Relevant bibliographies by topics / Knowledge acquisition bottleneck

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Knowledge acquisition bottleneck'

Author: Grafiati
Published: 31 August 2024

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Journal articles on the topic "Knowledge acquisition bottleneck"

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Cullen, J., and A. Bryman. "The Knowledge Acquisition Bottleneck: Time for Reassessment?" Expert Systems 5, no. 3 (August 1988): 216–25. http://dx.doi.org/10.1111/j.1468-0394.1988.tb00065.x.

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Wagner, Christian. "Breaking the Knowledge Acquisition Bottleneck Through Conversational Knowledge Management." Information Resources Management Journal 19, no. 1 (January 2006): 70–83. http://dx.doi.org/10.4018/irmj.2006010104.

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Aussenac-Gilles, Nathalie, and Fabien Gandon. "From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition." International Journal of Human-Computer Studies 71, no. 2 (February 2013): 157–65. http://dx.doi.org/10.1016/j.ijhcs.2012.10.009.

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Hoppenbrouwers, Stijn, Bart Schotten, and Peter Lucas. "Towards Games for Knowledge Acquisition and Modeling." International Journal of Gaming and Computer-Mediated Simulations 2, no. 4 (October 2010): 48–66. http://dx.doi.org/10.4018/jgcms.2010100104.

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Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.
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Sauer, Christopher, Thilo Breitsprecher, Christof Küstner, Benjamin Schleich, and Sandro Wartzack. "SLASSY—An Assistance System for Performing Design for Manufacturing in Sheet-Bulk Metal Forming: Architecture and Self-Learning Aspects." AI 2, no. 3 (July 8, 2021): 307–29. http://dx.doi.org/10.3390/ai2030019.

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Substantial efforts have been made to integrate manufacturing- and design-relevant knowledge into product development processes. A common approach is to provide the relevant knowledge to the design engineers using a knowledge-based system (KBS) that, in turn, becomes the engineering assistance system. Keeping the knowledge up to date is a critical issue, making knowledge acquisition a bottleneck of developing and maintaining KBS. This article presents a robust metamodel optimization and performance estimation architecture for developing and maintaining a KBS useful for design-for-manufacturing from the context of sheet-bulk metal forming. It is shown that the presented KBS or engineering assistance system helps achieve performing design-for-manufacturing, integrating both design and manufacturing knowledge. Using the presented approach helps overcome the bottleneck of knowledge acquisition and knowledge update through its self-learning component based on data mining and knowledge discovery.
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Lin, Shun-Chieh, Chia-Wen Teng, and Shian-Shyong Tseng. "Capturing Evolutional Knowledge Using Time Interval Tracing." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 4 (April 20, 2007): 373–80. http://dx.doi.org/10.20965/jaciii.2007.p0373.

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Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.
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Zhao, Guo Zhen, and Wan Li Zuo. "Semi-Supervised Word Sense Disambiguation via Context Weighting." Advanced Materials Research 1049-1050 (October 2014): 1327–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1327.

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Word sense disambiguation as a central research topic in natural language processing can promote the development of many applications such as information retrieval, speech synthesis, machine translation, summarization and question answering. Previous approaches can be grouped into three categories: supervised, unsupervised and knowledge-based. The accuracy of supervised methods is the highest, but they suffer from knowledge acquisition bottleneck. Unsupervised method can avoid knowledge acquisition bottleneck, but its effect is not satisfactory. With the built-up of large-scale knowledge, knowledge-based approach has attracted more and more attention. This paper introduces a new context weighting method, and based on which proposes a novel semi-supervised approach for word sense disambiguation. The significant contribution of our method is that thesaurus and machine learning techniques are integrated in word sense disambiguation. Compared with the state of the art on the test data of the English all words disambiguation task in Sensaval-3, our method yields obvious improvements over existing methods in nouns, adjectives and verbs disambiguation.
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Ren, Yong Chang, Tao Xing, and Ping Zhu. "An Attribute Reduction Algorithms of Expert System Knowledge Acquisition." Applied Mechanics and Materials 48-49 (February 2011): 187–91. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.187.

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Knowledge acquisition is the bottleneck of construction expert system, to provide an accurate inference of knowledge is the key decision-making plan. This article use the rough sets theory, through the rough sets reduction eliminate redundant condition attribute, to achieve the streamlining of the knowledge library. First study the knowledge acquisition, in exposition knowledge hierarchical structure foundation, has given the conceptualization, formal, the knowledge library refinement and so on three knowledge acquisition; and then study attributes reduction algorithms, in the research sets difference and the attribute importance, the reduction algorithms inferential reasoning process's foundation, has given the attribute reduction algorithms six steps. Finally, according to the attributes reduction algorithms and the steps, to estimate the expert system to the function analytic method construction software cost, the composition technology complexity factor of 14 factors reduction. The results showed that the use of rough sets theory to reduce the attributes, can simplify the structure of complex systems, and can effectively maintain the knowledge library structure and performance.
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Lv, Zhan Min, Wei Yan, Jun Liang He, and Dao Fang Chang. "Knowledge-Based System for Major-Pieces Lifting Project Using MRM." Applied Mechanics and Materials 170-173 (May 2012): 3260–65. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3260.

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Knowledge-based system (KBS) for major-pieces lifting projects can be useful for improving the level of decision-making. And it is important to use the knowledge acquired from domain experts to finish all kinds of projects for lifting major-pieces. Knowledge acquisition plays a critical role in constructing a KBS for major-pieces lifting project, also it is commonly regarded as a major obstacle and bottleneck in the process of designing and implementing a KBS. This paper presents a KBS model based on matrix representation and mapping (MRM) approach to facilitate the effectiveness of knowledge acquisition in constructing a KBS. A case study on practical application is employed to illustrate how the KBS works.
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Jensen, Isabel Nadine, Roumyana Slabakova, Marit Westergaard, and Björn Lundquist. "The Bottleneck Hypothesis in L2 acquisition: L1 Norwegian learners’ knowledge of syntax and morphology in L2 English." Second Language Research 36, no. 1 (February 28, 2019): 3–29. http://dx.doi.org/10.1177/0267658318825067.

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The Bottleneck Hypothesis (Slabakova, 2008, 2013) proposes that acquiring properties of the functional morphology is the most challenging part of learning a second language. In the experiment presented here, the predictions of this hypothesis are tested in the second language (L2) English of Norwegian native speakers. Two constructions are investigated that do not match in English and Norwegian: One involving functional morphology, subject–verb (SV) agreement, which is obligatory in the L2 but non-existent in the first language (L1), and one involving syntax, verb-second (V2) word order, which is obligatory in the L1, but restricted to specific contexts in the L2. The results of an acceptability judgement task indicate that the participants struggled more with identifying ungrammatical SV agreement than ungrammatical word order. We conclude that the findings lend tentative support to the Bottleneck Hypothesis.
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Dissertations / Theses on the topic "Knowledge acquisition bottleneck"

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Suraweera, Pramuditha. "Widening the Knowledge Acquisition Bottleneck for Intelligent Tutoring Systems." Thesis, University of Canterbury. Computer Science and Software Engineering, 2007. http://hdl.handle.net/10092/1150.

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Empirical studies have shown that Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing an ITS is a labour-intensive and time-consuming process. A major share of the development effort is devoted to acquiring the domain knowledge that accounts for the intelligence of the system. The goal of this research is to reduce the knowledge acquisition bottleneck and enable domain experts to build the domain model required for an ITS. In pursuit of this goal an authoring system capable of producing a domain model with the assistance of a domain expert was developed. Unlike previous authoring systems, this system (named CAS) has the ability to acquire knowledge for non-procedural as well as procedural tasks. CAS was developed to generate the knowledge required for constraint-based tutoring systems, reducing the effort as well as the amount of expertise in knowledge engineering and programming required. Constraint-based modelling is a student modelling technique that assists in somewhat easing the knowledge acquisition bottleneck due to the abstract representation. CAS expects the domain expert to provide an ontology of the domain, example problems and their solutions. It uses machine learning techniques to reason with the information provided by the domain expert for generating a domain model. A series of evaluation studies of this research produced promising results. The initial evaluation revealed that the task of composing an ontology of the domain assisted with the manual composition of a domain model. The second study showed that CAS was effective in generating constraints for the three vastly different domains of database modelling, data normalisation and fraction addition. The final study demonstrated that CAS was also effective in generating constraints when assisted by novice ITS authors, producing constraint sets that were over 90% complete.
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Ballout, Ali. "Apprentissage actif pour la découverte d'axiomes." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4026.

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Cette thèse aborde le défi de l'évaluation des formules logiques candidates, avec un accent particulier sur les axiomes, en combinant de manière synergique l'apprentissage automatique et le raisonnement symbolique. Cette approche innovante facilite la découverte automatique d'axiomes, principalement dans la phase d'évaluation des axiomes candidats générés. La recherche vise à résoudre le problème de la validation efficace et précise de ces candidats dans le contexte plus large de l'acquisition de connaissances sur le Web sémantique.Reconnaissant l'importance des heuristiques de génération existantes pour les axiomes candidats, cette recherche se concentre sur l'avancement de la phase d'évaluation de ces candidats. Notre approche consiste à utiliser ces candidats basés sur des heuristiques, puis à évaluer leur compatibilité et leur cohérence avec les bases de connaissances existantes. Le processus d'évaluation, qui nécessite généralement beaucoup de calculs, est révolutionné par le développement d'un modèle prédictif qui évalue efficacement l'adéquation de ces axiomes en tant que substitut du raisonnement traditionnel. Ce modèle innovant réduit considérablement les exigences en matière de calcul, en utilisant le raisonnement comme un "oracle" occasionnel pour classer les axiomes complexes lorsque cela est nécessaire.L'apprentissage actif joue un rôle essentiel dans ce cadre. Il permet à l'algorithme d'apprentissage automatique de sélectionner des données spécifiques pour l'apprentissage, améliorant ainsi son efficacité et sa précision avec un minimum de données étiquetées. La thèse démontre cette approche dans le contexte du Web sémantique, où le raisonneur joue le rôle d'"oracle" et où les nouveaux axiomes potentiels représentent des données non étiquetées.Cette recherche contribue de manière significative aux domaines du raisonnement automatique, du traitement du langage naturel et au-delà, en ouvrant de nouvelles possibilités dans des domaines tels que la bio-informatique et la preuve automatique de théorèmes. En mariant efficacement l'apprentissage automatique et le raisonnement symbolique, ces travaux ouvrent la voie à des processus de découverte de connaissances plus sophistiqués et autonomes, annonçant un changement de paradigme dans la manière dont nous abordons et exploitons la vaste étendue de données sur le web sémantique
This thesis addresses the challenge of evaluating candidate logical formulas, with a specific focus on axioms, by synergistically combining machine learning with symbolic reasoning. This innovative approach facilitates the automatic discovery of axioms, primarily in the evaluation phase of generated candidate axioms. The research aims to solve the issue of efficiently and accurately validating these candidates in the broader context of knowledge acquisition on the semantic Web.Recognizing the importance of existing generation heuristics for candidate axioms, this research focuses on advancing the evaluation phase of these candidates. Our approach involves utilizing these heuristic-based candidates and then evaluating their compatibility and consistency with existing knowledge bases. The evaluation process, which is typically computationally intensive, is revolutionized by developing a predictive model that effectively assesses the suitability of these axioms as a surrogate for traditional reasoning. This innovative model significantly reduces computational demands, employing reasoning as an occasional "oracle" to classify complex axioms where necessary.Active learning plays a pivotal role in this framework. It allows the machine learning algorithm to select specific data for learning, thereby improving its efficiency and accuracy with minimal labeled data. The thesis demonstrates this approach in the context of the semantic Web, where the reasoner acts as the "oracle," and the potential new axioms represent unlabeled data.This research contributes significantly to the fields of automated reasoning, natural language processing, and beyond, opening up new possibilities in areas like bioinformatics and automated theorem proving. By effectively marrying machine learning with symbolic reasoning, this work paves the way for more sophisticated and autonomous knowledge discovery processes, heralding a paradigm shift in how we approach and leverage the vast expanse of data on the semantic Web
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Suraweera, Pramuditha. "Widening the knowledge acquisition bottleneck for intelligent tutoring systems : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in the University of Canterbury /." 2006. http://library.canterbury.ac.nz/etd/adt-NZCU20070417.162903.

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Book chapters on the topic "Knowledge acquisition bottleneck"

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Weibel, Robert, Stefan Keller, and Tumasch Reichenbacher. "Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence." In Lecture Notes in Computer Science, 139–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60392-1_10.

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Mendes, David, Irene Pimenta Rodrigues, and Carlos Fernandes Baeta. "Clinical Practice Ontology Automatic Learning from SOAP Reports." In Healthcare Ethics and Training, 625–40. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2237-9.ch028.

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We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the “Knowledge Acquisition Bottleneck” problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel state-of-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.
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Mendes, David, Irene Pimenta Rodrigues, and Carlos Fernandes Baeta. "Clinical Practice Ontology Automatic Learning from SOAP Reports." In Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions, 349–63. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8828-5.ch016.

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We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the “Knowledge Acquisition Bottleneck” problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel state-of-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.
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Asai, Masataro, Hiroshi Kajino, Alex Fukunaga, and Christian Muise. "Chapter 2. Symbolic Reasoning in Latent Space: Classical Planning as an Example." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210349.

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Symbolic systems require hand-coded symbolic representation as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems. To address the gap between the two fields, one has to solve Symbol Grounding problem: The question of how a machine can generate symbols automatically. We discuss our recent work called Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We discuss several key ideas that made Latplan possible which would hopefully extend to many other symbolic paradigms outside classical planning.
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Nirenburg, Sergei. "Lexicon Acquisition for NLP: A Consumer Report." In Computational Approaches to the Lexicon, 313–48. Oxford University PressOxford, 1994. http://dx.doi.org/10.1093/oso/9780198239796.003.0012.

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Abstract Current natural language processing systems typically operate in a ‘demo’ mode—they sometimes feature sizeable grammars but seldom sizeable lexicons containing information about meaning. This difficulty of going beyond toy systems is one of the main bottlenecks of artificial intelligence in general. Scaling up the dictionaries (and other knowledge bases) of a knowledge-based system is, however, essential for the overall success of the field. There are several ways in which the indispensable massive knowledge acquisition program can in principle be conducted. One can envisage developing a machine learning system for automatic acquisition of vast quantities of knowledge through experimentation on a (sub)world.
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Garfield, Seth. "Drug Prospects." In Guaraná, 74–99. University of North Carolina PressChapel Hill, NC, 2022. http://dx.doi.org/10.5149/northcarolina/9781469671277.003.0005.

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Abstract This chapter looks at guaraná’s fleeting popularity in late nineteenth-century Anglo-American pharmacy and therapeutics. It explores Western scientists’ acquisition and dissemination of knowledge of guaraná and the drug’s interface with commercial health care, cultural practices, and epistemologies. Nineteenth-century scientists’ reclassification of guaraná according to the “universal” standards of botany and chemistry transpired within the context of colonial botanizing and Indigenous knowledge erasures. Bavarian Karl von Martius renamed the plant according to the precepts and politics of binominal nomenclature established by Karl Linnaeus, European chemists identified guaraná’s active principle as caffeine, and pharmaceutical firms such as the Parke-Davis Company and patent medicine manufacturers marketed the “tonic drug” for medical conditions ranging from headaches and diarrhea to neurasthenia, or nervous exhaustion. The Royal Botanical Gardens at Kew, eyeing the drug’s growing popularity, unsuccessfully promoted crop transplants to British colonies in South Asia. The high cost of guaraná relative to other sources of caffeine, bottlenecks in supply chains, and the advent of synthetic analgesics such as aspirin ultimately doomed guaraná as a pharmaceutical export. Employing insights from economic history, the history of science, the history of pharmaceuticals, and medical anthropology, this chapter explores the processes of transformation and adaptation surrounding cross-cultural therapeutic transfers.
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Conference papers on the topic "Knowledge acquisition bottleneck"

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Pasini, Tommaso. "The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/687.

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Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context. It lies at the base of Natural Language Processing as it provides semantic information for words. In the last decade, great strides have been made in this field and much effort has been devoted to mitigate the knowledge acquisition bottleneck problem, i.e., the problem of semantically annotating texts at a large scale and in different languages. This issue is ubiquitous in WSD as it hinders the creation of both multilingual knowledge bases and manually-curated training sets. In this work, we first introduce the reader to the task of WSD through a short historical digression and then take the stock of the advancements to alleviate the knowledge acquisition bottleneck problem. In that, we survey the literature on manual, semi-automatic and automatic approaches to create English and multilingual corpora tagged with sense annotations and present a clear overview over supervised models for WSD. Finally, we provide our view over the future directions that we foresee for the field.
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Barba, Edoardo, Luigi Procopio, Niccolò Campolungo, Tommaso Pasini, and Roberto Navigli. "MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/531.

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The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation. In this paper, we propose a semi-supervised approach based upon a novel label propagation scheme, which, by jointly leveraging contextualized word embeddings and the multilingual information enclosed in a knowledge base, projects sense labels from a high-resource language, i.e., English, to lower-resourced ones. Backed by several experiments, we provide empirical evidence that our automatically created datasets are of a higher quality than those generated by other competitors and lead a supervised model to achieve state-of-the-art performances in all multilingual Word Sense Disambiguation tasks. We make our datasets available for research purposes at https://github.com/SapienzaNLP/mulan.
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Chen, Xiyang, Kewei Zhang, and Yucheng Peng. "Research on Multi Diagnosis Methods for Hydro-Generator Sets." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58163.

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Hydro-Generator Sets Condition Monitoring and Predictive Maintenance activity has increased dramatically over the past few years. The Fault Diagnostic System is the key technique for the Predictive Maintenance. This paper discusses the Fault Diagnostic System function structure, system design and inferential strategy of Multi-Fault Diagnostic System of the Hydro-Generator Sets. The developing of the power system to big unit requires the higher automation and reliability of the power station. Electrical power systems are constantly exposed to faults and disturbances. This may lead to damage or it may pose a threat to reliable power system operation if a faulty cannot be quickly isolated from the system operation. In accordance with diversity and complexity of Hydro-Generator Sets faults, this paper brings forward a type of fault diagnosis method based on Multi-Diagnosis methods. The Multi-Diagnosis system is constituted of two Sub-Diagnosis systems: one is On-Line Sub-Diagnosis system that based on Bayesian Network (BN) just for the modeling with Bayesian Network has been a powerful tool to solve many uncertainty problems and also with the ability of predicting the future diagnosis; the other is Off-Line Sub-Diagnosis System that based on Model of a hydraulic Turbine-Generator Rotor-Bearing. In order to apply the Bayesian Network model to the engineering fields, we have to solve the problem of constructing the Bayesian Network. Then it suggests a method of constructing Bayesian Network based on the Fault Trees that widely used by the engineers. Base on the construction method, we will construct the Bayesian Network quickly, and Bayesian Network is more suitable for Hydro-Generator Sets fault diagnosis. In accordance with the On-Line Diagnosis Sub-System, it adopts Case-Based Reasoning to make the decision of final diagnosis result or further diagnosis. However, the method mentioned above is limited because of its bottleneck of the knowledge acquisition. The model strategy of the Rotor-Bearing system of Hydro-Generator is discussed and a multi-degree-freedom nonlinear model is developed. It proposes the simulation in accordance with the three fields such as: waterpower, electric and machine. Mechanical, electrical and hydraulic forces acting on rotor externally can be taken into account during the model calculating process. The transient responses of the system are calculated by combined used the transfer matrix method. This paper brings forward a prototype of Hydro-Generator Sets Fault Diagnostic System in order to make a more efficient fault diagnostic decision.
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Stensrud, Rune, Sigmund Valaker, and Olav Rune Nummedal. "Exploring Human autonomy teaming methods in challenging environments: the case of uncrewed system (UxS) solutions – challenges and opportunities (with AI)." In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004307.

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Current uncrewed system (UxS) solutions tend to operate with tightly coupled Command and Control systems, making it difficult to contribute to operating as an integrated force. The case presented in this article is used to reason at the conceptual level about the different requirements and approaches for a future Norwegian UxS Integrated C2 system in order to inform the national development of an UxS Integrated C2 Reference Architecture. This is one in a series of papers that will develop a mission engineering approach and represents functional analysis needed for future acquisition of Norwegian UxS. Based on this work and the development of the situated Cognitive Engineering (sCE)-method eliciting knowledge, and knowledge acquisition information, we make key findings for outlining a strategic guide for an initial Norwegian UxS reference system and set-up (manning, organization and technical know-how).UxS solutions must be available to support ISR services for a variety of tasks and units on all military branches and levels. An UxS reference system must be adapted to the operational area and be available to operate within a harsh environment at the Northern Flank of NATO supporting those who need the information. Modern UxS solutions are based on human control and management, which entails human autonomy teaming which can be labour-intensive, with the potential for cognitive overload as well as bottlenecks in information processing. In the article, we presents a framework that support future acquisition of Norwegian UxS that suggests that autonomy must be distributed to reduce vulnerability and be scalable to handle emergency adapted the Northern Flank of NATO environment e.g. an autonomous system that interacts with its surroundings demonstrating a cooperative design approach with new opportunities (e.g. with and without AI support). We claim that a common future acquisition framework of Norwegian UxS applications (with AI) can reduce the burden on the operator based on results from our Functional Analysis (sCE-method) and empirical studies.
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