Use Of Knowledge-based Systems Research Articles

The collaborative approaches of the knowledge-based performance management system and lean six sigma to improve contractor productivity and safety performances

Purpose This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities. Design/methodology/approach The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework. Findings The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field. Research limitations/implications The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors. Practical implications This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs. Social implications This paper does not discuss social implications Originality/value This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

РОЗРОБКА ОНТОЛОГІЧНОЇ МОДЕЛІ БАЗИ ЗНАНЬ «БІБЛІОТЕКА» НА БАЗІ СЕРЕДОВИЩА PROTÉGÉ

Modern development of society, increasing the degree and pace of integration of information technology achievements into the sphere of human life, traditional approaches to the construction of information systems cease to be effective or become too cumbersome. The development and use of knowledge-based systems is one way to solve this problem. The material of the work is devoted to the ontological modeling of the new subject area «Library». Ontology is considered in the context of knowledge sharing. The created ontological model of the «Library» knowledge base is quite modern and relevant today. The developed ontological model of the knowledge base in this field can be implemented on thematic web resources and will significantly facilitate the semantic search of information within the subject area in comparison with the existing ones. The terminological dictionary of this subject area is defined using the term system. An analysis of the capabilities of the Protégé ontology development environment for modeling the specific subject area «Library» was carried out. The basic principle of ontology modeling in the form of a semantic network was chosen. The proposed network has the opportunity to expand and deepen knowledge about the subject area «Library». Using the Protégé environment for the implementation of the ontological model of the knowledge base made it possible to use the advantages and features of the created «Library» model, such as: structuredness, completeness, reflexivity, functionality, transitivity, reliability and consistency of information. The criterion for assessing the correctness of the ontological model of the knowledge base was selected and used. Testing of the developed ontological knowledge base was conducted and a fairly high level of its correctness in the process of information search was confirmed. The average indicator (Average) according to the SUM metric, determined for all users, is equal to 83.25%, which is an acceptable indicator of the ontological knowledge base. When using the classic relational model of database organization to implement the «Library» database. the average SUM metric for 10 users is 75.26%. An example of the developed ontology in Protégé is considered, a graphical representation of the basic graph of the ontological model of the «Library» knowledge base is given, the model includes 10 classes and subclasses, 2 properties-relations and up to 10 properties-data are defined for each class and subclass, an example of the classes of the ontological model is given «Library», an example of the "property-relations" of the ontological model «Library» is given, an example of the "property-data" of the ontological model «Library» is given. Possible directions of further development of the ontological model «Library» have been determined.

The development of a smarter mode of transportation using ai to reduce travel and waiting times

Smarter mode of transportation (SMT) rely heavily on real-time traffic management. Safety on the roads is only one of many benefits brought forth by advancements in dynamic traffic management systems for congested metropolitan areas. In this study, we present the design and implementation of a Bayesian Belief Networks (BBN) traffic control system that is both flexible and reliable. The use of knowledge-based systems as a framework for making decisions in real-time has gained widespread acceptance. Since traditional dynamic controllers relied on sensors with their own set of drawbacks, we may employ vision sensors (such as cameras) to get around these problems. Computing based on images and videos is very useful for gauging traffic volumes. The present traffic management system at the road junction was found wanting, thus a new system was developed and put into place to alleviate the congestion. Lab VIEW and MATLAB are used to measure how well the suggested framework works. Extensive simulations utilizing the suggested method show that it reduces waiting time and speeds up movement on average compared to controllers employing traditional sensors.

KSN: Modeling and simulation of knowledge using machine learning in NFV/SDN-based networks

The rapid advancement of networking and computing technologies has led to the emergence of a wide range of diverse dynamic network services that present networks with new challenges, including in the provision and management of services. Recently, SDN and NFV technologies can be effective solutions in the dynamic management of network functions. For dealing with dynamic and complex problems in the field of NFV and SDN issues, the use of knowledge-based systems can be a good solution that has received more and more attention from researchers and developers. Despite the increasing attention to the use of knowledge such as machine learning and deep learning methods in the NFV and SDN field, the lack of a simulation framework with the ability to knowledge concept to implement and evaluate new methods has created a gap. In order to evaluate a new method on real platforms, a large-scale infrastructure has to be used, which faces challenges such as availability, cost, time and implementation difficulties. Since the existing simulation frameworks for networks using NFV and SDN technologies (e.g. CloudsimSDNNFV) do not support the knowledge-based concept, to fill this gap, we propose a highly extensible, interoperable, and scalable simulation framework for developing knowledge-based methods such as machine learning algorithms in the field of NFV and SDN, such as virtualized network functions auto-scaling, placement and migration. Also, for the proposed Knowledge-defined SDN/NFV (KSN) framework, an architecture is provided by defining the knowledge plane in the SDN/NFV integration structure. In addition, a new auto-scaling method based on the prediction of NFV resources consumption using machine learning has been proposed and simulated to demonstrate the capabilities of the framework and its evaluation. As we know, this is the first machine learning-based method in the NFV field, including VNF auto-scaling, which has been simulated in a network environment, and the impact of using machine learning has been practically evaluated and observed. The evaluation results show that the proposed method has a better impact on the SLA violation rate and resource usage than ElasticSFC and the basic version (without any auto-scaling method) and therefore it can be concluded that resource prediction is an effective and efficient solution for earlier decision making and VNF auto-scaling.

Effects of Demographic Factors for Fatigue Detection in Manufacturing

Over the years, advancement in automation technology is allowed the increased integration of humans and machines in a manufacturing environment, these days fewer humans. The use of Knowledge-based Systems in improving and converting human overall performance has been restrained in truth because of a lack of expertise of the way an individual’s overall performance deteriorates with fatigue buildup, which may range from employees to the work environment. As a result, the performance benefits of increased automation in a manufacturing environment, as well as the impact of human factors, must be taken into account. To predict fatigue in physically demanding tasks, this study takes a data-driven strategy. The influence of demographic characteristics, their physical fatigue states, detected workloads, and reactivity to physiological changes are investigated through sensors (Inertial Measurement Unit; IMU and Heart Rate Variability; HRV) in this paper. A framework is established for the selection of key features, machine learning algorithms, and evaluating subjective measures. To attain that, specific application scenarios of the framework are shown, each for different sorts of manufacturing-related tasks.

Successful Use of Knowledge-Based Systems for Collaboration in Higher Education

Knowledge-based systems are forms of computerized tools designed specially to help in many aspects specifically in collaboration activities. Despite the importance of these tools in generating, exchanging, and sharing the information and knowledge, many institutions, especially educational ones, are not interested in them, because of insufficient knowledge of the role of knowledge-based systems and their effectiveness in the academic events. The basic purposes of this paper are to find the role of knowledge-based systems collaboration tools (KBSCT) in higher educational institutions and to recognize in what way institution and student performance are affected by implementing KBSCT. The research questionnaires distributed to 100 participants, but only 67 were used and included in the frequency analysis. The results added a contribution in terms of identifying how the KBSCT effect on the higher educational institution and student's decisions.

The MATHESIS meta-knowledge engineering framework

The effect of the knowledge acquisition bottleneck is still limiting the widespread use of knowledge-based systems (KBS), especially in the area of model-tracing tutors, as they demand the developm...

The MATHESIS meta-knowledge engineering framework: Ontology-driven development of intelligent tutoring systems

The effect of the knowledge acquisition bottleneck is still limiting the widespread use of knowledge-based systems (KBS), especially in the area of model-tracing tutors, as they demand the development of deep domain expertise, tutoring and student models. The MATHESIS meta-knowledge engineering framework for model-tracing tutors, presented in this article, aims at maximizing knowledge reuse. This is achieved through ontological representation of both the declarative and procedural knowledge of a KBS (model-tracing tutor), as well as of the declarative and procedural authoring knowledge of the process to develop a KBS. Declarative knowledge is represented in Ontology Web Language (OWL). Procedural knowledge is represented using the concepts of atomic and composite processes of OWL-S web services description ontology. The framework provides knowledge engineering tools, integrated into the Protege OWL ontology editor, for the development and management of the KBS's ontological representation. It also provides meta-knowledge engineering tools for the ontological representation of the knowledge engineering expertise as a set of composite knowledge engineering processes and atomic knowledge engineering statements. The latter constitute a language, ONTOMATH, for building executable knowledge engineering models that, when executed by the tools, guide non-expert knowledge engineers like domain experts to the creation of new knowledge-based systems (model-tracing tutors). The framework, being in an experimental stage, was used for the development of a monomial multiplication and division tutor. However, the overall design and implementation aimed at constituting the framework as a proof-of-concept system that can be used for the meta-knowledge engineering of more complex model-tracing tutors.

Chapter 1: Biomedical Knowledge Integration

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

A META-TOOL TO SUPPORT THE DEVELOPMENT OF KNOWLEDGE ENGINEERING METHODOLOGIES AND PROJECTS

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

A survey of AI in operations management from 2005 to 2009

PurposeThe use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, the purpose of this paper is to present a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approachThe paper builds upon our previous survey of this field which was carried out for the ten‐year period 1995‐2004. Like the previous survey, it uses Elsevier's Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case‐based reasoning (CBR), fuzzy logic (FL), knowledge‐Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.FindingsThe survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/valueThis is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

Supporting decision making in urban wastewater systems using a knowledge-based approach

The use of knowledge-based systems has been shown to be a suitable approach to support decision making in environmental systems. Capturing and managing the huge quantity of data/information that has to be considered is an intrinsic factor that makes environmental systems a sophisticated domain. Organizing this data in a naive way can impact the efficacy of any knowledge-based system. Another intrinsic factor is the variety of data sources, which can result in inconsistent, uncertain or incomplete knowledge bases when different data sources are considered. Accordingly, two central issues of a successful knowledge-based system are the organization of its knowledge base and the expressiveness of its specification language. In this paper, we introduce a stratified framework for structuring any environmental knowledge base. We will argue that a declarative specification language, such as Answer Set Programming, is expressive enough to capture environmental knowledge bases that are inconsistent, uncertain and incomplete. We also present an automata-based approach to manage actions in knowledge-based systems. By solving a use case, specifically the diagnosis of the safety of a particular industrial wastewater discharge in an urban wastewater system, we illustrate how to represent relevant abstractions to model related complex processes. We show that by using them it is also possible to automate the diagnosis process (in the present case, for example, to diagnose problems at a wastewater treatment plant and afterward in the river) and hence support the decision-making task.

CommonKADS analysis and description of a knowledge based system for the assessment of breast cancer

A knowledge based system (KBS) has been created in order to provide decision support to the Triple Assessment Process used to diagnose breast cancer. The KBS is implemented using the PROforma knowledge representation language, the user interface is provided by Java Server Pages, Java Servlets, Javascript, and Cascading Style Sheets.In this paper, we describe and analyse the KBS using the CommonKADS methodology. In order to perform this analysis we examine the business processes, personnel, knowledge assets, and other resources used in triple assessment and describe how these would have to change in order to accommodate the use of KBS. We analyse the feasibility of the KBS along three axes: business feasibility, technical feasibility, and project feasibility.We also describe the knowledge that the KBS is required to represent and the reasoning that it is required to perform, as well as the overall design of the system.The analysis reveals that, while it could bring improvements the triple assessment process, the routine use of the KBS would require integration with an Electronic Patient Record System. It also identifies key strengths and weaknesses in PROforma and in the CommonKADS methodology.

Spatial Knowledge-Based Decision Analysis System for Pond Site Assessment

In the South Platte River Basin of Colorado, seasonally flooded wetland ponds are being developed for groundwater recharge and wildlife habitat. The selection and evaluation of pond sites is primarily done by local experts. This study presents an approach that combines the use of knowledge-based systems, multicriterion decision analysis techniques, and geographic information systems to develop a representation of the pond evaluation process. The multicriterion decision analysis technique is used to calculate a surrogate certainty factor; that is, a suitability factor, for processing rules within a forward-chaining knowledge-based system to determine the overall suitability of a specific site. Spreadsheet software was used to implement the rule processing, with site data and spatial analysis results provided through links with a geographic information system. The spreadsheets were designed to provide flexibility for users to change thresholds, rating scales, and relative importance weights. Scenario testing and testing of the system by local experts demonstrate its potential use for pond site evaluations.

A KNOWLEDGE-BASED KALMAN FILTER FOR AN INTELLIGENT PEDESTRIAN NAVIGATION SYSTEM

Continuous and reliable position determination is very important in any navigation application. Therefore a combination and integration of different location techniques and positioning sensors is required. In most navigation applications this integration is performed using a Kalman filter approach. In this paper a new approach which makes use of knowledge-based systems for preprocessing the sensor observations is presented. In the preprocessing step the quality and reliability of the sensor observations is tested and gross errors and outliers are detected and eliminated. Furthermore the preprocessing step is used to determine the weightings of the sensor observations in the stochastic model of the following central Kalman filter. The weightings of the sensor observations can then be adjusted in the filter depending on their availability and quality. This approach is developed in a research project at our University for a pedestrian navigation and guidance service. In this project different location techniques such as GNSS and indoor positioning are combined with dead reckoning sensors (e.g. digital compass for heading determination, accelerometers for measurement of travelled distance, barometric pressure sensor for altitude determination) for continuous position determination of a pedestrian user. The project takes a user case into account, i.e., the navigation and guidance of visitors of our university to certain offices and persons. Selected results of field tests using different sensors are also presented in the paper. From the tests it could be seen that such a service can achieve a high accuracy and reliability for continuous position determination of a pedestrian user. It can also be expected that the performance of the system can be increased using the new intelligent knowledge-based Kalman filter approach for the integration of all available sensor observations.

Could the use of a knowledge-based system lead to implicit learning?

The primary objective of a knowledge-based system (KBS) is to use stored knowledge to provide support for decision-making activities. Empirical studies identify improvements in decision processes and outcomes with the use of such knowledge-based systems. This research suggests that though a KBS is primarily developed to help users in their decision-making activities, as an unintentional consequence, it may induce them to implicitly learn more about a problem. Implicit learning occurs when a person learns unconsciously or unintentionally, without being explicitly instructed or tutored. To test these ideas, a laboratory-based experiment was conducted with a KBS that could provide support for data modeling activities. Results indicated support for implicit learning because subjects who interacted with the KBS exhibited better knowledge on data modeling concepts than those who did not interact with the KBS. Two versions of the KBS were tested, one with a restrictive interface and the other with a guidance interface, and both versions of the interface supported implicit learning. Implications for future research on the design and development of KBSs are proposed.

The differential use and effect of knowledgebased system explanations in novice and expert judgment decisions

Explanation facilities are considered essential in facilitating user interaction with knowledge-based systems (KBS). Research on explanation provision and the impact on KBS users has shown that the domain expertise affects the type of explanations selected by the user and the basis for seeking such explanations. The prior literature has been limited, however, by the use of simulated KBS that generally provide only feedback explanations (i.e., ex post to the recommendation of the KBS being presented to the user). The purpose of this study is to examine the way users with varying levels of expertise use alternative types of KBS explanations and the impact of that use on decision making. A total of 64 partner/ manager-level and 82 senior/staff-level insolvency professionals participated in an experiment involving the use of a fully functioning KBS to complete a complex judgment task. In addition to feedback explanations, the KBS also provided feedforward explanations (i.e., general explanations during user input about the relationships between information cues in the KBS) and included definition type explanations (i.e., declarative-level knowledge). The results show that users were more likely to adhere to recommendations of the KBS when an explanation facility was available. Choice patterns in using explanations indicated that novices used feedforward explanations more than experts did, while experts were more likely than novices to use feedback explanations. Novices also used more declarative knowledge and initial problem solving type explanations, while experts used more procedural knowledge explanations. Finally, use of feedback explanations led to greater adherence to the KBS recommendation by experts-a condition that was even more prevalent as the use of feedback explanations increased. The results have several implications for the design and use of KBS in a professional decision-making environment.

The Differential Use and Effect of Knowledge-Based System Explanations in Novice and Expert Judgment Decisions

Explanation facilities are considered essential in facilitating user interaction with knowledge-based systems (KBS). Research on explanation provision and the impact on KBS users has shown that the domain expertise affects the type of explanations selected by the user and the basis for seeking such explanations. The prior literature has been limited, however, by the use of simulated KBS that generally provide only feedback explanations (i.e., ex post to the recommendation of the KBS being presented to the user). The purpose of this study is to examine the way users with varying levels of expertise use alternative types of KBS explanations and the impact of that use on decision making. Sixty-four partner/manager-level and 82 senior/staff-level insolvency professionals participated in an experiment involving the use of a fully functioning KBS to complete a complex judgment task. In addition to feedback explanations, the KBS also provided feedforward explanations (i.e., general explanations during user input about the relationships between information cues in the KBS) and included definition type explanations (i.e., declarative-level knowledge). The results show that users were more likely to adhere to recommendation of the KBS when an explanation facility was available. Choice patterns in using explanations indicated that novices used feedforward explanations more than experts did, while experts were more likely than novices to use feedback explanations. Novices also used more declarative knowledge and initial problem solving type explanations, while experts used more procedural knowledge explanations. Finally, use of feedback explanations led to greater adherence to the KBS recommendation by experts - a condition that was even more prevalent as the use of feedback explanations increased. The results have several implications for the design and use of KBS in a professional decision making environment.

Analysis of convergent evidence in an evidential reasoning knowledge-based classification

The use of knowledge-based systems (KBSs) that use evidential reasoning for land-cover mapping derived from remotely sensed images is spreading widely. In recent years, KBSs utilizing the Dempster–Shafer Theory of Evidence (D-S ToE) have been found most successful in a wide range of remote sensing applications, partly because of their ability to combine diverse information sources. An important feature of the D-S ToE is that it provides a measure for the evidential support (belief) accumulated for each object class at each pixel. Despite the importance of cumulative belief values (CBVs) in representing the weighting of supportive versus conflicting evidence for each class, their analysis has received little attention in the literature. The objective of the present study was to assess the performance (represented by the kappa coefficient) of a KBS based on D-S ToE and of an unsupervised classification (ISODATA), with relation to the CBV distribution determined for each class. This was done for the task of crop recognition in a wide heterogeneous region in Israel. It was found that while KBS performs very well in cases of conflicts and moderate support, the US classification performed well only in cases of homogeneity and uniqueness. Crop recognition by means of KBS was applied to almost one-third of the country's agricultural areas, and it provided a high level of differentiation among seven crop types, orchards and natural vegetation types.

Modelling Audit Risk Assessments: Exploration of an Alternative to the Use of Knowledge‐based Systems

This paper compares decision‐modelling approaches. The decision modelled is the assessment of inherent and control risk in the purchases, accounts payable and inventory cycle. It is modelled using two different approaches. Firstly, knowledge‐based models are constructed using established development shells. Secondly, a model is constructed using a conventional procedural programming language. Both modelling approaches are tested against the output of human practitioners and compared to each other to determine if the more restrictive, assumption‐laden approach offered by the procedural model is adequate to deal with the decision problem under examination, or if the greater flexibility offered by the knowledge‐based approach is required. This comparison yields positive results. The procedural model is able to reproduce satisfactorily the output of the human decision makers and the knowledge‐based models for the chosen decision problem. This result emphasises the importance of devoting time to the selection of the most appropriate modelling approach for a given decision problem.