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MacMillan, Jean, Eileen B. Entin, and Daniel Serfaty. "Evaluating Expertise in a Complex Domain–Measures Based on Theory." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 17 (October 1993): 1152–55. http://dx.doi.org/10.1177/154193129303701706.
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Human factors practitioners are often concerned with defining and evaluating expertise in complex domains where there may be no agreed-upon expertise levels, no single right answers to problems, and where the observation and measurement of real-world expert performance is difficult. This paper reports the results of an experiment in which expertise was assessed in an extremely complex and demanding domain–military command decision making in tactical warfare. The hypotheses of the experiment were: 1) command decisionmaking expertise can be recognized in practice by domain experts; 2) differences in the command decisionmaking expertise of individuals can be identified even under conditions that do not fully replicate the real world; and 3) observers who are not domain experts can recognize the expert behaviors predicted by a mental-model theory about the nature of expertise. In the experiment, the expertise of military officers in developing tactical plans was assessed independently by three “super-expert” judges, and these expertise-level ratings were correlated with independent theory-based measures used by observers who were not domain experts. The results suggest that experts in a domain have a shared underlying concept of expertise in that domain even if they cannot articulate that concept, that this expertise can be elicited and measured in situations that do not completely mimic the real world, and that expertise measures based on a mental-model theory can be used effectively by observers who are not experts in the domain.
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Lepouras, George, Costas Vassilakis, Constantin Halatsis, and Panagiotis Georgiadis. "Domain expert user development." Communications of the ACM 50, no. 9 (September 2007): 79–83. http://dx.doi.org/10.1145/1284621.1284623.
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Poo, Chiang-Choon Danny, and Hongjun Lu. "Multi-domain expert systems." Expert Systems 8, no. 2 (May 1991): 67–73. http://dx.doi.org/10.1111/j.1468-0394.1991.tb00254.x.
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Chan, Christine, and Izak Benbasat. "Case research on knowledge acquisition: observations and lessons." Knowledge Engineering Review 6, no. 2 (June 1991): 97–120. http://dx.doi.org/10.1017/s0269888900005634.
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AbstractExpert systems are being built despite the widely acknowledged problem of acquiring knowledge from experts. This study attempts to understand how knowledge acquisition is conducted in practice by investigating three expert system development projects. A CASE research methodology is adopted, and data is collected through unobtrusive observation, from taped protocols of knowledge acquisition sessions, retrospective interviews with the participants involved, and deliverables produced. The variables examined include the problem domain, the domain expert, the knowledge engineer, the knowledge acquisition process, the expert system construction process, potential users, organizational setting, and the expert system itself. The knowledge acquisition processes for three expert systems in the domains of law of negligence, telephone line fault diagnosis, and wastewater treatment have been examined. By juxtaposing the observations drawn with findings from the relevant literature, the study makes prescriptive suggestions on considerations and techniques for future acquisition efforts, and provides data for hypothesis generation in further research.
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Husain, Omayma, Naomie Salim, Rose Alinda Alias, Samah Abdelsalam, and Alzubair Hassan. "Expert Finding Systems: A Systematic Review." Applied Sciences 9, no. 20 (October 11, 2019): 4250. http://dx.doi.org/10.3390/app9204250.
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The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.
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Prytz, Erik G., Caroline Norén, and Carl-Oscar Jonson. "Fixation Differences in Visual Search of Accident Scenes by Novices and Expert Emergency Responders." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 8 (August 13, 2018): 1219–27. http://dx.doi.org/10.1177/0018720818788142.
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Objective: We sought to investigate whether expert–novice differences in visual search behavior found in other domains also apply to accident scenes and the emergency response domain. Background: Emergency service professionals typically arrive at accidents only after being dispatched when a civilian witness has called an emergency dispatch number. Differences in visual search behavior between the civilian witness (usually a novice in terms of emergency response) and the professional first responders (experts at emergency response) could thus result in the experts being given insufficient or erroneous information, which would lead them to arrive unprepared for the actual situation. Method: A between-subjects, controlled eye-tracking experiment with 20 novices and 17 experts (rescue and ambulance service personnel) was conducted to explore expert–novice differences in visual search of accident and control images. Results: The results showed that the experts spent more time looking at task-relevant areas of the accident images than novices did, as predicted by the information reduction hypothesis. The longer time was due to longer fixation durations rather than a larger fixation count. Conclusion: Expert–novice differences in visual search are present in the emergency domain. Given that this domain is essential to saving lives and also relies heavily on novices as the first link in the chain of response, such differences deserve further exploration. Application: Visual search behavior from experts can be used for training purposes. Eye-tracking studies of novices can be used to inform the design of emergency dispatch interviews.
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Anandhan, Anitha, Maizatul Akmar Ismail, and Liyana Shuib. "EXPERT RECOMMENDATION THROUGH TAG RELATIONSHIP IN COMMUNITY QUESTION ANSWERING." Malaysian Journal of Computer Science 35, no. 3 (July 27, 2022): 201–21. http://dx.doi.org/10.22452/mjcs.vol35no3.2.
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Community Question Answering (CQA) services are technical discussion forums websites on social media that serve as a platform for users to interact mainly via question and answer. However, users of this platform have posed dissatisfaction over the slow response and the preference for user domains due to the overwhelming information in CQA websites. Numerous past studies focusing on expert recommendation are solely based on the information available from websites where they rarely account for the preference of users’ domain knowledge. This condition prompts the need to identify experts for the questions posted on community-based websites. Thus, this study attempts to identify ranking experts’ derived from the tag relationship among users in the CQA websites to construct user profiles where their interests are realized in the form of tags. Experts are considered users who post high-quality answers and are often recommended by the system based on their previous posts and associated tags. These associations further describe tags that often co-occur in posts and the significant domains of user interest. The current study further explores this relationship by adopting the “Tag Relationship Expert Recommendation (TRER)” method where Questions Answer (QA) Space is utilized as a dataset to identify users with similar interests and subsequently rank experts based on the tag-tag relationship for user’s question. The results show that the TRER method outperforms existing baseline methods by effectively improving the performance of relevant domain experts in CQA, thereby facilitating the expert recommendation process in answering questions posted by technical and academic professionals.
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Walton, Joan, M. A. Musen, D. M. Combs, C. D. Lane, E. H. Shortliffe, and L. M. Fagan. "Graphical Access to Medical Expert Systems: III. Design of a Knowledge Acquisition Environment*)." Methods of Information in Medicine 26, no. 03 (July 1987): 78–88. http://dx.doi.org/10.1055/s-0038-1635497.
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SummaryKnowledge acquisition for expert systems typically is a tedious, iterative process involving long hours of consultation between the domain experts and the computer scientists who serve as knowledge engineers. For well-understood domains, however, it may be possible to facilitate the knowledge acquisition process by allowing domain experts to develop and edit a knowledge base directly. Administration of protocol-directed cancer chemotherapy is such a well-understood application area, and a knowledge acquisition system, called OPAL, has been developed for eliciting chemotherapy-protocol knowledge directly from expert oncologists. OPAL’s knowledge acquisition approach is based on the interactive graphics environment available on current generation workstations. The use of graphics improves the interface by reducing typing, avoiding natural language interpretations, and allowing flexibility in entry sequence. The knowledge in OPAL is displayed using an arrangement of hierarchically related, graphical forms. The position of a particular form in the hierarchy defines the context of the knowledge contained in the form. Intelligent editing programs such as OPAL can streamline the knowledge engineering process for highly structured domains requiring repetitive knowledge entry.
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Albagli-Kim, Sivan, and Dizza Beimel. "Knowledge Graph-Based Framework for Decision Making Process with Limited Interaction." Mathematics 10, no. 21 (October 26, 2022): 3981. http://dx.doi.org/10.3390/math10213981.
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In this work, we present an algorithmic framework that supports a decision process in which an end user is assisted by a domain expert to solve a problem. In addition, the communication between the end user and the domain expert is characterized by a limited number of questions and answers. The framework we have developed helps the domain expert to pinpoint a small number of questions to the end user to increase the likelihood of their insights being correct. The proposed framework is based on the domain expert’s knowledge and includes an interaction with both the domain expert and the end user. The domain expert’s knowledge is represented by a knowledge graph, and the end user’s information related to the problem is entered into the graph as evidence. This triggers the inference algorithm in the graph, which suggests to the domain expert the next question for the end user. The paper presents a detailed proposed framework in a medical diagnostic domain; however, it can be adapted to additional domains with a similar setup. The software framework we have developed makes the decision-making process accessible in an interactive and explainable manner, which includes the use of semantic technology and is, therefore, innovative.
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Jung, Ju Yeon, Tom Steinberger, John L. King, and Mark S. Ackerman. "How Domain Experts Work with Data: Situating Data Science in the Practices and Settings of Craftwork." Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (March 30, 2022): 1–29. http://dx.doi.org/10.1145/3512905.
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Domain experts play an essential role in data science by helping data scientists situate their technical work beyond the statistical analysis of large datasets. How domain experts themselves may engage with data science tools as a type of end-user remains largely invisible. Understanding data science as domain expert-driven depends on understanding how domain experts use data. Drawing on an ethnographic study of a craft brewery in Korea, we show how craft brewers worked with data by situating otherwise abstract data within their brewing practices and settings. We contribute theoretical insight into how domain experts use data distinctly from technical data scientists in terms of their view of data (situated vs. abstract), purposes for engaging with data (guiding processes over predicting outcomes), and overall goals of using data (flexible control vs. precision). We propose four ways in which working with data can be supported through the design of data science tools, and discuss how craftwork can be a useful lens for integrating domain expert-driven understandings of data science into CSCW and HCI research.
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Nguyen Duc, Tho, Chanh Minh Tran, Phan Xuan Tan, and Eiji Kamioka. "Domain Adaptation for Imitation Learning Using Generative Adversarial Network." Sensors 21, no. 14 (July 9, 2021): 4718. http://dx.doi.org/10.3390/s21144718.
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Imitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert. However, standard imitation learning methods assume that the agents and the demonstrations provided by the expert are in the same domain configuration. Such an assumption has made the learned policies difficult to apply in another distinct domain. The problem is formalized as domain adaptive imitation learning, which is the process of learning how to perform a task optimally in a learner domain, given demonstrations of the task in a distinct expert domain. We address the problem by proposing a model based on Generative Adversarial Network. The model aims to learn both domain-shared and domain-specific features and utilizes it to find an optimal policy across domains. The experimental results show the effectiveness of our model in a number of tasks ranging from low to complex high-dimensional.
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Liu, Lu, Wanli Zuo, Jiayu Han, and Tao Peng. "DEDSC: A Domain Expert Discovery Method Based on Structure and Content." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 04 (July 12, 2018): 583–99. http://dx.doi.org/10.1142/s0218488518500277.
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Researchers usually extract domain experts only through analyzing network structure or partitioning users into several communities according to their label information. Combining structure and content to discovery domain experts is a new attempt. Motivated by that, this paper proposes a domain expert discovery method based on network structure and content semantics, called DEDSC, which can extract authority nodes in overlapping communities. To analyze the overall authority for each user in the social network, two definitions, structure authority value and content authority value, are proposed to evaluate the authority of users in different perspectives. Partitioning users into communities can make the results more accurate. Experimental results show that our proposed method can discover domain experts effectively. In addition, when we need to extract domain experts in a new test dataset, we do not need to re-train the data in the training dataset.
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Dhir, Vinita, Caseng Zhang, Nick Barrowman, Ewa Sucha, Rachael F. Grace, John D. Grainger, Nancy L. Young, and Robert J. Klaassen. "Quality of Life in Childhood Immune Thrombocytopenia: Revision of the Kids' ITP Tools (KIT)." Blood 142, Supplement 1 (November 28, 2023): 1215. http://dx.doi.org/10.1182/blood-2023-182449.
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Introduction New treatment approaches have emerged for pediatric ITP over the years, potentially impacting the health-related quality of life (HRQoL) of affected individuals. The Kids' ITP Tools (KIT) (Child self-report/Parent proxy-report and Parent impact-report) have gained international attention as valid tools for monitoring HRQoL in children with ITP and the impact on their parents. The questionnaires were recently updated to reflect changes in clinical management. However, it has also become common practice to evaluate the impact of new therapies using domain scales of QOL (i.e., sub-categories), which are not yet available for the KIT. Thus, the primary objective of this study was to establish domain scales for the KIT 2.0 that accurately quantify and identify specific aspects of HRQoL that are impacted in children with ITP and their parents. Methods The study was conducted in two sequential phases. Phase 1 involved ITP experts identifying key conceptual domains for the KIT questionnaires based on their clinical experiences. An online survey (“card sort survey”) was distributed, and experts sorted the current KIT 2.0 questions into domains, with domain headings suggested as well. Phase 2 involved a statistical approach (i.e., exploratory factor analysis (EFA)) to yield models with different number of domains. Data from children with ITP and their parents was utilized to develop the models, and the data was specifically from three previous international multicentre studies: ITP Consortium of North America (ICON1 study), UK Children's Hospital (UK ITP Paediatric Registry), and the Children's Hospital of Eastern Ontario (KIT international validation). Results In Phase 1, 24 experts completed the Child self-report card-sort survey, while 12 experts completed the Parent impact survey. There were two models that were derived for the Child Self-Report, with one having three domain scales and the other with four domain scales. The Parent Impact resulted in only one model that had three domain scales. In Phase 2, exploratory factor analysis (EFA) resulted in three models for each of the three questionnaires (Table 1), with the final models selected from the EFA and based on clinical interpretability. The four-domain model was selected for the Child Self-Report. The model had a higher Tucker Lewis Index (TLI) score and cumulative percentage of variance compared to the three-domain model. Though the cumulative percentage of variance was higher for the six-domain model, the model had two domains that each contained only two questions, which is not ideal for face validity. The final domain headings for the Child self-report were derived from the expert card sort survey: ITP Uncertainty; General symptoms; ITP challenges/disruptions; and Loss of control. The four-domain model was also selected for the Parent proxy-report on the basis of the same factors as the Child self-report. Likewise, the four-factor model had higher TLI score and relatively lower root mean square error of approximation (RMSEA) compared to the three-factor. The six-factor model had an ideal RMSEA, but contained one domain that had only two questions. The final domain headings are the same as the Child self-report as the context of the questions are the same. The four-domain model was selected for the Parent impact-report. Though both the four- and six-factor models contained two domains with only two questions each, the two-factor model was not ideal clinically as the questions within each of the two domains were not all similar. The headings were also derived from the expert card-sort: ITP Uncertainty; Treatment; Functional well-being; and Activities. Conclusion The revised KIT 2.0 aims to comprehensively summarize health-related quality of life in pediatric patients with ITP and their parents. The introduction of a domain structure derived from expert- and patient-data to the KIT will enable identification of specific aspects of QOL that are affected, which is imperative given the introduction of novel pharmaceutical agents in ITP treatment.
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Atanasova, Irena. "On Design and Development of QLIFEX." International Journal of Applied Behavioral Economics 4, no. 2 (April 2015): 16–35. http://dx.doi.org/10.4018/ijabe.2015040102.
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This article presents an architectural framework of an expert system in the social area domain, and describes the design and the process of development of the expert system. The designed system is intended for the evaluation of quality of life (QL). The development of expert system for quality of life evaluation is a new information technology derived from artificial intelligence research. The new expert system will contain knowledge about sets of factors and indicators, which may be used for quality of life measure, as followings: equal protection by the law; freedom from discrimination; right to be treated equally without regard to gender, race, language, religion, political beliefs, etc. Details of the expert system for quality of life evaluation, its basic modules, design and some implementation details are also explained. The system uses the vast database and the knowledge acquired from social experts. The system is being developed in C Language Integrated Production System CLIPS. The expert system, described in this paper, is called QLIFEX, and it has already been designed so it uses the same knowledge for the following function: to provide expert evaluation for quality of life in the social area. The knowledge for the expert system will be acquired from domain experts, texts and other related sources.
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Moitra, Abha, Ravi Palla, and Arvind Rangarajan. "Automated Capture and Execution of Manufacturability Rules Using Inductive Logic Programming." Proceedings of the AAAI Conference on Artificial Intelligence 30, no. 2 (February 18, 2016): 4028–34. http://dx.doi.org/10.1609/aaai.v30i2.19080.
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Capturing domain knowledge can be a time-consuming process that typically requires the collaboration of a Subject Matter Expert and a modeling expert to encode the knowledge. In a number of domains and applications, this situation is further exacerbated by the fact that the Subject Matter Expert may find it difficult to articulate the domain knowledge as a procedure or rules, but instead may find it easier to classify instance data. To facilitate this type of knowledge elicitation from Subject Matter Experts, we have developed a system that automatically generates formal and executable rules from provided labeled instance data. We do this by leveraging the techniques of Inductive Logic Programming (ILP) to generate Horn clause based rules to separate out positive and negative instance data. We illustrate our approach on a Design For Manufacturability (DFM) platform where the goal is to design products that are easy to manufacture by providing early manufacturability feedback. Specifically we show how our approach can be used to generate feature recognition rules from positive and negative instance data supplied by Subject Matter Experts. Our platform is interactive, provides visual feedback and is iterative. The feature identification rules generated can be inspected, manually refined and vetted.
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Jabali, Ehsan Hashemi. "The Effects of Perspective-Taking on Perceptual Learning." International Letters of Social and Humanistic Sciences 48 (February 2015): 123–32. http://dx.doi.org/10.18052/www.scipress.com/ilshs.48.123.
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Research in perceptual psychology and anthropology has demonstrated that experts will literally see objects and events in their domain differently than non-experts. Experts can make distinctions and notice subtleties that a novice does not perceive. Experts also have strategies for looking at data and artifacts in a domain; they know where to look so that they can answer the important questions. An expert perspective can be described as the ways of seeing and experiencing phenomena that are influenced by the specialized knowledge that an expert has. The present paper will survey the existing literature on perspective-taking and learning, with a short discussion at the end of some of the ways that existing technologies have been used to support the sharing of perspectives. Of particular interest in this paper is the potential to use new media technologies to convey the perspective of someone with specialized knowledge or insider information on an important event—a viewpoint that could be termed an "expert perspective."
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Gao, Shengxiang, Zhengtao Yu, Linbin Shi, Xin Yan, and Haixia Song. "A Method to Review Expert Recommendation Using Topic Relevance and Expert Relationship." International Journal of Cooperative Information Systems 27, no. 01 (March 2018): 1741004. http://dx.doi.org/10.1142/s0218843017410040.
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In the process of recommending review experts to projects, in order to effectively make use of the relevance among topics and the relationship among experts, a new method is proposed for review expert recommendation using topic relevance and expert relationship. In this method, firstly, the relevance among topics and the relationships among experts are used to respectively construct the Markov network of topics and the Markov network of experts. Next, the maximum topic clique is extracted from the topic Markov network and the maximum expert clique is extracted from the expert Markov network; then, with the information of the two maximum cliques, the relevance between experts and projects is calculated. After that, according to the descending order of the relevant degree, the candidates are ranked. Finally, the experts, who are the top N to projects, are recommended. The experiments on five domain datasets are made and the results show that the proposed method can improve the effect of review expert recommendation, and the F-value increases by an average of 5% than without considering the relevance among topics and the relationship among experts.
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Gegenfurtner, Andreas, Hans Gruber, Erno Lehtinen, and Roger Säljö. "Horizontal Transition of Expertise." Frontline Learning Research 12, no. 3 (September 3, 2024): 20–44. http://dx.doi.org/10.14786/flr.v12i3.543.
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Expert performance in a domain is often defined as maximal adaptation to stable task constraints. This definition is useful when analysing the vertical transition when novices become experts. However, many workplaces undergo considerable changes and, thus, task constraints change as well. In this paper a complementary conceptualisation of expertise is offered, one that focuses on expert performance as recurring adaptation to dynamic task constraints. This definition is useful when analysing the horizontal transition when experts adapt to dynamically changing work contexts. Using the documentary method, the aim of the present study was to analyze cases of horizontal transitions based on qualitative biographical interview data from five experts reconstructing different types of adaptation to technological change in their domains that they have experienced. Implications for studying horizontal transitions at dynamic worksites are discussed.
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Rahman, Protiva, Arnab Nandi, and Courtney Hebert. "Amplifying Domain Expertise in Clinical Data Pipelines." JMIR Medical Informatics 8, no. 11 (November 5, 2020): e19612. http://dx.doi.org/10.2196/19612.
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Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.
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Sebastiani, Gian Domenico, Marta Mosca, Roberto Ravasio, Pietro Brambilla, Paola Raimondo, and Andrea Doria. "Treatment management of patients with systemic lupus erythematosus: a delphi consensus analysis." Global & Regional Health Technology Assessment 9 (October 10, 2022): 123–32. http://dx.doi.org/10.33393/grhta.2022.2470.
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Background: Systemic lupus erythematosus (SLE) is associated with clinical burden for the patient and organ damage. The development of therapies for SLE has been constrained by clinical and biologic heterogeneity. These represent challenges in clinical trial design and endpoint selection. Objective: To identify the most relevant descriptors for efficacy, endpoints, disease activity, organ damage, quality of life (QoL), and Patient Reported Outcome Measures (PROMs) in the treatment of SLE. Methods: A Delphi study was conducted using a national expert panel of clinicians in the treatment of SLE. A steering committee composed of 3 opinion leaders with deep expertise in SLE treatment was defined. The steering committee analyzed and appraised the evidence, designed the Delphi study, defined the statements, and analyzed the expert panel responses. A 2-round Delphi survey was conducted. Participants were asked to rate the statements using a five-point Likert scale. Results: Nine experts participated in the Delphi survey. After the two rounds, the consensus was reached on 18 of the 23 statements: 2 statements were included in the “efficacy” domain, 2 in the “glucocorticoid-sparing” domain, 2 in the “endpoint evaluation” domain, 4 in the “score” domain, 1 in the “disease activity” domain, 1 in the “organ damage” domain, 1 in the “QoL” domain, 2 in the “PROMs” domain, 1 in the “AIFA monitoring” domain and 2 in the “extra” domain. No statements reached consensus within the “onset” domain. Conclusion: In this Delphi study, 18 statements across 11 domains were agreed upon for the treatment of SLE.
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El-Nakla, Samir. "Case-Based Expert System to Support Electronics Design in Mechatronic System." Applied Mechanics and Materials 229-231 (November 2012): 2793–97. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.2793.
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Many current systems rely for their performance on achieving a balance between electronics, software and mechanical systems and the transfer of functionality between those domains. The design of such mechatronic systems therefore relies on the ability of the individual domain specialists to transfer knowledge about their domain within the overall design process. Enhancing the ability of non-specialists to understand the relationships between the various system elements and to communicate with the domain specialists will serve to enhance and support the design process. The paper therefore considers a tool based around the use of case-based expert system which is intended to provide such support by allowing a non-specialist to access information from a range of domains in a way which is easy to use and understand and which will establish and define the links between the various areas of technology.
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Chakravarty, S., and Y. Shahar. "Acquisition and Analysis of Repeating Patterns in Time-oriented Clinical Data." Methods of Information in Medicine 40, no. 05 (2001): 410–20. http://dx.doi.org/10.1055/s-0038-1634201.
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Summary Objectives: (1) Creation of an expressive language for specification of temporal patterns in clinical domains, (2) Development of a graphical knowledge-acquisition tool allowing expert physicians to define meaningful domain-specific patterns, (3) Implementation of an interpreter capable of detecting such patterns in clinical databases, and (4) Evaluation of the tools in the domains of diabetes and oncology. Methods: We describe a constraint-based language, named CAPSUL, for specification of temporal patterns. We implemented a knowledge-acquisition tool and a temporal-pattern interpreter within Résumé, a larger temporal-abstraction architecture. We evaluated the knowledge-acquisition process with the help of domain experts. In collaboration with the Rush Presbyterian/St. Luke’s Medical Center, we analyzed data of bone-marrow transplantation patients. The expert compared the detected patterns to a manual inspection of the data, with the help of an experimental information-visualization tool we are developing in a related project. Results: The CAPSUL language was expressive enough during the knowledge-acquisition process to capture almost all of the patterns that the experts found useful. The patterns detected in the data by the pattern interpreter were all verified as correct. Completeness (whether all correct patterns were found) was difficult to assess, due to the size of the database. Conclusions: The CAPSUL language enables medical experts to express temporal patterns involving multiple levels of abstraction of clinical data. The ability to reuse both domain-patterns and abstract constraints seems highly useful. The Résumé interpreter, augmented by the CAPSUL semantics, finds the complex patterns within a clinical time-oriented database in a sound fashion.
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Stern, David, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, and Armando Tacchella. "Collaborative Expert Portfolio Management." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 179–84. http://dx.doi.org/10.1609/aaai.v24i1.7561.
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We consider the task of assigning experts from a portfolio of specialists in order to solve a set of tasks. We apply a Bayesian model which combines collaborative filtering with a feature-based description of tasks and experts to yield a general framework for managing a portfolio of experts. The model learns an embedding of tasks and problems into a latent space in which affinity is measured by the inner product. The model can be trained incrementally and can track non-stationary data, tracking potentially changing expert and task characteristics. The approach allows us to use a principled decision theoretic framework for expert selection, allowing the user to choose a utility function that best suits their objectives. The model component for taking into account the performance feedback data is pluggable, allowing flexibility. We apply the model to manage a portfolio of algorithms to solve hard combinatorial problems. This is a well studied area and we demonstrate a large improvement on the state of the art in one domain (constraint solving) and in a second domain (combinatorial auctions) created a portfolio that performed significantly better than any single algorithm.
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Xu, Wenda, Michael Saxon, Misha Sra, and William Yang Wang. "Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11566–74. http://dx.doi.org/10.1609/aaai.v36i10.21410.
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Expert-layman text style transfer technologies have the potential to improve communication between members of scientific communities and the general public. High-quality information produced by experts is often filled with difficult jargon laypeople struggle to understand. This is a particularly notable issue in the medical domain, where layman are often confused by medical text online. At present, two bottlenecks interfere with the goal of building high-quality medical expert-layman style transfer systems: a dearth of pretrained medical-domain language models spanning both expert and layman terminologies and a lack of parallel corpora for training the transfer task itself. To mitigate the first issue, we propose a novel language model (LM) pretraining task, Knowledge Base Assimilation, to synthesize pretraining data from the edges of a graph of expert- and layman-style medical terminology terms into an LM during self-supervised learning. To mitigate the second issue, we build a large-scale parallel corpus in the medical expert-layman domain using a margin-based criterion. Our experiments show that transformer-based models pretrained on knowledge base assimilation and other well-established pretraining tasks fine-tuning on our new parallel corpus leads to considerable improvement against expert-layman transfer benchmarks, gaining an average relative improvement of our human evaluation, the Overall Success Rate (OSR), by 106%.
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Hendrawaty, Hendrawaty, Alfiya Zahara, and Azhar Azhar. "SISTEM DIAGNOSA PENYAKIT CORONA DENGAN METODE CASE-BASED REASONING DAN CERTAINTY FACTOR." Jurnal Real Riset 4, no. 2 (July 2, 2022): 174–79. http://dx.doi.org/10.47647/jrr.v4i2.645.
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Expert systems are part of a general category of computers known as intelligence.Created an expert system to work on a particular domain. In artificialintelligence, an expertsystem is a computersystemthat imitates the decision-making abilities of humans who are experts in theirfields. The main thrust of artificial intelligence is in developing, solving problems,to build a knowledge-based system in the specific medical field in the domain toaim at diagnosing Covid-19 through the application of an expert system in theform of symptom input. This has prompted the creation of an expert system for diagnosing Covid 19 using the Case Base Reasoning and Certainty Factormethods based on the Responsive Web. The Certanty Factor method is used tofind the certainty value of the disease by calculating the probability value of thedisease symptoms and Case Based Reasoning to find the similarity value of thedisease with the previous case. Based on the calculation results, the expert systemcan be used for early diagnosis of the disease. The result of this study is that thesystem can assist experts in diagnosing Covid-19 disease based on the results ofsystem testing through surveys by conducting interviews with Covid-19 experts.This study also tested the software using the Black Box method.Keywords : casebased reasoning, certainty factor, covid-19,expert system
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Hamilton, D. Kirk. "Expert in My Domain; Beginner in Yours." HERD: Health Environments Research & Design Journal 3, no. 3 (April 2010): 19–21. http://dx.doi.org/10.1177/193758671000300304.
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Williams, Mark A., Julian D. Ward, Paul Ward, and Nicolas J. Smeeton. "Domain Specificity, Task Specificity, and Expert Performance." Research Quarterly for Exercise and Sport 79, no. 3 (September 2008): 428–33. http://dx.doi.org/10.1080/02701367.2008.10599509.
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Dunstan, Neil. "Generating domain-specific web-based expert systems." Expert Systems with Applications 35, no. 3 (October 2008): 686–90. http://dx.doi.org/10.1016/j.eswa.2007.07.048.
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McDonnell, J. "Domain modelling in second-generation expert systems." Computer Physics Communications 61, no. 1-2 (November 1990): 13–21. http://dx.doi.org/10.1016/0010-4655(90)90103-8.
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Tavana, Madjid, and Vahid Hajipour. "A practical review and taxonomy of fuzzy expert systems: methods and applications." Benchmarking: An International Journal 27, no. 1 (September 11, 2019): 81–136. http://dx.doi.org/10.1108/bij-04-2019-0178.
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Purpose Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems. Design/methodology/approach The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems. Findings The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field. Originality/value Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.
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Qin, H., J. Xiao, X. Gao, and H. Wang. "Horse-Expert: An aided expert system for diagnosing horse diseases." Polish Journal of Veterinary Sciences 19, no. 4 (December 1, 2016): 907–15. http://dx.doi.org/10.1515/pjvs-2016-0112.
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Abstract In contrast to the rapid development of the horse husbandry in China, the ability of horse veterinarians to diagnose diseases has not been improved and only a few domain experts have considerable expertise. At present, many expert systems have been developed for diseases diagnosis, but few for horse diseases diagnosis have been studied in depth. This paper presents the design and development of a computer-aided expert system for diagnosing horse diseases. We suggest an approach for diagnosis of horse diseases based on the analysis of diagnostic characteristics and the experiential knowledge of domain experts. It is based on using evidence-weighted uncertainty reasoning theory, which is a combination of evidence theory and an uncertainty pass algorithm of confidence factors. It enables drawing of inferences with atypical clinical signs and the uncertainty of the user’s subjective understanding. It reduces the influence of subjective factors on diagnostic accuracy. The system utilizes a user friendly interface for users and requests a confidence factor from users when feedback is given to the system. Horse-Expert combines the confidence factors with weight factors assigned to clinical signs by experts during the knowledge acquisition process to make diagnostic conclusions. The system can diagnose 91 common horse diseases, and provides suggestions for appropriate treatment options. In addition, users can check the medical record through statistical charts. The system has been tested in seven demonstration areas of Xinjiang province in northwestern China. By constantly maintaining and updating the knowledge base, the system has potential application in veterinary practice.
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Singhal, Tarang. "Consulting Application a Dedicated Social Media platform to get Expert Advice." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1778–79. http://dx.doi.org/10.22214/ijraset.2021.39400.
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Abstract: In today’s fast growing world, The Information is the most powerful tool. A Doctor is an expert in medical sciences, an Engineer is an expert in Technical things similarly a fitness trainer is an expert in fitness related things. But What if a Person has some problem like a technical problem and he is not an expert in technical things ? In such situations A Person wants to have some expert advice. Now to overcome such problems a person may want to communicate with someone who is an expert in his problem domain. So Our Goal of the project is to reduce the gap between the problem seeker and the experts.
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Roth, E. M., W. C. Elm, D. D. Woods, and J. M. Gallagher. "Providing On-Line Advice for a Dynamic Control Task: A Case Study in Intelligent Support System Design." Proceedings of the Human Factors Society Annual Meeting 31, no. 1 (September 1987): 36–40. http://dx.doi.org/10.1177/154193128703100108.
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This paper describes an Intelligent Manual Feedwater Control Station (IMFCS) that provides on—line expert guidance for a process control task: control of feedwater during power plant startup and operation. The IFMCS provides control action advice derived from analysis of the performance of expert operators. It represents a novel approach to advisory systems in that (a) the IMFCS does not attempt to replicate in detail the surface activities of domain experts, but rather abstracts the cognitive competencies required for expert performance and provides a domain representation that promotes those competencies; (b) the advice takes an analogue graphic display form; (c) a common frame of reference is established between machine advisor and human practitioner that integrates advice and “explanation” avoiding problems associated with opaque advice.
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Candello, Heloisa, Claudio Pinhanez, Michael Muller, and Mairieli Wessel. "Unveiling Practices of Customer Service Content Curators of Conversational Agents." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (November 7, 2022): 1–33. http://dx.doi.org/10.1145/3555768.
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Conversational interfaces require two types of curation: data curation by data science workers and content curation by domain experts. Recent years have seen the possibilities for content curators to instruct conversational machines in the customer service domain (i.e., Machine Teaching). The activities of curating specialized data are time-consuming. These activities have a learning curve for the domain expert, and they rely on collaborators beyond the domain experts, including product owners, technology expert curators, management, marketing, and communication employees. However, recent research has looked at making this task easier for domain experts with a lack of knowledge in the Machine Learning system, and few papers have investigated the work practices and collaborations involved in this role. This paper aims to fill this gap, presenting and unveiling practices extracted from eleven semi-structured interviews and four design workshops with experts in Banking, Technical support, Humans Resources, Telecommunications, and Automotive sectors. First, we investigate the articulation work of the content curators and tech curators in training conversational machines. Second, we inspect the curatorial and collaboration strategies they use, which are not afforded by current conversational platforms. Third, we draw the design implications and possibilities to support individual and collaboration curating practices. We reflect on how those practices rely on self and collaboration with others for curation, trust, and data tracking and ownership.
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Adawi, Mudhar Al, Ibtisam Al Siyabi, Nasra Al Hashmi, Fatma Mahmood AbdulRasool, Asma Al Harrasi, Khalid Al Busaidi, and Warda Al Amri. "Developing nurse preceptor competency domain guide tool: A Delphi study." Journal of Nursing Education and Practice 12, no. 5 (December 23, 2021): 33. http://dx.doi.org/10.5430/jnep.v12n5p33.
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Background and objective: One of the strategies used to prepare novice nurses for their professional journeys in clinical practice is by implementing the preceptorship teaching and learning model. Competencies such as knowledge, experience, abilities, and attributes need to be measured to ensure the desired outcomes of the preceptorship are achieved and consistent. This study aimed to develop a nurse preceptor competency domain guide tool at a tertiary hospital in Oman.Methods: Three-round Delphi iterative design with experts was used to develop the nurse preceptor competency domain guide tool. Following standard measures, eight expert opinions were combined until a group consensus was achieved. The level of consensus within the expert panel was defined as ≥ 75% scoring of items were selected as an essential required competency/item.Results: Eight experts from a main tertiary hospital were included in the panel. Five core competency domains and five subdomains were identified and considered to be relevant for nurse preceptors at the hospital with consensus levels varying from 75% to 100%. A total of 83 descriptive items were identified for the competency guide tool.Conclusions: This study found that the main core competency domains of the tool that nurse preceptors should acquire to be competent preceptors are inter-professional communication skills, appropriate teaching strategies, time management skills, building a learning atmosphere, and coaching critical thinking. This tool would improve nurse preceptors’ performance and equip them with the required prerequisite competencies to professionally start their journey in clinical practices. Follow-up research on tool implementation is highly recommended to evaluate its effectiveness.
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McClure, Janice E. "DEVELOPMENT OF AN EXPERT SYSTEM." HortScience 26, no. 6 (June 1991): 795B—795. http://dx.doi.org/10.21273/hortsci.26.6.795b.
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The development of expert systems in agriculture consists of many steps such as problem definition, selection of experts, audience considerations, knowledge representation, coding, testing, and feedback. The problem definition and selection of experts for the problem domain are the foundation of a working system. Audience definition, economics and goal setting are areas that must be documented before knowledge engineering. Knowledge representation methods and system conceptual layout are the next level of development. The use of the user feedback and field testing data to improve the system are often overlooked. Benefits of expert systems for on farm decision making include education, efficiency, and adaption to changing regulations. Many aspects of agricultural expert systems are similar to traditional expert systems; yet special problem inherent in agriculture make the development interesting and challenging.
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Molnar, Kathleen K., and Ramesh Sharda. "Using the Internet for Knowledge Acquisition in Expert Systems Development: A Case Study." Journal of Information Technology 11, no. 3 (September 1996): 223–34. http://dx.doi.org/10.1177/026839629601100304.
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Knowledge acquisition is a major task in expert system development. This paper proposes one way of acquiring knowledge for expert system development: through the use of the Internet. Internet resources (e.g. Usenet groups, ListServ discussion lists, archive sites and on-line literature/database searches) are knowledge sources. Internet tools such as newsreaders, electronic mail, Telnet, FTP, gophers, archie, WAIS and World Wide Web provide access to these sources. The results of an exploratory study that examined the use of the Internet as a knowledge source are presented here in conjunction with a framework for using the Internet in the planning phase. Four major advantages can be found in this: the availability of multiple experts in multiple domains, the interaction of domain experts and end users, time/cost savings, and convenience. The lessons learned and some additional issues are also presented.
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Metzler, Donald, Yi Tay, Dara Bahri, and Marc Najork. "Rethinking search." ACM SIGIR Forum 55, no. 1 (June 2021): 1–27. http://dx.doi.org/10.1145/3476415.3476428.
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When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers. Successful question answering systems offer a limited corpus created on-demand by human experts, which is neither timely nor scalable. Pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than domain experts - they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over. This paper examines how ideas from classical information retrieval and pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of domain expert advice.
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Yang, Liu, Zhi Gang Hu, Jun Long, and Tao Guo. "Conceptual Modelling for Domain Ontology Using a 5W1H Six-Layer Framework." Advanced Materials Research 282-283 (July 2011): 68–73. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.68.
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Domain ontology plays an essential role in formalization and sharing of domain knowledge. However, constructing domain ontology is a labour intensive and time consuming engineering. To efficiently represent knowledge as a set of concepts and relations within domain ontology, a 5W1H method is proposed to analysis concepts and relations of it from six aspects including Who, When, Where, What, Why and How. The theory and methodology of modelling domain ontology based 5W1H is also presented in this paper. Take Expert Ontology in expert information domain for an example, six-layer framework for conceptual model of expert ontology based 5W1H is designed, and conceptual model of expert ontology is constructed, in which concepts and relations within expert ontology are defined. Application shows that the 5W1H modelling method is flexible and scalable, because it is not only able to assist ontology engineering to construct domain ontology quicker and more accurately, but also adaptable to business changes and user requirements. In addition, it is helpful to define reasoning rules for inferring knowledge and to share domain knowledge.
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Moser, Thomas, Stefan Biffl, Wikan Danar Sunindyo, and Dietmar Winkler. "Integrating Production Automation Expert Knowledge Across Engineering Domains." International Journal of Distributed Systems and Technologies 2, no. 3 (July 2011): 88–103. http://dx.doi.org/10.4018/jdst.2011070106.
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The engineering of a complex production automation system involves experts from several backgrounds, such as mechanical, electrical, and software engineering. The production automation expert knowledge is embedded in their tools and data models, which are, unfortunately, insufficiently integrated across the expert disciplines, due to semantically heterogeneous data structures and terminologies. Traditional integration approaches to data integration using a common repository are limited as they require an agreement on a common data schema by all project stakeholders. This paper introduces the Engineering Knowledge Base (EKB), a semantic-web-based framework, which supports the efficient integration of information originating from different expert domains without a complete common data schema. The authors evaluate the proposed approach with data from real-world use cases from the production automation domain on data exchange between tools and model checking across tools. Major results are that the EKB framework supports stronger semantic mapping mechanisms than a common repository and is more efficient if data definitions evolve frequently.
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Basri, H. "An expert system for planning landfill restoration." Water Science and Technology 37, no. 8 (April 1, 1998): 211–17. http://dx.doi.org/10.2166/wst.1998.0327.
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The application of expert systems technology in the domain of environmental management is particularly appropriate in order to preserve and disseminate efficiently valuable and scarce expertise at reasonable costs. The Landfill Restoration Plan Advisor (LRPA) is an expert system designed for use in the planning of sanitary landfill restoration. This paper describes the LRPA and the methodology and techniques that were employed for its development. A unique feature of this experience was that knowledge was acquired from multiple expertise sources in various forms. Expertise acquired from text analysis of textbooks and manuals was used in developing the core knowledge base for the expert system; while those acquired from text analysis of research publications and interviews with selected domain experts was used in the subsequent refinement and expansion of the basic prototype. The building process for the LRPA prototype adopted a modular approach using the incremental rapid prototyping methodology.
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Gray, Kristy L., Mayya Grebenshchikova, Sharleen L. O’Reilly, Lois McKellar, Peter M. Clifton, and Jennifer B. Keogh. "Development and Validation of an Online Survey to Assess Perception of Diabetes Risk and Barriers and Facilitators to Weight Loss Following Gestational Diabetes." International Journal of Environmental Research and Public Health 18, no. 2 (January 8, 2021): 480. http://dx.doi.org/10.3390/ijerph18020480.
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Our objective was to describe the development and validation of a survey investigating barriers to weight loss, perception of diabetes risk, and views of diet strategies following gestational diabetes (GDM). The survey underwent three stages of development: generation of items, expert evaluation, and pilot testing. A content validation index (CVI) was calculated from expert responses regarding item relevance, coherence, clarity, and response options. Experts also responded to the domain fit of questions linked to the Theoretical Domains Framework (TDF). Pilot responders answered the survey and responded to review questions. Six experts in the field of nutrition, midwifery, psychology, or other health or medical research completed the expert review stage of the survey. In the pilot test, there were 20 responders who were women with previous GDM and who were living in Australia. The overall CVI from the expert review was 0.91. All questions except one received an I-CVI of >0.78 for relevance (n = 35). Fourteen of the 27 items linked to the TDF received an agreement ratio of <1.0. Twenty-seven of the 31 pilot questions were completed by ≥90% of responders. Pilot review questions revealed an agreement percentage of ≥86% (n = 12) regarding the survey’s ease to complete, understand, importance, length, and interest level. The final survey tool consists of 30 items and achieved content validation through expert evaluation and pilot testing.
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Gray, Kristy L., Mayya Grebenshchikova, Sharleen L. O’Reilly, Lois McKellar, Peter M. Clifton, and Jennifer B. Keogh. "Development and Validation of an Online Survey to Assess Perception of Diabetes Risk and Barriers and Facilitators to Weight Loss Following Gestational Diabetes." International Journal of Environmental Research and Public Health 18, no. 2 (January 8, 2021): 480. http://dx.doi.org/10.3390/ijerph18020480.
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Our objective was to describe the development and validation of a survey investigating barriers to weight loss, perception of diabetes risk, and views of diet strategies following gestational diabetes (GDM). The survey underwent three stages of development: generation of items, expert evaluation, and pilot testing. A content validation index (CVI) was calculated from expert responses regarding item relevance, coherence, clarity, and response options. Experts also responded to the domain fit of questions linked to the Theoretical Domains Framework (TDF). Pilot responders answered the survey and responded to review questions. Six experts in the field of nutrition, midwifery, psychology, or other health or medical research completed the expert review stage of the survey. In the pilot test, there were 20 responders who were women with previous GDM and who were living in Australia. The overall CVI from the expert review was 0.91. All questions except one received an I-CVI of >0.78 for relevance (n = 35). Fourteen of the 27 items linked to the TDF received an agreement ratio of <1.0. Twenty-seven of the 31 pilot questions were completed by ≥90% of responders. Pilot review questions revealed an agreement percentage of ≥86% (n = 12) regarding the survey’s ease to complete, understand, importance, length, and interest level. The final survey tool consists of 30 items and achieved content validation through expert evaluation and pilot testing.
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Teng, Jing, Xinyue Wang, Kelong Lu, Xinuo Qiao, and Ning Hao. "Domain-specific and Domain-general Creativity Differences between Expert and Novice Designers." Creativity Research Journal 34, no. 1 (November 8, 2021): 55–67. http://dx.doi.org/10.1080/10400419.2021.1997175.
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Miyata, Takashi, and Yuji Matsumoto. "Natural Language Generation for Legal Expert System and Visualization of Generation Process." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 1 (February 20, 1998): 26–33. http://dx.doi.org/10.20965/jaciii.1998.p0026.
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An HPSG-based grammar and a sentence generation system for a small set of Japanese in legal expert domains are constructed. The system adopts its own general semantic system in which a domain-specific logical form is converted. This separation between domain-specific and linguistic semantics gives flexibility to both task processing and sentence generation. We also propose a visualization system which shows the generation process in a tabular form and operates as a graphical user interface for grammar debugging.
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Barley, William C., Jeffrey W. Treem, and Paul M. Leonardi. "Experts at Coordination: Examining the Performance, Production, and Value of Process Expertise." Journal of Communication 70, no. 1 (February 2020): 60–89. http://dx.doi.org/10.1093/joc/jqz041.
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Abstract This paper argues that coordination among domain experts can be viewed as a distinct form of knowledge in itself, and an area in which an individual may become an expert. We discuss why domain experts may be ill-equipped to coordinate their knowledge with the knowledge of others, and why individuals with process expertise may be better equipped to facilitate coordination between domain experts. Drawing on a qualitative study of nurses organizing emergency pediatric transfers, we demonstrate how process expertise is established, maintained, and enacted through situated communicative practices. The analysis characterizes process expertise as operating interdependently with, but distinct from, domain expertise, and shows how process expertise can aid in settings that demand complex coordination. This work challenges assumptions that coordination among domain experts is best addressed through supporting network connections or overcoming interpretive obstacles, and offers an alternative way to support coordination by cultivating process experts.
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Sartori, Fabio, and Riccardo Melen. "Wearable expert system development: definitions, models and challenges for the future." Program 51, no. 3 (September 5, 2017): 235–58. http://dx.doi.org/10.1108/prog-09-2016-0061.
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Purpose A wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues. Design/methodology/approach The WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowledge artifacts, each devoted to the acquisition and management of a specific kind of knowledge. The KAFKA framework is introduced from both the conceptual and computational points of view. An example is given which demonstrates the interaction, within this framework, of taxonomies, Bayesian networks and rule-based systems. An experimental assessment of the framework usability is also given. Findings The most interesting characteristic of WESs is their capability to evolve over time, due both to the measurement of new values for input variables and to the detection of new input events, that can be used to modify, extend and maintain knowledge bases and to represent domains characterized by variability over time. Originality/value WES is a new and challenging concept, dealing with the possibility for a user to develop his/her own decision support systems and update them according to new events when they arise from the environment. The system fully supports domain experts and users with no particular skills in knowledge engineering methodologies, to create, maintain and exploit their expert systems, everywhere and when necessary.
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Kutrovátz, Gábor. "Poznávat spolu s experty: kontextuální vědění ve vědě a kolem ní." Teorie vědy / Theory of Science 32, no. 2 (January 5, 2011): 479–505. http://dx.doi.org/10.46938/tv.2010.57.
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The original concept of epistemic dependence suggests uncritical deference to expert opinions for non-experts. In the light of recent work in science studies, however, the actual situation of epistemic dependence is seen to involve the necessary and ubiquitous need for lay evaluations of scientific experts. As expert knowledge means restricted cognitive access to some epistemic domain, lay evaluations of expert knowledge are rational and informed only when the criteria used by non-experts when judging experts are different from the criteria used by experts when making their claims. The distinction between ‘substantial knowledge’ and ‘contextual knowledge’ allows for the laypeople to know with experts without having to know precisely what experts know. Such meta-expert evaluations are not specific to the public sphere outside science, nor are they limited internally to science, but they are present in a wide range of contexts in and around science. The paper legitimizes the concept of contextual knowledge by relating it to the relevant literature, and expounds the idea by identifying some elements of such a knowledge.
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Whaiduzzaman, Md, Adnan Sakib, Nisha Jaman Khan, Sudipto Chaki, Labiba Shahrier, Sudipto Ghosh, Md Saifur Rahman, et al. "Concept to Reality: An Integrated Approach to Testing Software User Interfaces." Applied Sciences 13, no. 21 (November 3, 2023): 11997. http://dx.doi.org/10.3390/app132111997.
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This paper delves into the complex task of evaluating a website user interface (UI) and user experience (UX), a process complicated by gaps in research. To bridge this, we introduced an innovative human–computer interaction (HCI) framework that synergizes expert cognitive walkthroughs and user surveys for a comprehensive view. We transformed user responses into three key domains—control, engagement, and goal. Our work also generalized an extract of four context-level data metrics for a robust evaluation. The numerical evidence, such as a C1 score of 4.1, surpassing the expert usability benchmark, indicated our framework’s effectiveness. Our research not only addresses an essential gap by integrating assessments from both users and experts, but also offers actionable insights for UI/UX design. The findings extend beyond this specific context, providing a potent evaluation tool for website usability across various websites. Lastly, the research underscores the importance of prioritizing users’ needs and expert recommendations in design principles, significantly contributing to the broader domain of website usability and user experience.
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Xie, Hong Tao. "Bayesian Network Based Expert System for Tunnel Surrounding Rockmass Classification." Applied Mechanics and Materials 482 (December 2013): 248–51. http://dx.doi.org/10.4028/www.scientific.net/amm.482.248.
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Traditional surrounding rockmass classification methods have disadvantages of relative narrow scope of application, most of the time the classification result needs some modifications by geological expert and field situation. Based on the surrounding rockmass classification methods of BQ system, the Bayesian network and corresponding uncertainty reasoning principle has been introduced to develop an expert system for surrounding rockmass classification. By combining prior knowledge of domain experts with worksite data recorder, we get the posterior probability density of most nodes. The field practices proved that the expert system has good applicability.