Journal articles: 'Expert heuristics'

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Khowaja, Kamran, and Dena Al-Thani. "New Checklist for the Heuristic Evaluation of mHealth Apps (HE4EH): Development and Usability Study." JMIR mHealth and uHealth 8, no. 10 (October 28, 2020): e20353. http://dx.doi.org/10.2196/20353.

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Background Diabetes is one of the leading causes of death in developing countries. Existing mobile health (mHealth) app design guidelines lack a description of the support of continuous self-monitoring of health status, behavior change to improve and adopt a healthy lifestyle, and communication with health educators and health care professionals in case of any need. Objective This paper presents the development of a specialized set of heuristics called heuristic evaluation for mHealth apps (HE4EH) as an all-in-one tool and its applicability by performing a heuristic evaluation of an mHealth app. Methods An extensive review of heuristics and checklists was used to develop the HE4EH. The HE4EH was evaluated by domain experts for heuristics, checklist items, severity ratings, and overall satisfaction. The OneTouch app, which helps individuals with diabetes manage their blood glucose levels, was evaluated using HE4EH to identify usability problems that need to be fixed in the app. Results The expert evaluation of HE4EH revealed that the heuristics were important, relevant, and clear. The checklist items across the heuristics were clear, relevant, and acceptably grouped. In terms of evaluating the OneTouch app using the HE4EH, the most frequently violated heuristics included Content, Visibility, Match, and Self-monitoring. Most of the usability problems found were minor. The system usability scale score indicated that the OneTouch app is marginally acceptable. Conclusions This heuristic evaluation using the OneTouch app shows that the HE4EH can play a vital role for designers, researchers, and practitioners to use HE4EH heuristics and checklist items as a tool to design a new or evaluate and improve an existing mHealth app.

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Gonzaga de Oliveira, Sanderson L., and Libério M. Silva. "Low-cost heuristics for matrix bandwidth reduction combined with a Hill-Climbing strategy." RAIRO - Operations Research 55, no. 4 (July 2021): 2247–64. http://dx.doi.org/10.1051/ro/2021102.

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This paper studies heuristics for the bandwidth reduction of large-scale matrices in serial computations. Bandwidth optimization is a demanding subject for a large number of scientific and engineering applications. A heuristic for bandwidth reduction labels the rows and columns of a given sparse matrix. The algorithm arranges entries with a nonzero coefficient as close to the main diagonal as possible. This paper modifies an ant colony hyper-heuristic approach to generate expert-level heuristics for bandwidth reduction combined with a Hill-Climbing strategy when applied to matrices arising from specific application areas. Specifically, this paper uses low-cost state-of-the-art heuristics for bandwidth reduction in tandem with a Hill-Climbing procedure. The results yielded on a wide-ranging set of standard benchmark matrices showed that the proposed strategy outperformed low-cost state-of-the-art heuristics for bandwidth reduction when applied to matrices with symmetric sparsity patterns.

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Rassbach, Laura, Elizabeth Bradley, and Ken Anderson. "Providing Decision Support for Cosmogenic Isotope Dating." AI Magazine 32, no. 2 (March 16, 2011): 69. http://dx.doi.org/10.1609/aimag.v32i2.2349.

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Human experts in scientific fields routinely work with evidence that is noisy and untrustworthy, heuristics that are unproven, and possible conclusions that are contradictory. We present a deployed AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses two-dimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments. Calvin is in daily use by isotope dating experts.

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Frank, Lukas, Rouven Poll, Maximilian Roeglinger, and Rupprecht Lea. "Design heuristics for customer-centric business processes." Business Process Management Journal 26, no. 6 (March 22, 2020): 1283–305. http://dx.doi.org/10.1108/bpmj-06-2019-0257.

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PurposeCustomer centricity has evolved into a success factor for many companies, requiring all corporate activities – including business processes – to be aligned with customer needs. With most existing approaches to business process (re-)design focusing on process efficiency, customers are often treated as second-class citizens. Despite emergent research on customer process management, there is a lack of guidance on how to design customer-centric business processes.Design/methodology/approachThe authors conducted a structured literature review and analyzed companies awarded for outstanding customer centricity to compile design heuristics for customer-centric business processes. The authors iteratively validated and refined these heuristics with experts from academia and industry. Finally, the heuristics was grouped according to their expected impact on interaction capabilities to enable their prioritization in specific settings.FindingsThe authors proposed 15 expert-approved and literature-backed design heuristics for customer-centric business processes together with real-world examples. The heuristics aim at increasing customer satisfaction with interaction-intensive core processes, which is an important driver of corporate success.Originality/valueThe design heuristics complement existing efficiency-centered (re-)design heuristics. They reflect cognitive shortcuts that support process analysts in the generation of innovative ideas during process (re-)design. The heuristics also add to customer process management and help put customer centricity into practice.

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Chow, K. C. Ander, and W. E. Watt. "A knowledge-based expert system for flood frequency analysis." Canadian Journal of Civil Engineering 17, no. 4 (August 1, 1990): 597–609. http://dx.doi.org/10.1139/l90-068.

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Single-station flood frequency analysis is an important element in hydrotechnical planning and design. In Canada, no single statistical distribution has been specified for floods; hence, the conventional approach is to select a distribution based on its fit to the observed sample. This selection is not straightforward owing to typically short record lengths and attendant sampling error, magnified influence of apparent outliers, and limited evidence of two populations. Nevertheless, experienced analysts confidently select a distribution for a station based only on a few heuristics. A knowledge-based expert system has been developed to emulate these expert heuristics. It can perform data analyses, suggest an appropriate distribution, detect outliers, and provide means to justify a design flood on physical grounds. If the sample is too small to give reliable quantile estimates, the system performs a Bayesian analysis to combine regional information with station-specific data. The system was calibrated and tested for 52 stations across Canada. Its performance was evaluated by comparing the distributions selected by experts with those given by the developed system. The results indicated that the system can perform at an expert level in the task of selecting distributions. Key words: flood frequency, expert system, single-station, fuzzy logic, inductive reasoning, production system.

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Mehrez, A., and G. Steinberg. "Rule-Based Expert System versus Novices' Heuristics: A Matching Identification Problem." Psychological Reports 82, no. 3_suppl (June 1998): 1423–31. http://dx.doi.org/10.2466/pr0.1998.82.3c.1423.

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The present study focused on encoding and retrieval of knowledge as aids to decision-making by comparing the performance of rule-based expert systems and novices' heuristics within the framework of a matching identification problem. A rule-based expert system is developed with a computerized controlled procedure to evaluate and compare its performance with search strategies employed by novices. Analysis indicated that, as problem size increased, the system's outcome compared to novices' heuristics is improved.

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CHAN, CHRISTINE WAICHI, WEERAPONG KRITPIPHAT, and PAITOON TONTIWACHWUTHIKUL. "KNOWLEDGE ENGINEERING OF A MONITORING AND CONTROL DECISION SUPPORT SYSTEM." International Journal of Software Engineering and Knowledge Engineering 10, no. 03 (June 2000): 301–18. http://dx.doi.org/10.1142/s0218194000000183.

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This paper presents the Object-Oriented Knowledge Engineering (OOKE) methodology and its application in developing an expert system. OOKE is an expert system development methodology which incorporates the conceptual modelling tool of Inferential Modelling Technique into the analysis model of the Object-Oriented Software Engineering methodology. It was applied to develop a supervisory and decision support system for monitor and control of a water distribution system called the Water Advisor. The expertise, heuristics and reasoning knowledge of experts were acquired and then formulated in a model building process using the OOKE into a conceptual model which became the basis for a prototype expert system.

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Dobre, Jolie, Craig Harrington, Jennifer Herout, Charlene Weir, Ashley Cook, Tippy Carter, Donna Baggetta, and Walter “Bud” Relihan. "Rapid Heuristic Evaluation: Ensuring Fast and Reliable Usability Support." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 610–14. http://dx.doi.org/10.1177/1541931213601638.

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The U.S. Department of Veterans Affairs (VA) Human Factors Engineering (HFE) office developed a usability testing method called “Rapid Heuristic Evaluation” (Rapid HE) that offers benefits to users of the Agile development process. Rapid HE addresses the need to combine fast, reliable usability support with feedback from clinical subject matter experts (SMEs) during the design and development of an electronic health record (EHR). The Rapid HE process leverages established EHR heuristics to accelerate wireframe review and approval, and merges a traditional heuristic evaluation (HE) with an expert review by two SMEs. Our application of Rapid HEs has maximized use of resources and minimized the amount of time needed to provide feedback during Agile development cycles. This paper describes the Rapid HE process, deviations from traditional HEs, and reports on data from 16 HEs that our group conducted on an EHR platform currently being developed by VA.

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Barai, Sudhikumar, and Padmesh Charan Pandey. "KNOWLEDGE BASED EXPERT SYSTEM APPROACH TO INSTRUMENTATION SELECTION (INSEL)." TRANSPORT 19, no. 4 (August 31, 2004): 171–76. http://dx.doi.org/10.3846/16484142.2004.9637971.

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The selection of appropriate instrumentation for any structural measurement of civil engineering structure is a complex task. Recent developments in Artificial Intelligence (AI) can help in an organized use of experiential knowledge available on instrumentation for laboratory and in‐situ measurement. Usually, the instrumentation decision is based on the experience and judgment of experimentalists. The heuristic knowledge available for different types of measurement is domain dependent and the information is scattered in varied knowledge sources. The knowledge engineering techniques can help in capturing the experiential knowledge. This paper demonstrates a prototype knowledge based system for INstrument SELection (INSEL) assistant where the experiential knowledge for various structural domains can be captured and utilized for making instrumentation decision. In particular, this Knowledge Based Expert System (KBES) encodes the heuristics on measurement and demonstrates the instrument selection process with reference to steel bridges. INSEL runs on a microcomputer and uses an INSIGHT 2+ environment.

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Beyer, F., D. Schneider, and A. Schumacher. "Finding three-dimensional layouts for crashworthiness load cases using the graph and heuristic based topology optimization." Structural and Multidisciplinary Optimization 63, no. 1 (November 14, 2020): 59–73. http://dx.doi.org/10.1007/s00158-020-02768-0.

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AbstractIn this paper we present a new procedure using the graph and heuristic based topology optimization in order to find layouts for three-dimensional frame structures under crash loads. A three-dimensional graph describes the geometry and is used to derive a finite element shell model. The model of the frame structure consists of different profiles with continuous cross-sections. The ends of the profiles are currently rigidly connected. Each cross-section is defined by an individual two-dimensional graph. After performing a simulation its results are used by competing heuristics to propose new topologies for the frame structure. Most of these heuristics are derived from expert knowledge. Over several iterations, the goal is to improve the structures mechanical behavior. Typical objectives are the minimization of the structural intrusion in a crash scenario or the minimization of the maximal contact force between structural components. The presented method includes topology optimization by heuristics and shape optimization respectively sizing by mathematical optimization algorithms. The new flexible syntax for three- and two-dimensional graphs, the optimization process and the currently used heuristics are described. The performance is demonstrated for two examples, each optimized twice with opposing objectives.

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Kulshrestha, S., and R. Khosa. "CLIPS based decision support system for Water Distribution Networks." Drinking Water Engineering and Science Discussions 4, no. 1 (March 7, 2011): 1–38. http://dx.doi.org/10.5194/dwesd-4-1-2011.

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Abstract. The Water Distribution Networks (WDN) are managed by experts, who, over the years of their association and responsibility, acquire an empirical knowledge of the system and, characteristically, this knowledge remains largely confined to their respective personal domains. In the event of any new information and/or emergence of a new problem, these experts apply simple heuristics to design corrective measures and cognitively seek to predict network performance. The human interference leads to inefficient utilization of resources and unfair distribution. Researchers over the past, have tried to address to the problem and they have applied Artificial Intelligence (AI) tool to automate the decision process and encode the heuristic rules. The application of AI tool in the field of WDN management is meager. This paper describes a component of an ongoing research initiative to investigate the potential application of artificial intelligence package CLIPS (short for C Language Integrated Production System, developed at NASA/Johnson Space Center) in the development of an expert decision support system for management of a water distribution network. The system aims to meet several concerns of modern water utility managers as it attempts to formalize operational and management experiences, and provides a frame work for assisting water utility managers even in the absence of expert personnel.

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Murlidharan, T. L., H. B. Aravind, G. V. Suryakumar, and N. V. Raman. "Expert Tower Analysis and Design System. I: Architecture and Heuristics." Journal of Computing in Civil Engineering 5, no. 2 (April 1991): 175–92. http://dx.doi.org/10.1061/(asce)0887-3801(1991)5:2(175).

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Gilbert-Saad, Antoine, Rod B. McNaughton, and Frank Siedlok. "Inexperienced decision-makers' use of positive heuristics for marketing decisions." Management Decision 59, no. 7 (May 11, 2021): 1706–27. http://dx.doi.org/10.1108/md-09-2019-1330.

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PurposeResearch has reliably demonstrated that decision-makers, especially expert ones, use heuristics to make decisions under uncertainty. However, whether decision-makers with little or no experience also do, and if so, how? is unknown. This research addresses this issue in the marketing context by studying how a group of young and generally inexperienced entrepreneurs decide when asked to set a price and choose a distribution channel in a scenario involving a hypothetical firm.Design/methodology/approachThe authors used think-aloud protocols to elicit data and then used inductive procedures to code the data for analysis.FindingsThe inexperienced entrepreneurs in the sample used three types of heuristics in their decision-making, forming a structured process that narrows in scope. First, metacognitive heuristics, which specify a decision-making approach, were used, followed by heuristics representing the criteria they considered, and finally, heuristics detailing the execution of a selected option. The authors also found that heuristics relating to a market orientation, especially customer-centric criteria, were the most common, but these were balanced with ones representing an internal orientation or growth.Research limitations/implicationsThe generally inexperienced decision-makers the authors’ studied used heuristics in a structured way that helped them to select and balance several potentially conflicting decision-making criteria. As with most research using qualitative research designs, the generalizability of these findings is unclear. Further research on the mechanisms by which relatively inexperienced decision-makers learn the heuristics they use is recommended.Originality/valueThis research's novelty lies in its focus on heuristic use by nonexpert decision-makers under conditions of uncertainty and the findings about their scope and the order they are used. As the authors collected data from think-aloud protocols with relatively young entrepreneurs with limited experience, they also offer a description of the heuristics used by nascent entrepreneurs when making marketing decisions about pricing and channels. The most surprising conclusion is that even without relevant domain-specific knowledge, decision-makers can use heuristics in an ecologically rational way (i.e. structured to match the environment).

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Vries, A. De. "Uncertainty in Expert Policy Advice: Variation in Heuristics for Dealing With Uncertainy in Dutch Expert Institutes." Epidemiology 17, Suppl (November 2006): S527. http://dx.doi.org/10.1097/00001648-200611001-01419.

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Gursoz, E. L., and F. B. Prinz. "The Use of Robotics and Expert Systems for the Manufacture of Structural Beams." Journal of Offshore Mechanics and Arctic Engineering 110, no. 4 (November 1, 1988): 330–36. http://dx.doi.org/10.1115/1.3257070.

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This paper describes an expert system for a class of automated cutting operations which are of practical use in the offshore manufacturing industry. These operations include plasma cutting, oxy-fuel cutting, laser cutting and water-jet cutting. The common features in these processes, which define this class of cutting operations, are: 1) the work material is cut by the sweeping action of a line segment; 2) the cutting effect terminates at an imprecise point along the cutting segment; 3) the cutting task at hand can be fully described by the surface-boundary representation of the workpiece and the surface to be cut. The fundamental problem in such a planning task is that neither a strictly geometrical analysis, nor a purely heuristic approach is sufficient when considered alone. In this paper, we present a knowledge-based system which blends heuristics with spatial reasoning within the framework of a solid modeling system.

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MEHREZ, A. "RULE-BASED EXPERT SYSTEM VERSUS NOVICES' HEURISTICS: A MATCHING IDENTIFICATION PROBLEM." Psychological Reports 82, no. 3 (1998): 1423. http://dx.doi.org/10.2466/pr0.82.3.1423-1431.

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Yilmaz, Seda, and Colleen M. Seifert. "Creativity through design heuristics: A case study of expert product design." Design Studies 32, no. 4 (July 2011): 384–415. http://dx.doi.org/10.1016/j.destud.2011.01.003.

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Andreev, G. N., and O. K. Argirov. "Implementation of human expert heuristics in computer supported infrared spectra interpretation." Journal of Molecular Structure 347 (March 1995): 439–48. http://dx.doi.org/10.1016/0022-2860(95)08566-e.

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Prechelt, Lutz, and Alexander Pepper. "Bflinks: Reliable Bugfix Links via Bidirectional References and Tuned Heuristics." International Scholarly Research Notices 2014 (October 29, 2014): 1–13. http://dx.doi.org/10.1155/2014/701357.

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Background. Data from software version archives and defect databases can be used for defect insertion circumstance analysis and defect prediction. The first step in such analyses is identifying defect-correcting changes in the version archive (bugfix commits) and enriching them with additional metadata by establishing bugfix links to corresponding entries in the defect database. Candidate bugfix commits are typically identified via heuristic string matching on the commit message. Research Questions. Which filters could be used to obtain a set of bugfix links? How to tune their parameters? What accuracy is achieved? Method. We analyze a modular set of seven independent filters, including new ones that make use of reverse links, and evaluate visual heuristics for setting cutoff parameters. For a commercial repository, a product expert manually verifies over 2500 links to validate the results with unprecedented accuracy. Results. The heuristics pick a very good parameter value for five filters and a reasonably good one for the sixth. The combined filtering, called bflinks, provides 93% precision and only 7% results loss. Conclusion. Bflinks can provide high-quality results and adapts to repositories with different properties.

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Alqurni, Jehad, Roobaea Alroobaea, and Mohammed Alqahtani. "Effect of User Sessions on the Heuristic Usability Method." International Journal of Open Source Software and Processes 9, no. 1 (January 2018): 62–81. http://dx.doi.org/10.4018/ijossp.2018010104.

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Heuristic evaluation (HE) is a widely used method for assessing software systems. Several studies have sought to improve the effectiveness of HE by developing its heuristics and procedures. However, few studies have involved the end-user, and to the best of the authors' knowledge, no HE studies involving end-users with non-expert evaluators have been reported. Therefore, the aim of this study is to investigate the impact of end-users on the results obtained by a non-expert evaluator within the HE process, and through that, to explore the number of usability problems and their severity. This article proposes introducing two sessions within the HE process: a user exploration session (UES-HE) and a user review session (URS-HE). The outcomes are compared with two solid benchmarks in the usability-engineering field: the traditional HE and the usability testing (UT) methods. The findings show that the end-user has a significant impact on non-expert evaluator results in both sessions. In the UES-HE method, the results outperformed all usability evaluation methods (UEMs) regarding the usability problems identified, and it tended to identify more major, minor, and cosmetic problems than other methods.

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Mannino, Michael V., and Vijay S. Mookerjee. "Optimizing Expert Systems: Heuristics for Efficiently Generating Low-Cost Information Acquisition Strategies." INFORMS Journal on Computing 11, no. 3 (August 1999): 278–91. http://dx.doi.org/10.1287/ijoc.11.3.278.

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Eccles, David W., Susanne E. Walsh, and David K. Ingledew. "The use of heuristics during route planning by expert and novice orienteers." Journal of Sports Sciences 20, no. 4 (January 2002): 327–37. http://dx.doi.org/10.1080/026404102753576107.

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Pjesivac, Ivanka, Nicholas Geidner, and Jaclyn Cameron. "Social credibility online: The role of online comments in assessing news article credibility." Newspaper Research Journal 39, no. 1 (March 2018): 18–31. http://dx.doi.org/10.1177/0739532918761065.

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This 2 × 2 experimental study (N = 196) tested the effects of source expertise and opinion valence in readers’ comments on the credibility of an online news story about genetically modified organisms (GMOs). Source expertise had a significant influence on perceptions of article credibility; articles were judged more credible when public comments embedded in the story were from expert sources (e.g., scientists) rather than nonexpert sources (e.g., Twitter users). Effects were larger on high-frequency news users, regardless of whether comments were for or against GMOs. Results suggest that Internet users mainly use the peripheral or heuristic route of information processing to evaluate online news credibility. The importance for online journalism of social heuristics via opinions of other people is discussed.

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Mtebe, Joel S. "Examining user experience of eLearning systems implemented in two universities in Tanzania." Interactive Technology and Smart Education 17, no. 1 (September 27, 2019): 39–55. http://dx.doi.org/10.1108/itse-05-2019-0025.

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Purpose This study aims to investigate the factors that influence user experience (UX) of eLearning systems implemented in two institutions in Tanzania. Design/methodology/approach The study adopted questionnaire consisting of Nielsen’s heuristics and didactic metrics as pragmatic metrics and hedonic metrics followed by focus group discussions with students. Findings The study found that the eLearning system of University of Dar es Salaam had 43 UX problems related to Nielsen’s heuristics and 54 UX problems related to didactic heuristics. The eLearning system of the Open University of Tanzania had 50 UX problems related to Nielsen’s heuristics and 59 UX problems related to didactic heuristics. Moreover, the two systems provided positive UX hedonic quality on identification and evocation dimensions while stimulation was perceived to be neutral. Research limitations/implications The study has used learners as evaluators rather than expert evaluators. Learners are not particularly experienced in the learning domain, and therefore, it is difficult for them to identify many didactic violations of the eLearning systems. Originality/value The study contributes toward finding the underlying factors for non-use or underuse of the installed eLearning systems in various universities in sub-Saharan Africa.

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Perez, Oren. "Judicial Strategies for Reviewing Conflicting Expert Evidence: Biases, Heuristics, and Higher-Order Evidence." American Journal of Comparative Law 64, no. 1 (March 28, 2016): 75–120. http://dx.doi.org/10.5131/ajcl.2016.0003.

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Schrempf, Patrick, Hannah Watson, Eunsoo Park, Maciej Pajak, Hamish MacKinnon, Keith W. Muir, David Harris-Birtill, and Alison Q. O’Neil. "Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports." Machine Learning and Knowledge Extraction 3, no. 2 (March 24, 2021): 299–317. http://dx.doi.org/10.3390/make3020015.

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Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

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BILLHARDT, HOLGER, DANIEL BORRAJO, and VICTOR MAOJO. "LEARNING RETRIEVAL EXPERT COMBINATIONS WITH GENETIC ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 01 (February 2003): 87–113. http://dx.doi.org/10.1142/s0218488503001965.

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The goal of information retrieval (IR) is to provide models and systems that help users to identify the relevant documents to their information needs. Extensive research has been carried out to develop retrieval methods that solve this goal. These IR techniques range from purely syntax-based, considering only frequencies of words, to more semantics-aware approaches. However, it seems clear that there is no single method that works equally well on all collections and for all queries. Prior work suggests that combining the evidence from multiple retrieval experts can achieve significant improvements in retrieval effectiveness. A common problem of expert combination approaches is the selection of both the experts to be combined and the combination function. In most studies the experts are selected from a rather small set of candidates using some heuristics. Thus, only a reduced number of possible combinations is considered and other possibly better solutions are left out. In this paper we propose the use of genetic algorithms to find a suboptimal combination of experts for a document collection at hand. Our approach automatically determines both the experts to be combined and the parameters of the combination function. Because we learn this combination for each specific document collection, this approach allows us to automatically adjust the IR system to specific user needs. To learn retrieval strategies that generalize well on new queries we propose a fitness function that is based on the statistical significance of the average precision obtained on a set of training queries. We test and evaluate the approach on four classical text collections. The results show that the learned combination strategies perform better than any of the individual methods and that genetic algorithms provide a viable method to learn expert combinations. The experiments also evaluate the use of a semantic indexing approach, the context vector model, in combination with classical word matching techniques.

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Borodin, A., R. El-Yaniv, and V. Gogan. "Can We Learn to Beat the Best Stock." Journal of Artificial Intelligence Research 21 (May 1, 2004): 579–94. http://dx.doi.org/10.1613/jair.1336.

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A novel algorithm for actively trading stocks is presented. While traditional expert advice and ``universal'' algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can ``beat the market'' and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.

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Stuart, Neil A., David M. Schultz, and Gary Klein. "Maintaining the Role of Humans in the Forecast Process: Analyzing the Psyche of Expert Forecasters." Bulletin of the American Meteorological Society 88, no. 12 (December 1, 2007): 1893–98. http://dx.doi.org/10.1175/bams-88-12-1893.

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The Second Forum on the Future Role of the Human in the Forecast Process occurred on 2–3 August 2005 at the American Meteorological Society's Weather Analysis and Forecasting Conference in Washington, D.C. The forum consisted of three sessions. This paper discusses the second session, featuring three presentations on the cognitive and psychological aspects of expert weather forecasters. The first presentation discussed the learning gap between students (goal seekers) and teachers (knowledge seekers)—a similar gap exists between forecasters and researchers. In order to most effectively train students or forecasters, teachers must be able to teach across this gap using some methods described within. The second presentation discussed the heuristics involved in weather forecasting and decision making under time constraints and uncertainty. The final presentation classified the spectrum of forecasters from intuitive scientists to the disengaged. How information technology can best be adapted so as not to inhibit intuitive scientists from their mental modeling of weather scenarios is described. Forecasters must continuously refine their skills through education and training, and be aware of the heuristic contributions to the forecast process, to maintain expertise and have the best chance of ensuring a dynamic role in the future forecast process.

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Baguley, Thom, and S. Ian Robertson. "Where does fast and frugal cognition stop? The boundary between complex cognition and simple heuristics." Behavioral and Brain Sciences 23, no. 5 (October 2000): 742–43. http://dx.doi.org/10.1017/s0140525x0023344x.

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Simple heuristics that make us smart presents a valuable and valid interpretation of how we make fast decisions particularly in situations of ignorance and uncertainty. What is missing is how this intersects with thinking under even greater uncertainty or ignorance, such as novice problem solving, and with the development of expert cognition.

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Ellsworth, R., A. Parkinson, and F. Cain. "The Complementary Roles of Knowledge-Based Systems and Numerical Optimization in Engineering Design Software." Journal of Mechanisms, Transmissions, and Automation in Design 111, no. 1 (March 1, 1989): 100–103. http://dx.doi.org/10.1115/1.3258951.

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In many engineering design problems, the designer converges upon a good design by iteratively evaluating a mathematical model of the design problem. The trial-and-error method used by the designer to converge upon a solution may be complex and difficult to capture in an expert system. It is suggested that in many cases, the design rule base could be made significantly smaller and more maintainable by using numerical optimization methods to identify the best design. The expert system is then used to define the optimization problem and interpret the solution, as well as to apply the true heuristics to the problem. An example of such an expert system is presented for the design of a valve anticavitation device. Because of the capabilities provided by the optimization software, the expert system has been able to outperform the expert in the test cases evaluated so far.

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Elkin, Peter, Daniel Schlegel, Michael Anderson, Jordan Komm, Gregoire Ficheur, and Leslie Bisson. "Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support." Applied Clinical Informatics 09, no. 02 (April 2018): 432–39. http://dx.doi.org/10.1055/s-0038-1656547.

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AbstractEvoking strength is one of the important contributions of the field of Biomedical Informatics to the discipline of Artificial Intelligence. The University at Buffalo's Orthopedics Department wanted to create an expert system to assist patients with self-diagnosis of knee problems and to thereby facilitate referral to the right orthopedic subspecialist. They had two independent sports medicine physicians review 469 cases. A board-certified orthopedic sports medicine practitioner, L.B., reviewed any disagreements until a gold standard diagnosis was reached. For each case, the patients entered 126 potential answers to 26 questions into a Web interface. These were modeled by an expert sports medicine physician and the answers were reviewed by L.B. For each finding, the clinician specified the sensitivity (term frequency) and both specificity (Sp) and the heuristic evoking strength (ES). Heuristics are methods of reasoning with only partial evidence. An expert system was constructed that reflected the posttest odds of disease-ranked list for each case. We compare the accuracy of using Sp to that of using ES (original model, p < 0.0008; term importance * disease importance [DItimesTI] model, p < 0.0001: Wilcoxon ranked sum test). For patient referral assignment, Sp in the DItimesTI model was superior to the use of ES. By the fifth diagnosis, the advantage was lost and so there is no difference between the techniques when serving as a reminder system.

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Riza, Lala Septem, Anita Dyah Pertiwi, Eka Fitrajaya Rahman, Munir Munir, and Cep Ubad Abdullah. "Question Generator System of Sentence Completion in TOEFL Using NLP and K-Nearest Neighbor." Indonesian Journal of Science and Technology 4, no. 2 (September 1, 2019): 294–311. http://dx.doi.org/10.17509/ijost.v4i2.18202.

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Test of English as a Foreign Language (TOEFL) is one of learning evaluation forms that requires excellent quality of questions. Preparing TOEFL questions using a conventional way certainly spends a lot of time. Computer technology can be used to solve the problem. Therefore, this research was conducted in order to solve the problem of making TOEFL questions with sentence completion type. The built system consists of several stages: (1) input data collection from foreign media news sites with excellent English grammar quality; (2) preprocessing with Natural Language Processing (NLP); (3) Part of Speech (POS) tagging; (4) question feature extraction; (5) separation and selection of news sentences; (6) determination and value collection of seven features; (7) conversion of categorical data value; (8) target classification of blank position word with K-Nearest Neighbor (KNN); (9) heuristic determination of rules from human experts; and (10) options selection or distraction based on heuristic rules. After conducting the experiment on 10 news, it is obtained that 20 questions based on the results of the evaluation showed that the generated questions had a very good quality with percentage of 81.93% (after the assessment by the human expert), and 70% was the same blank position from the historical data of TOEFL questions. So, it can be concluded that the generated question has the following characteristics: the quality of the result follows the data training from the historical TOEFL questions, and the quality of the distraction is very good because it is derived from the heuristics of human experts.

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Escobar, José-Pablo, Camila Castro, Marion Garolera, Angélica Sepúlveda, Martín Santa Cruz, and Ricardo Rosas. "Testing of a Drawing Toy for Children with Blindness: The Kuwu Experience." International Journal of Early Childhood Special Education 13, no. 1 (May 5, 2021): 71–79. http://dx.doi.org/10.9756/int-jecse/v13i1.211009.

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Play is a fundamental activity in early childhood and a source of cognitive development. One ludic activity in early childhood is drawing, a semiotic activity in which children can build and transform symbols and meanings in a way to reflect their thoughts and emotions. However, drawing has a visual component that is a challenge for some children with blindness or visual impairment. This research aims to evaluate a prototype of a haptic pencil called Kuwu. Through user’s usability tests and expert heuristic inspection, we evaluated 10 children with blindness or visual impairments between 5 and 8 years old, from 2 schools of Santiago, Chile. Heuristics evaluation shows the minimalist design, ease, and flexibility of use of the pencil that allows children to draw independently. Usability testing is a foundational phase in any technological development, especially when creating from a universal design perspective.

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Ales, Zacharie, Alexandre Pauchet, and Arnaud Knippel. "Extraction and Clustering of Two-Dimensional Dialogue Patterns." International Journal on Artificial Intelligence Tools 27, no. 02 (March 2018): 1850001. http://dx.doi.org/10.1142/s021821301850001x.

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This article proposes a two-step methodology to ease the identification of dialogue patterns in a corpus of annotated dialogues. The annotations of a given dialogue are represented within a two-dimensional array whose lines correspond to the utterances of the dialogue ordered chronologically. The first step of our methodology consists in extracting recurrent patterns. To that end, we adapt a dynamic programming algorithm used to align two-dimensional arrays by reducing its complexity and improving its trace-back procedure. During the second step, the obtained patterns are clustered using various heuristics from the literature. As evaluation process, our method is applied onto a corpus of annotated dialogues between a parent and her child in a storytelling context. The obtained partitions of dialogue patterns are evaluated by an expert in child development of language to assess how the methodology helps the expert into explaining the child behaviors. The influence of the method parameters (clustering heuristics, minimum extraction score, number of clusters and substitution score array) are studied. Dialogue patterns that manual extractions have failed to detect are highlighted by the method and the most efficient values of the parameters are therefore determined.

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Sayed, Biju Theruvil. "APPLICATION OF EXPERT SYSTEMS OR DECISION-MAKING SYSTEMS IN THE FIELD OF EDUCATION." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 18, 2021): 1396–405. http://dx.doi.org/10.17762/itii.v9i1.283.

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Expert system (ES) is a branch of artificial intelligence (AI) that is used to manage different problems by making use of interactive computer-based decision-making process. It uses both factual information and heuristics to resolve the complicated decision-making issues in a specific domain. The architecture of the expert system was analyzed and found that it includes several parts such as user interface, knowledge base, working memory, inference engine, explanation system, system engineer, and knowledge engineer, user, and expert system shell in which each part of the architecture of an expert system is based on different functionary that helps it to make an adequate decision by analyzing complex situations. The research aims to analyze the application of expert systems or decision-making systems in the field of education and found that it is used for different purposes such as assessing teacher performance, providing guidance to the students regarding their career, and providing quality learning to students with disabilities. It is also used to help the students to make rightful career decisions and become efficient professionals after completing their studies.

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Atanasiu, Radu. "The lifecycle of heuristics as managerial proverbs." Management Decision 59, no. 7 (June 4, 2021): 1617–41. http://dx.doi.org/10.1108/md-08-2019-1025.

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PurposeThis paper proposes a theory-based process model for the generation, articulation, sharing and application of managerial heuristics, from their origin as unspoken insight, to proverbialization, to formal or informal sharing, and to their adoption as optional guidelines or policy.Design/methodology/approachA conceptual paper is built using systematic and non-systematic review of literature. This paper employs a three-step approach to propose a process model for the emergence of managerial heuristics. Step one uses a systematic review of empirical studies on heuristics in order to map extant research on four key criteria and to obtain, by flicking through this sample in a moving-pictures style, the static stages of the process; step two adapts a knowledge management framework to yield the dynamic aspect; step three assembles these findings into a graphical process model and uses insights from literature to enrich its description and to synthesize four propositions.FindingsThe paper provides insights into how heuristics originate from experienced managers confronted with negative situations and are firstly expressed as an inequality with a threshold. Further articulation is done by proverbialization, refining and adapting. Sharing is done either in an informal way, through socialization, or in a formal way, through regular meetings. Soft adoption as guidelines is based on expert authority, while hard adoption as policy is based on hierarchical authority or on collective authority.Research limitations/implicationsThe findings are theory-based, and the model must be empirically refined.Practical implicationsPractical advice for managers on how to develop and share their portfolio of heuristics makes this paper valuable for practitioners.Originality/valueThis study addresses the less-researched aspect of heuristics creation, transforms static insights from literature into a dynamic process model, and, in a blended-theory approach, considers insights from a distant, but relevant literature – paremiology (the science of proverbs).

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Jiang, Qing, Hai Ying Hu, Xiu Fang Jia, Cui Ping Lu, and Ru Jing Wang. "A Hybrid Knowledge-Base System Approach for Disaster Emergency and Relief Decision." Advanced Materials Research 791-793 (September 2013): 2234–37. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2234.

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Hybrid knowledge representation schemes, including production rules, object-oriented programming, and procedural methods, are employed to express expert heuristics and standard emergency knowledge during the development of the knowledge-based system (KBS) for emergency decision for disaster reduction. This approach renders it possible to take advantages of the characteristics of each method. The system can provide the user with advice on Preliminary plan evaluation, Plan optimization, Plan evaluation ,Plan summary and Miscellaneous.

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Murray, John, and Yili Liu. "Operator Support in Traffic Management: A Heuristics Model and Experimental Study." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 18 (October 1996): 872–76. http://dx.doi.org/10.1177/154193129604001801.

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The identification of problems from numeric traffic measurements is an important part of control center activities in ATMS (Advanced Traffic Management Systems). However, an information modeling process that relies solely upon ‘traditional’ quantitative data analysis does not reflect faithfully the actual methods used by human operators. In addition to common-sense knowledge and specific contextual information, operators also use various heuristics and rules-of-thumb to supplement the numerical analysis. This paper describes an experiment to examine the effectiveness of an expert system that integrates quantitative and qualitative traffic information using a human-centered knowledge system design. The system's performance was investigated using a data suite of real traffic scenarios; the statistically significant results showed that the integrated process had superior performance compared to the ‘traditional’ quantitative analysis running alone.

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Pham, D. T., and E. Oztemel. "An Integrated Neural Network and Expert System Tool for Statistical Process Control." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 209, no. 2 (February 1995): 91–97. http://dx.doi.org/10.1243/pime_proc_1995_209_060_02.

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Control charts are a basic means for monitoring the quality characteristics of a manufacturing process to ensure the required quality level. They are used to track product and process variations through graphical representation of the quality variable of interest. A control chart shows the state of control of a process and can exhibit different types of patterns which are indicative of long-term trends in it. This paper describes the integration of an expert system and a neural-network-based pattern recognizer for analysing and interpreting control charts. The expert system has an on-line process monitoring package to detect general out-of-control situations and a diagnosis module to suggest corrective actions. The pattern recognizer is an on-line system comprising two neural networks and an heuristics module designed to identify incipient process abnormalities from control chart patterns. The paper also compares neural networks and expert systems and provides the rationale for the integration process.

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Mak, Brenda, and Toshinori Munakata. "Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3." European Journal of Operational Research 136, no. 1 (January 2002): 212–29. http://dx.doi.org/10.1016/s0377-2217(01)00062-5.

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MacMillan, Jean, Michael J. Paley, Daniel Serfaty, and Yuri N. Levchuk. "Designing the Best Team for the Task: A Method that Combines Algorithms, Heuristics, and Expert Judgment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 43, no. 3 (September 1999): 298–302. http://dx.doi.org/10.1177/154193129904300336.

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LEE, CHUNG-MONG, TING-CHUEN PONG, and JAMES R. SLAGLE. "A KNOWLEDGE-BASED SYSTEM FOR THE IMAGE CORRESPONDENCE PROBLEM." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (March 1990): 45–55. http://dx.doi.org/10.1142/s0218001490000046.

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The image correspondence problem has generally been considered the most difficult step in both stereo and temporal vision. Most existing approaches match area features or linear features extracted from an image pair. The approach described in this paper is novel in that it uses an expert system shell to develop an image correspondence knowledge-based system for the general image correspondence problem. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions in images for every edge or region matching. A computation network is used to represent this knowledge. It allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. Heuristics for determining the correspondences between image features and the problem of handling missing information will be discussed. The values of the individual matching results are used to direct the traversal and pruning of the global matching process. The problem of parallelizing the entire process will be discussed. Experimental results on real-world images show that all matching edges and regions have been identified correctly.

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McDougall, William A., Thomas L. Seamster, and Eleana S. Edens. "Instructor Strategies in the Assessment of Aircrews: Heuristics in CRM Assessment and Their Operational Implications." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 1 (October 1993): 127–31. http://dx.doi.org/10.1177/154193129303700130.

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Established Crew Resource Management (CRM) research has concentrated on the analysis of actual crew behaviors and crew training. As CRM research and training matures, assessment issues take on increasing importance. Current research emphasis has broadened and shifted focus from crew training methodologies in recognition of the importance of the assessment process. The identification of the strategies that experienced instructors employ in efficient CRM assessment is essential to a better understanding of the assessment process. These learned strategies help experienced instructors focus on key crew behaviors in complex Line-Oriented Flight Training (LOFT). This present research was designed to identify these strategies employed by experienced instructors as part of a larger effort to develop a prototype CRM assessment expert system. Protocol analysis techniques were used to develop a set of assessment rules for the prototype expert system. The experienced instructor protocols were translated into a set of pseudo-code rules. The lower-level rules, derived from the experienced instructor protocols, were analyzed and abstracted into a set of general strategies. These are the strategies that the experienced instructor has learned to use to help focus on the key crew behaviors in the complex Line-Oriented Flight Training (LOFT) scenario environment where the instructor cannot observe all behaviors. These strategies have a number of implications for training new instructor/assessors, the standardization and calibration of LOFT assessors, and for achieving more systematic CRM assessments.

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Lajoie, Susanne P., Roger Azevedo, and David M. Fleiszer. "Cognitive Tools for Assessment and Learning in a High Information Flow Environment." Journal of Educational Computing Research 18, no. 3 (April 1998): 205–35. http://dx.doi.org/10.2190/ltrw-br1g-b75l-3f65.

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A simulation-based intelligent tutoring system (ITS) was designed for nurses working in a Surgical Intensive Care Unit (SICU). A cognitive task analysis approach was used to identify the cognitive components of clinical decision making of “expert” surgical nurses. Quantitative analyses revealed that expert nurses reached the same decisions. However, a qualitative analysis of the verbal protocols revealed great variability in how the nurses arrived at their clinical decisions. Differences were observed in: hypothesis generation, planning of medical interventions, actions performed, results of evidence gathering, interpretation of the results, heuristics, and the overall solution paths. The results of these analyses were used to design a prototype ITS. The tutoring environment (SICUN) is described in terms of the cognitive tools it provides, and the assessment opportunities it presents. Implications for the evaluation of this system are discussed.

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Read, Stuart, Nicholas Dew, Saras D. Sarasvathy, Michael Song, and Robert Wiltbank. "Marketing under Uncertainty: The Logic of an Effectual Approach." Journal of Marketing 73, no. 3 (May 2009): 1–18. http://dx.doi.org/10.1509/jmkg.73.3.001.

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How do people approach marketing in the face of uncertainty, when the product, the market, and the traditional details involved in market research are unknowable ex ante? The authors use protocol analysis to evaluate how 27 expert entrepreneurs approach such a problem compared with 37 managers with little entrepreneurial expertise (all 64 participants are asked to think aloud as they make marketing decisions in exactly the same unpredictable situation). The hypotheses are drawn from literature in cognitive science on (1) expertise in general and (2) entrepreneurial expertise in particular. The results show significant differences in heuristics used by the two groups. While those without entrepreneurial expertise rely primarily on predictive techniques, expert entrepreneurs tend to invert these. In particular, they use an effectual or nonpredictive logic to tackle uncertain market elements and to coconstruct novel markets with committed stakeholders.

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White, Nicola, Priscilla Harries, Adam JL Harris, Victoria Vickerstaff, Philip Lodge, Catherine McGowan, Ollie Minton, et al. "44 An evidenced-based heuristics model (or rule of thumb) to improve doctors’ intuition about when patients are imminently dying." BMJ Supportive & Palliative Care 8, no. 3 (September 2018): 376.2–376. http://dx.doi.org/10.1136/bmjspcare-2018-mariecurie.44.

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IntroductionEvidence suggests that the majority of doctors are not very good at identifying when a patient is dying1 however there is little training available to improve this skill. Even experts are unable to articulate how they recognise when a patient is dying other than by saying that ‘I just knew’.2AimTo understand how expert palliative care doctors recognise a dying person.MethodsRather than relying on ‘years of experience’ as a surrogate measure of expertise we developed a test to identify which doctors really are the prognostic ‘experts’. The prognostic test consisted of 20 real patient case summaries. Participants (palliative care doctors) were asked to predict whether or not they expected the patient to die within the next 3 days. Those who were the most accurate at this task were deemed to be the ‘prognostic experts’ and were invited to complete an additional online judgement task. In this task it was possible to identify which factors were most influential in their prognostic decision-making.Results19/99 doctors who completed the prognostic test were deemed to be ‘experts’. Of those 14 also completed the additional judgement task. The following factors influenced the experts’ decisions: Cheyne Stokes breathing palliative performance score (PPS) a decline in condition in the previous 24 hours respiratory secretions cyanosis and level of agitation or sedation.ConclusionThis novel study presents a simple evidenced-based heuristic (or rule of thumb) to help novices recognise when a patient is imminently dying.References. Neuberger J, Guthrie C, Aaronovitch D. More care less pathway: A review of the Liverpool Care Pathway. Department of Health2013.. Taylor P, Dowding D, Johnson M. Clinical decision making in the recognition of dying: A qualitative interview study. BMC Palliative Care2017;16(1):11.

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Mohan, Deepika, Baruch Fischhoff, Derek C. Angus, Matthew R. Rosengart, David J. Wallace, Donald M. Yealy, Coreen Farris, Chung-Chou H. Chang, Samantha Kerti, and Amber E. Barnato. "Serious games may improve physician heuristics in trauma triage." Proceedings of the National Academy of Sciences 115, no. 37 (August 27, 2018): 9204–9. http://dx.doi.org/10.1073/pnas.1805450115.

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Trauma triage depends on fallible human judgment. We created two “serious” video game training interventions to improve that judgment. The interventions’ central theoretical construct was the representativeness heuristic, which, in trauma triage, would mean judging the severity of an injury by how well it captures (or “represents”) the key features of archetypes of cases requiring transfer to a trauma center. Drawing on clinical experience, medical records, and an expert panel, we identified features characteristic of representative and nonrepresentative cases. The two interventions instantiated both kinds of cases. One was an adventure game, seeking narrative engagement; the second was a puzzle-based game, emphasizing analogical reasoning. Both incorporated feedback on diagnostic errors, explaining their sources and consequences. In a four-arm study, they were compared with an intervention using traditional text-based continuing medical education materials (active control) and a no-intervention (passive control) condition. A sample of 320 physicians working at nontrauma centers in the United States was recruited and randomized to a study arm. The primary outcome was performance on a validated virtual simulation, measured as the proportion of undertriaged patients, defined as ones who had severe injuries (according to American College of Surgeons guidelines) but were not transferred. Compared with the control group, physicians exposed to either game undertriaged fewer such patients [difference = −18%, 95% CI: −30 to −6%, P = 0.002 (adventure game); −17%, 95% CI: −28 to −6%, P = 0.003 (puzzle game)]; those exposed to the text-based education undertriaged similar proportions (difference = +8%, 95% CI: −3 to +19%, P = 0.15).

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Hester, Patrick. "Epistemic Uncertainty Analysis: An Approach Using Expert Judgment and Evidential Credibility." International Journal of Quality, Statistics, and Reliability 2012 (November 8, 2012): 1–8. http://dx.doi.org/10.1155/2012/617481.

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When dealing with complex systems, all decision making occurs under some level of uncertainty. This is due to the physical attributes of the system being analyzed, the environment in which the system operates, and the individuals which operate the system. Techniques for decision making that rely on traditional probability theory have been extensively pursued to incorporate these inherent aleatory uncertainties. However, complex problems also typically include epistemic uncertainties that result from lack of knowledge. These problems are fundamentally different and cannot be addressed in the same fashion. In these instances, decision makers typically use subject matter expert judgment to assist in the analysis of uncertainty. The difficulty with expert analysis, however, is in assessing the accuracy of the expert's input. The credibility of different information can vary widely depending on the expert’s familiarity with the subject matter and their intentional (i.e., a preference for one alternative over another) and unintentional biases (heuristics, anchoring, etc.). This paper proposes the metric of evidential credibility to deal with this issue. The proposed approach is ultimately demonstrated on an example problem concerned with the estimation of aircraft maintenance times for the Turkish Air Force.

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Grabowski, Martha. "Decision Support to Masters, Mates on Watch, and Pilots: The Piloting Expert System." Journal of Navigation 43, no. 03 (September 1990): 364–84. http://dx.doi.org/10.1017/s0373463300014004.

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Piloting large vessels in increasingly congested waterways is no simple task. As in many ‘decision-making under uncertainty’ scenarios, masters, mates and pilots engaged in piloting are inundated with much information and required to make crucial decisions in real time. Piloting is also an inherently judgmental activity. Pilots and ships' captains invariably develop heuristics for transiting particular waterways. As vessels become larger, cargoes more hazardous, and the waterways more congested, decision aid technology is being considered to improve piloting decision-making. This paper describes one approach to providing improved on-board decision support to masters, mates on watch, and pilots navigating in restricted waters. We discuss (1) the use of cognitive decision aids in piloting, (2) the design of such a decision aid developed for New York harbour, (3) simulator experiments evaluating the expert system, and (4) plans to apply the approach and ‘lessons learned’ to the development of an expert system for tankers transiting the Gulf of Alaska.

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Journal articles on the topic 'Expert heuristics'

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