes it difficult to achieve a feasible solution. In this article, we propose an artificial intelligence approach that integrates expert systems and constraint programming to implement a course timetabling system. Expert systems are utilized to incorporate knowledge into the timetabling system and to provide a reasoning capability for knowledge deduction. Separating out the knowledge base, the facts, and the inference engine in expert systems provides greater flexibility in supporting changes. The constraint hierarchy and the constraint network are utilized to capture hard and soft constraints and to reason about constraints by using constraint satisfaction and relaxation techniques. In addition, object-oriented software engineering is applied to improve the development and maintenance of the course timetabling system. A course timetabling system in the Department of Computer Science and Information Engineering at the National Changhua University of Education (NCUE) is used as an illustrative example of the proposed approach.

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Levitt, Raymond E., and John C. Kunz. "Using artificial intelligence techniques to support project management." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 1, no. 1 (February 1987): 3–24. http://dx.doi.org/10.1017/s0890060400000111.

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AbstractThis paper develops a philosophy for the use of Artificial Intelligence (AI) techniques as aids in engineering project management.First, we propose that traditional domain-independent, ‘means–and’ planners, may be valuable aids for planning detailed subtasks on projects, but that domain-specific planning tools are needed for work package or executive level project planning. Next, we propose that hybrid computer systems, using knowledge processing techniques in conjunction with procedural techniques such as decision analysis and network-based scheduling, can provide valuable new kinds of decision support for project objective-setting and project control, respectively. Finally we suggest that knowledge-based interactive graphics, developed for providing graphical explanations and user control in advanced knowledge processing environments, can provide powerful new kinds of decision support for project management.The first claim is supported by a review and analysis of previous work in the area of automated AI planning techniques. Our experience with PLATFORM I, II and III, a series of prototype AI-leveraged project management systems built using the IntelliCorp Knowledge Engineering Environment (KEE™), provides the justification for the latter two claims.

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Shah, Yamini D., Shailvi M. Soni, and Manish P. Patel. "Artificial intelligence in healthcare." Indian Journal of Pharmacy and Pharmacology 8, no. 2 (June 15, 2021): 102–15. http://dx.doi.org/10.18231/j.ijpp.2021.018.

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Artificial Intelligence (AI) is described as a field of science and engineering that is concerned with the artificial appreciation of what is generally referred to as prudent behavior and the formation of fascinations that demonstrate such conduct. AI is an expansive concept that encloses a series of advances (a considerable lot of which have been being worked on for quite a few years) that are expected to use human-like insight to handle the problems. Right now in combination with enhanced AI developments like extreme or significantly more engaged, we are experiencing a renewed enthusiasm for AI, energized by a tremendous increase in computing capacity and a significantly greater increase in knowledge. AI, along with machine learning, can be used in computer vision. More advantages in the field of engineering as well as in medicine can be accomplished based on these future scenarios worldwide. Healthcare is seen as the next domain that is said to be altered by the use of the concept of artificial intelligence. The AI process is used for critical diseases such as cancer, neurology, cardiology and diabetes. The review includes the ongoing flow status of medical services for AI applications. A few progressive explorations of AI applications in medicinal services that provide a perspective on future where human interactions are gradually brought together by social insurance conveyance. Likewise, this review will discuss how AI and machine learning can save the life of someone. It is also a guide for healthcare professionals to see how, when, and where AI can be more efficient and have the desired outcomes.

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de Mántaras, Ramón López, Ulises Cortés, Jaume Manero, and Enric Plaza. "Knowledge engineering for a document retrieval system." Fuzzy Sets and Systems 38, no. 2 (November 1990): 223–40. http://dx.doi.org/10.1016/0165-0114(90)90151-u.

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Graham, Ian. "Book review: Fuzzy logic in knowledge engineering." Fuzzy Sets and Systems 24, no. 3 (December 1987): 392–93. http://dx.doi.org/10.1016/0165-0114(87)90038-8.

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RAMAMOORTHY, C. V., LUIS MIGUEL, and YOUNG-CHUL SHIM. "ON ISSUES IN SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE." International Journal of Software Engineering and Knowledge Engineering 01, no. 01 (March 1991): 9–20. http://dx.doi.org/10.1142/s0218194091000044.

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Software engineering (SE) needs to make substantial breakthroughs in many different areas to allow order of magnitude improvements in software development times, software quality, and system cost. Artificial intelligence (AI) is uniquely positioned to help the SE research community in many of these areas, and we examine issues in AI for SE research. Given the fuzzy definition of AI, we provide a list of AI techniques to identify how much AI there is in specific AI for SE research. We recommend using the divide and conquer approach for SE automation and provide criteria for dividing the SE problems. We provide a vision of the future CASE environment, a knowledge and database management system at the center in a client-server architecture, and argue that it constitutes an ideal test-bed for research in AI for SE. We recommend an AI for SE research approach that includes dividing the problem up, using protocol analysis, implementing on a realistic CASE environment, and evaluating in industrial settings. We give criteria to evaluate applications of AI to SE including generality, scalability, and combinability. We conclude that AI will help SE to make slow and steady progress, but that it constitutes no silver bullet.

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Sicilia, Miguel-Angel, Elena García-Barriocanal, Salvador Sánchez-Alonso, and Daniel Rodríguez-García. "Ontologies of engineering knowledge: general structure and the case of Software Engineering." Knowledge Engineering Review 24, no. 3 (September 2009): 309–26. http://dx.doi.org/10.1017/s0269888909990087.

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AbstractEngineering knowledge is a specific kind of knowledge that is oriented to the production of particular classes of artifacts, is typically related to disciplined design methods, and takes place in tool-intensive contexts. As a consequence, representing engineering knowledge requires the elaboration of complex models that combine functional and structural representations of the resulting artifacts with process and methodological knowledge. The different categories used in the engineering domain vary in their status and in the way they should be manipulated when building applications that support engineering processes. These categories include artifacts, activities, methods and models. This paper surveys existing models of engineering knowledge and discusses an upper ontology that abstracts the categories that crosscut different engineering domains. Such an upper model can be reused for particular engineering disciplines. The process of creating such elaborations is reported on the particular case study of Software Engineering as a concrete application example.

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Paul, Norbert. "Deep models for medical knowledge engineering." Artificial Intelligence in Medicine 5, no. 6 (December 1993): 525–26. http://dx.doi.org/10.1016/0933-3657(93)90042-2.

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Lucas, Peter J. F. "Logic engineering in medicine." Knowledge Engineering Review 10, no. 2 (June 1995): 153–79. http://dx.doi.org/10.1017/s0269888900008134.

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AbstractThe safety-critical nature of the application of knowledge-based systems to the field of medicine requires the adoption of reliable engineering principles with a solid foundation for their construction. Logical languages with their inherent, precise notions of consistency, soundness and completeness provide such a foundation, thus promoting scrupulous engineering of medical knowledge. Moreover, logic techniques provide a powerful means for getting insight into the structure and meaning of medical knowledge used in medical problem solving. Unfortunately, logic is currently only used on a small scale for building practical medical knowledge-based systems. In this paper, the various approaches proposed in the literature are reviewed, and related to the various types of knowledge and problem solving employed in the medical field. The appropriateness of logic for building medical knowledge-based expert systems is further motivated.

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Sokol, Robert J., and Lawrence Chik. "A prototype system for perinatal knowledge engineering using an artificial intelligence tool." Journal of Perinatal Medicine 16, no. 4 (January 1988): 273–81. http://dx.doi.org/10.1515/jpme.1988.16.4.273.

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Kourtz, Peter. "Artificial intelligence: a new tool for forest management." Canadian Journal of Forest Research 20, no. 4 (April 1, 1990): 428–37. http://dx.doi.org/10.1139/x90-060.

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Articicial intelligence is a new science that deals with the representation, automatic acquisition, and use of knowledge. Artificial intelligence programs attempt to emulate human thought processes such as deduction, inference, language, and visual recognition. The goal of artificial intelligence is to make computers more useful for reasoning, planning, acting, and communicating with humans. Development of artificial intelligence applications involves the integration of advanced computer science, psychology, and sometimes robotics. Of the subfields that artificial intelligence can be broken into, the one of most immediate interest to forest management is expert systems. Expert systems involve encoding knowledge usually derived from an expert in a narrow subject area and using this knowledge to mimic his decision making. The knowledge is represented usually in the form of facts and rules, involving symbols such as English words. At the core of these systems is a mechanism that automatically searches for and pieces together the facts and rules necessary to solve a specific problem. Small expert systems can be developed on common microcomputers using existing low-cost commercial expert shells. Shells are general expert systems empty of knowledge. The user merely defines the solution structure and adds the desired knowledge. Larger systems usually require integration with existing forestry data bases and models. Their development requires either the relatively expensive expert system development tool kits or the use of one of the artificial intelligence development languages such as lisp or PROLOG. Large systems are expensive to develop, require a high degree of skill in knowledge engineering and computer science, and can require years of testing and modification before they become operational. Expert systems have a major role in all aspects of Canadian forestry. They can be used in conjunction with conventional process models to add currently lacking expert knowledge or as pure knowledge-based systems to solve problems never before tackled. They can preserve and accumulate forestry knowledge by encoding it. Expert systems allow us to package our forestry knowlege into a transportable and saleable product. They are a means to ensure consistent application of policies and operational procedures. There is a sense of urgency associated with the integration of artificial intelligence tools into Canadian forestry. Canada must awaken to the potential of this technology. Such systems are essential to improve industrial efficiency. A possible spin-off will be a resource knowledge business that can market our forestry knowledge worldwide. If we act decisively, we can easily compete with other countries such as Japan to fill this niche. A consortium of resource companies, provincial resource agencies, universities, and federal government laboratories is required to advance this goal.

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Fensel, Dieter. "Formal specification languages in knowledge and software engineering." Knowledge Engineering Review 10, no. 4 (December 1995): 361–404. http://dx.doi.org/10.1017/s0269888900007566.

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AbstractDuring the last few years, a number of formal specification languages for knowledge-based systems (KBS) have been developed. Characteristics of such systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Languages for KBS have to cover both these aspects. They have to provide a means to specify a complex and large amount of knowledge and they have to provide a means to specify the dynamic reasoning behaviour of a KBS. Nevertheless, KBS are just a specific type of software system. Therefore, it seems quite natural to compare formal languages for specifying KBS with formal languages which were developed by the software community for specifying software systems. That is the subject of this paper.

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Breuker, Joost, André Valente, and Radboud Winkels. "Legal Ontologies in Knowledge Engineering and Information Management." Artificial Intelligence and Law 12, no. 4 (December 2004): 241–77. http://dx.doi.org/10.1007/s10506-006-0002-1.

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Yue, Hongwei, Hanhui Lin, Yingying Jin, Hui Zhang, and Ken Cai. "Opening Knowledge Graph Model Building of Artificial Intelligence Curriculum." International Journal of Emerging Technologies in Learning (iJET) 17, no. 14 (July 26, 2022): 64–77. http://dx.doi.org/10.3991/ijet.v17i14.32613.

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The knowledge points setting of artificial intelligence curriculum has shortcomings in connection between theory and practices. To overcome the problem, this study designs an open knowledge point design model based on knowledge graph. Fist, to promote the construction of the knowledge graph (KG) of curriculums, associated teaching research was analyzed visually. Then the order and hierarchical structure of the knowledge points were defined, and the ontology structure of curriculum knowledge and the relationship between knowledge points and posts were designed as well. Moreover, an overall logic structure for the construction of the open KG of curriculums was proposed. Results demonstrated that high attention should be paid to the construction and concern of teaching teams for artificial intelligence algorithms and the KG of curriculum construction. Additionally, the opening model can strengthen the openness of the KG of curriculums to reinforce the close connections between classroom knowledge and practices. Research conclusions are conducive to understand the existing problems in the KG of curriculums and provide beneficial references to the integration of information technology and education.

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Lepape, Brice. "Achievements and Results of the Knowledge Engineering Area in ESPRIT I." AI Communications 3, no. 2 (1990): 73–79. http://dx.doi.org/10.3233/aic-1990-3205.

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Chrpa, Lukás, Thomas L. McCluskey, Mauro Vallati, and Tiago Vaquero. "The Fifth International Competition on Knowledge Engineering for Planning and Scheduling: Summary and Trends." AI Magazine 38, no. 1 (March 31, 2017): 104–6. http://dx.doi.org/10.1609/aimag.v38i1.2719.

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We review the 2016 International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS), the fifth in a series of competitions started in 2005. ICKEPS series focuses on promoting the importance of knowledge engineering methods and tools for automated Planning and Scheduling systems.

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Aleksander, Igor. "Artificial intelligence for production engineering: a historical approach." Robotica 5, no. 2 (April 1987): 99–110. http://dx.doi.org/10.1017/s026357470001506x.

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SUMMARYThis paper describes the principles of the advanced programming techniques often dubbed Artificial Intelligence involved in decision making as may be of some value in matters related to production engineering. Automated decision making in the context of production can adopt many aspects. At the most obvious level, a robot may have to plan a sequence of actions on the basis of signals obtained from changing conditions in its environment. These signals may, indeed, be quite complex, for example the input of visual information from a television camera.At another level, automated planning may be required to schedule the entire work cycle of a plant that includes many robots as well as other types of automated machinery. The often-quoted dark factory is an example of this, where not only some of the operations (such as welding) are done by robots, but also the transport of part-completed assemblies is automatically scheduled as a set of actions for autonomic transporters and cranes. It is common practice for this activity to be preprogrammed to the greatest detail. Automated decision making is aimed at adding flexibility to the process so that it can absolve the system designer from having to forsee every eventuality at the design stage.Frequent reference is made in this context to artificial intelligence (AI), knowledge-based and expert systems. Although these topics are more readily associated with computer science, it is the automated factory, in general, and the robot, in particular, that will benefit from success in these fields. In this part of the paper we try to sharpen up this perspective, while in part II we aim to discuss the history of artificial intelligence in this context. In part III we discuss the industrial prospects for the field.

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SMITH, F. J., and M. V. KRISHNAMURTHY. "QPS — A TOOL FOR QUANTITATIVE PROBLEM SOLVING IN SCIENCE AND ENGINEERING." International Journal on Artificial Intelligence Tools 05, no. 03 (September 1996): 313–22. http://dx.doi.org/10.1142/s0218213096000213.

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An AI tool called the Quantitative Problem Solver (QPS) has been developed for building knowledge based systems which can solve quantitative problems in science and engineering. QPS can store and manipulate quantitative knowledge comprising numerical data and scientific laws represented by formulas. The human interface is based on the symbols commonly used by scientists and engineers. All knowledge is represented as objects and classes in an object-oriented knowledge base. QPS employs the familiar Problem Decomposition strategy for selecting the correct sequence of equations needed for solving problems and it has been tested by the building of knowledge based systems to solve several simple problems in Engineering and Physics.

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Hao, Jia, Lei Zhao, Jelena Milisavljevic-Syed, and Zhenjun Ming. "Integrating and navigating engineering design decision-related knowledge using decision knowledge graph." Advanced Engineering Informatics 50 (October 2021): 101366. http://dx.doi.org/10.1016/j.aei.2021.101366.

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Silverman, Barry G., and R. Gregory Wenig. "Engineering experts critics for cooperative systems." Knowledge Engineering Review 8, no. 4 (December 1993): 309–28. http://dx.doi.org/10.1017/s0269888900000321.

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AbstractKnowledge collection systems often assume they are cooperating with an unbiased expert. They have few functions for checking and fixing the realism of the expertise transferred to the knowledge base, plan, document or other product of the interaction. The same problem arises when human knowledge engineers interview experts. The knowledge engineer may suffer from the same biases as the domain expert. Such biases remain in the knowledge base and cause difficulties for years to come.To prevent such difficulties, this paper introduces the reader to “critic engineering”, a methodology that is useful when it is necessary to doubt, trap and repair expert judgment during a knowledge collection process. With the use of this method, the human expert and knowledge-based critic form a cooperative system. Neither agent alone can complete the task as well as the two together.The methodology suggested here offers a number of extensions to traditional knowledge engineering techniques. Traditional knowledge engineering often answers the questions delineated in generic task (GT) theory, yet GT theory fails to provide four additional sets of questions that one must answer to engineer a knowledge base, plan, design or diagnosis when the expert is prone to error. This extended methodology is called “critic engineering”.

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Lehner, Paul E., and Leonard Adelman. "Behavioural decision theory and it's implication for knowledge engineering." Knowledge Engineering Review 5, no. 1 (March 1990): 5–14. http://dx.doi.org/10.1017/s0269888900005208.

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AbstractThis paper explores the implications of research results in behavioural decision theory on knowledge engineering. Behavioural decision theory, with its performance (versus process) orientation, can tell us a great deal about the validity of human expert knowledge, and when it should be modelled. A brief history of behavioural decision theory is provided. Implications for knowledge elicitation and representation are discussed. An approach to knowledge engineering is proposed that takes into account these implications.

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GERO, JOHN S. "AI EDAM at 20: Artificial intelligence in designing." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 17–18. http://dx.doi.org/10.1017/s0890060407070084.

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From its inception the journal Artificial Intelligence for Engineering Design, Analysis and Manufacturing recognized that designing is the precursor to analysis and manufacturing by placing it at the front of the list of areas it covers. Designing distinguishes itself from other aspects of engineering by its goal of changing the world within which it operates: designers are change agents. This characteristic makes designing a difficult task even for humans let alone for machines, because most of our knowledge and the means to acquire it assume that the world is given to us and what we need to do is characterize it. The Journal provided one of two continuing publication outlets for artificial intelligence (AI) in engineering at the time. This created the opportunity to have a focal point for the publication of archival research in the area, research that had previously appeared in disparate locations. It also offered the potential to develop a coherent research area where future research could build on previously published research.

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Singh, Nitu, Sunny Malik, Anvita Gupta, and Kinshuk Raj Srivastava. "Revolutionizing enzyme engineering through artificial intelligence and machine learning." Emerging Topics in Life Sciences 5, no. 1 (April 9, 2021): 113–25. http://dx.doi.org/10.1042/etls20200257.

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The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

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Hägglund, Sture. "The Linköping approach to technology transfer in knowledge engineering." Knowledge Engineering Review 2, no. 3 (September 1987): 153–58. http://dx.doi.org/10.1017/s0269888900000898.

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AbstractA technology transfer programme, where people from industry have been educated and trained in knowledge engineering on a project basis, has been in operation since 1984 in the Computer and Information Science Department at Linköping University. This review presents the background for the programme, its organization, examples of training projects, educational activities and plans for the future development.

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Ward, R. D., and D. Sleeman. "Learning to use the S.1 knowledge engineering tool." Knowledge Engineering Review 2, no. 4 (December 1987): 265–76. http://dx.doi.org/10.1017/s026988890000415x.

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AbstractIt is often claimed that it is easy to write expert systems. This claim was examined by monitoring experienced programmers learning to use the S.I knowledge engineering tool. Their achievements and difficulties were examined using a framework that has emerged from previous research into novices learning to use standard programming languages. Even though the experienced programmers all had several years' experience of programming in more than one standard language, there were similarities between their difficulties in learning to use S.I and the difficulties of complete novices learning to program in standard languages.The experienced programmers were however able to overcome their initial difficulties fairly quickly, but it is argued that complete novices would not find it so easy to do so. Also the experienced programmers did take time to develop a repertoire of schemeta for representing different kinds of factual, judgemental and procedural knowledge. It was concluded that in S.1, as with other programming languages and softwares tools, it is easy to learn how to do simple things, but difficult, even for experienced programmers to learn how to do more complex things.No criticism of S.1 is implied. S.1 was found to be a suitable vehicle for introducing non-trivial knowledge engineering concepts, and we believe that similar difficulties would occur in learning to use other knowledge engineering tools.

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Fernández, Susana, Daniel Borrajo, Raquel Fuentetaja, Juan D. Arias, and Manuela Veloso. "PLTOOL: A knowledge engineering tool for planning and learning." Knowledge Engineering Review 22, no. 2 (June 2007): 153–84. http://dx.doi.org/10.1017/s0269888907001075.

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AbsractArtificial intelligence (AI) planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will transform some initial state into some desirable end-state. There is a long tradition of work in AI for developing planners that make use of heuristics that are shown to improve their performance in many real world and artificial domains. The developers of planners have chosen between two extremes when defining those heuristics. The domain-independent planners use domain-independent heuristics, which exploit information only from the ‘syntactic’ structure of the problem space and of the search tree. Therefore, they do not need any ‘semantic’ information from a given domain in order to guide the search. From a knowledge engineering (KE) perspective, the planners that use this type of heuristics have the advantage that the users of this technology need only focus on defining the domain theory and not on defining how to make the planner efficient (how to obtain ‘good’ solutions with the minimal computational resources). However, the domain-dependent planners require users to manually represent knowledge not only about the domain theory, but also about how to make the planner efficient. This approach has the advantage of using either better domain-theory formulations or using domain knowledge for defining the heuristics, thus potentially making them more efficient. However, the efficiency of these domain-dependent planners strongly relies on the KE and planning expertise of the user. When the user is an expert on these two types of knowledge, domain-dependent planners clearly outperform domain-independent planners in terms of number of solved problems and quality of solutions. Machine-learning (ML) techniques applied to solve the planning problems have focused on providing middle-ground solutions as compared to the aforementioned two extremes. Here, the user first defines a domain theory, and then executes the ML techniques that automatically modify or generate new knowledge with respect to both the domain theory and the heuristics. In this paper, we present our work on building a tool, PLTOOL (planning and learning tool), to help users interact with a set of ML techniques and planners. The goal is to provide a KE framework for mixed-initiative generation of efficient and good planning knowledge.

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

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