Rule-based Expert System Research Articles

A Novel Rule-based Expert System for Early Diagnosis of Bipolar and Major Depressive Disorder

A confident and timely diagnosis of mental illnesses is one of the primary challenges practitioners repeatedly encounter when they start treating new patients. However, diagnosing can quickly become problematic as the subjects expose comparative symptoms among mental illnesses. Due to influencing a broad populace among mental ailments, an adjusted differentiation between Major Depressive Disorder, Mania Bipolar Disorder, Depressive Bipolar Disorder, and ordinary individuals with mild symptoms is one of the critical subjects for community health. This study responded to the described problem by proposing a novel rule-based Expert System, which evaluates the impact of disorder symptoms on the Certainty Factor concerning each mental status. The semantic rules are developed based on the recommendation of experts, and the implementation is carried out using Prolog and C# languages. Furthermore, an easy-to-use user interface is considered to facilitate the system workflow. The consistency of the developed framework is established by performing rigorous tests by expert psychiatrists as well as 120 clinical samples collected from private samples. Based on the results, the current model classifies mental disorder cases with a success rate of 93.33% using only the 17 symptoms specified in the ontology model. Furthermore, a questionnaire that measures user satisfaction after the test also achieves a mean score of 3.56 out of 4, which indicates a high degree of user acceptance. As a result, it is concluded that the current framework is a reliable tool for achieving a solid diagnosis in a shorter period.

Robotic assistants for the rehabilitation of communication disorders in children: A proposal based on MTO manufacturing for developing countries

Language is fundamental to human communication, allowing individuals to express and exchange ideas, thoughts, and emotions. In early childhood, some children experience communication disorders that impede their ability to articulate words correctly, posing significant challenges to their learning and development. This issue is exacerbated in developing countries, where limited resources and a lack of technological tools hinder access to effective speech therapy. Traditional speech therapy remains vital, but the latest technological advancements have introduced robotic assistants to enhance therapy for communication disorders. Despite their potential, these technologies are often inaccessible in developing regions due to high production costs and a lack of sustainable manufacturing models. For these reasons, this paper presents “FONA,” a robotic assistant that employs rule-based expert systems to provide tactile, auditory, and visual stimuli. FONA supports children aged 3 to 6 in speech therapy by delivering exercises such as syllable production, word formation, and pictographic storytelling of various phonemes. Notably, FONA was successfully tested on children with cochlear implants, reducing the number of sessions required to produce isolated phonemes. The paper also introduces an innovative analysis of the Make To Order (MTO) manufacturing system for producing FONA in developing countries. This analysis explores two key perspectives: collaborative networks and entrepreneurship, offering a sustainable production model. In a pilot experiment, FONA significantly improved children’s attention spans, increasing the period by 17 min. Furthermore, the economic analysis demonstrates that producing FONA through collaborative networks can significantly reduce costs, making it more accessible to institutions in developing countries. The findings suggest that the project is viable for a five-year period, providing a sustainable and effective solution for addressing communication disorders in children.

Integrating AI in cause-of-death regular statistical production with human and expert system codings

Abstract Background Cause-of-death (CoD) statistics are key indicators in epidemiology and public health. These statistics are derived from death certificates completed by physicians and are usually coded by official statistics authorities according to the standards of the WHO ICD-10 classification to construct time- and cross-country-comparable statistics. For coding causes of death in free-text format in France according to ICD-10, predictions by deep neural networks (DNNs) are employed in addition to fully automatic batch coding by a rule-based expert system and to interactive coding by the coding team. Methods Seq-to-seq DNNs are trained from scratch on previously coded data to ICD-10 code multiple causes and underlying causes of death. Human coding is focused on certificates with a special public health interest or with low confidence in AI prediction quality to maximize the quality of the overall statistics disseminated when human resources are limited. DNNs also directly predict multiple causes and underlying causes of death for part of the certificates. Hence, the coding campaign aims to optimally allocate a coding mode to a given certificate. Results For deaths in 2021, 63% of the certificates are automatically batch coded by the expert system, 14% by the coding team, and 23% by DNNs. Compared to a traditional campaign that would have relied on automatic batch coding and manual coding, the present campaign reaches an accuracy of 95.7% for ICD-10 coding of the underlying cause (97.3% at the European shortlist level). Conclusions The 3-coding mode approach enables timeliness of dissemination of CoD statistics and may lead in the future to more responsive surveillance. Key messages • Deep neural networks trained on already coded data can be integrated into the regular statistical production of the CoD database in a fully controlled way. • The allocation between the different modes of coding can be optimized to achieve best quality and timeliness under the constraint of limited human resources.

An effective farmer-centred mobile intelligence solution using lightweight deep learning for integrated wheat pest management

Integrated Pest Management (IPM) techniques have been widely used in agriculture to manage pest damage in the most economical way and to minimise harm to people, property and the environment. However, current research and products on the market cannot consolidate this process. Most existing solutions either require experts to visually identify pests or cannot automatically assess pest levels and make decisions based on detection results. To make the process from pest identification to pest management decision making more automated and intelligent, we propose an end-to-end integrated pest management solution that uses deep learning for semi-automated pest detection and an expert system for pest management decision making. Specifically, a low computational cost sampling point generation algorithm is proposed to enable mobile devices to generate uniformly distributed sampling points in irregularly shaped fields. We build a pest detection model based on YoloX and use Pytorch Mobile to deploy it on mobile phones, allowing users to detect pests offline. We develop a standardised sampling specification and a mobile application to guide users to take photos that allow pest population density to be calculated. A rule-based expert system is established to derive pest management thresholds from prior agricultural knowledge and make decisions based on pest detection results. We also propose a human-in-the-loop algorithm to continuously track and update the validity of the thresholds in the expert system. The mean average precision of the pest detection model is 58.17% for 97 classes, 75.29% for 2 classes, and 57.33% for 11 classes on three pest datasets, respectively. The usability of the pest management system is assessed by the User Experience Surveys and achieves a System Usability Scale (SUS) score of 76. The usability of the proposed solution is validated by qualitative field experiments.

Belief-Rule-Based System with Self-organizing and Multi-temporal Modeling for Sensor-based Human Activity Recognition.

Smart environment is an efficient and cost- effective way to afford intelligent supports for the elderly people. Human activity recognition (HAR) is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based HAR model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.

Intelligent System for Tourist Guidance in Tumbes-Perú

An Intelligent System (IS) for Tourist Guidance was developed for the city of Tumbes, Peru. Considering the preferences of the tourist, the IS obtains the optimal route for the visit and provides a visit plan with schedules and times of stay in each place. It is an innovative hybrid system since it is complemented by two artificial intelligence techniques, a rule-based expert system and a genetic algorithm. The methodology for the implementation of the IS consisted of the collection of tourist, geographic, georeferenced information and photographs of 28 tourist places, grouped into Beaches, Nature, Adventure, Culture and Medicinal Sites, then the logical rules, the distance matrix and time of stay in each place were developed, and finally with the vehicle routing technique the genetic algorithm is implemented that allows obtaining the optimal route. The results indicate that the GA provides optimal solutions with an execution time of 5.6 seconds and zero errors. This contribution is very important for future work on tourist profiles and new tourist routes, since the intelligent system can be adapted for use in other cities.

A novel CLIPS-based medical expert system for migraine diagnosis and treatment recommendation

Migraines are classified as a neurological disorder defined by recurrent headaches with pain that ranges from mild to severe. Currently, this disorder lacks a permanent cure and definitive diagnostic test. Diagnosis instead requires an assessment of physical and psychological symptoms which differ among patients. To help in the diagnosis process, medical expert systems have been developed and validated since 1960. In this paper, we propose the Migraine Diagnosis and Treatment Expert System (MDATES), a medical expert system for migraine diagnosis and treatment recommendation. The system was designed and implemented using the C Language Integrated Production System (CLIPS) shell. MDATES is able to recognize seven symptoms, two classes of migraines (chronic and episodic), and four subtypes of migraine-classification knowledge (hormonal, aura, hemiplegic, and cluster). A dataset of 300 anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by MDATES were compared against the known diagnoses, and a high level of accuracy was observed, with 67% of the 100 training cases were correctly diagnosed, and 100% of the 200 testing cases were correctly diagnosed. These results highlight the effectiveness and reliability of MDATES and provide valuable assistance to medical professionals in diagnosing migraines. Moreover, we present a literature review that places our proposed system within the broader context of rule-based expert systems for migraine diagnosis and treatment recommendation. This review explores the effectiveness, limitations, and challenges of these systems, and accurately places our system within the current landscape.

A new aeronautical relay health state assessment method based on generic belief rule base with attribute reliability

As a classical rule-based modeling approach, belief rule base (BRB) expert system can integrate expert knowledge and possesses good interpretability. BRB with attribute reliability (BRB-r), built upon BRB, provides an effective way to deal with the problems of model reliability and environmental disturbances. Moreover, robustness is an important measure of perturbation resistance, and a robust BRB-r can remain reliable and stable in various environments. Therefore, to improve the model's ability to resist perturbations and enhance the model's adaptability, a new generic BRB with attribute reliability (G-BRB-r) is developed. Specifically, the robustness of BRB-r is analyzed in this paper to explore the change of BRB-r robustness under different perturbations. In addition, combining the effects of different factors on robustness, the construction criteria and constraints of robust BRB-r are given to guide modeling. Then, considering the effects of attribute reliability and robustness on modeling performance, a new generic BRB with attribute reliability is developed. Finally, the effectiveness and adaptability of the proposed method are demonstrated through a case study for health state assessment of the aerospace relay.

Trustworthy Fault Diagnosis Method Based on Belief Rule Base With Multisource Uncertain Information for Vehicle

This paper develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with combining multi-source uncertainty information for vehicle, such as flying machine. To improve the performance of fault diagnosis for vehicle, a new belief rule base expert system with multi-source uncertainty information (BRB-MU) is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle three problems: unbalanced observation data of system states, uncertain expert knowledge and random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition (SVD) in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of BRB-MU is conduced that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conduced.

A novel game-based belief rule base

Establishing an initial belief rule base (BRB) expert system typically relies on expert knowledge. However, the heterogeneity in knowledge arising from variations in expert cognition directly impacts the accuracy and robustness of BRB. Furthermore, the existing research lacks a discussion on building the initial BRB under multi-expert knowledge. In response to this challenge, a novel game-based belief rule base (GBRB) is proposed in this paper. First, a consensus matrix is introduced to formalize the interaction among expert knowledge, which is the foundation for constructing a multi-expert evolutionary game. Subsequently, the replicator equation for population dynamics is employed to simulate the dynamic process of the multi-expert evolutionary game. Finally, optimal expert knowledge, which refers to referential values and belief degrees, is determined based on the evolution results to participate in establishing GBRB. Case studies are conducted to illustrate the construction process and effectiveness of the GBRB. The experimental results are compared with machine learning-related methodologies. The comparative results demonstrate the good performance of GBRB in modeling effectiveness and prediction accuracy.

Combining deep neural networks, a rule-based expert system and targeted manual coding for ICD-10 coding causes of death of French death certificates from 2018 to 2019

ObjectiveFor ICD-10 coding causes of death in France in 2018 and 2019, predictions by deep neural networks (DNNs) are employed in addition to fully automatic batch coding by a rule-based expert system and to interactive coding by the coding team focused on certificates with a special public health interest and those for which DNNs have a low confidence index. MethodsSupervised seq-to-seq DNNs are trained on previously coded data to ICD-10 code multiple causes and underlying causes of death. The DNNs are then used to target death certificates to be sent to the coding team and to predict multiple causes and underlying causes of death for part of the certificates. Hence, the coding campaign for 2018 and 2019 combines three modes of coding and a loop of interaction between the three. FindingsIn this campaign, 62% of the certificates are automatically batch coded by the expert system, 3% by the coding team, and the remainder by DNNs. Compared to a traditional campaign that would have relied on automatic batch coding and manual coding, the present campaign reaches an accuracy of 93.4% for ICD-10 coding of the underlying cause (95.6% at the European shortlist level). Some limitations (risks of under- or overestimation) appear for certain ICD categories, with the advantage of being quantifiable. ConclusionThe combination of the three coding methods illustrates how artificial intelligence, automated and human codings are mutually enriching. Quantified limitations on some chapters of ICD codes encourage an increase in the volume of certificates sent for manual coding from 2021 onward.

An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings

The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES’s parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%.

A Hybrid Expert System for Estimation of the Manufacturability of a Notional Design

The more “manufacturable” a product is, the “easier” it is to manufacture. For two different product designs targeting the same role, one may be more manufacturable than the other. Evaluating manufacturability requires experts in the processes of manufacturing, “manufacturing process engineers” (MPEs). Human experts are expensive to train and employ, while a well-designed expert system (ES) could be quicker, more reliable, and provide higher performance and superior accuracy. In this work, a group of MPEs (“Team A”) externalized a portion of their expertise into a rule-based expert system in cooperation with a group of ES knowledge engineers and developers. We produced a large ES with 113 total rules and 94 variables. The ES comprises a crisp ES which constructs a Fuzzy ES, thus producing a two-stage ES. Team A then used the ES and a derivation of it (the “MAKE A”) to conduct assessments of the manufacturability of several “notional” designs, providing a sanity check of the rule-base. A provisional assessment used a first draft of the rule-base, and MAKE A, and was of notional wing designs. The primary assessment, using an updated rule-base and MAKE A, was of notional rotor blade designs. We describe the process by which this ES was made and the assessments that were conducted and conclude with insights gained from constructing the ES. These insights can be summarized as follows: build a bridge between expert and user, move from general features to specific features, do not make the user do a lot of work, and only ask the user for objective observations. We add the product of our work to the growing library of tools and methodologies at the disposal of the U.S. Army Engineer Research and Development Center (ERDC). The primary findings of the present work are (1) an ES that satisfied the experts, according to their expressed performance expectations, and (2) the insights gained on how such a system might best be constructed.

A Rule-Based Expert System for Academic Advising and Guidance Toward Undergraduate Students

The provision of academic guidance to undergraduate students is a crucial and significant duty for the academic faculty in most prestigious universities. Expert systems are often regarded as one of the most successful areas in the field of artificial intelligence. The system utilizes a rule-based decision engine to assist individuals lacking experience in enhancing their skills. Nevertheless, a significant number of students are required to meet with their advisors to arrange their study schedule. This thesis is driven by the belief that the successful development of an academic advisory expert system will result in an expansion of the range and extent of challenges that students, academic staff, and other academic activities can effectively address, leading to a higher level of achievement in the university learning process. Nevertheless, the effectiveness of an advisory expert system is constrained by the caliber of the acquired knowledge, specifically in the context of academic advisory expert systems. The performance of such systems is primarily determined by the quality of the framework employed for academic expert knowledge acquisition. The objective of this study is to suggest a revision of the current knowledge acquisition framework in order to make it appropriate for implementation in higher education institutions. The proposed modification aims to create a prototype rule-based expert system specifically designed for academic advising of undergraduate students. The system's output also offers a precise and conflict-free course proposal for the undergraduate student. It functions as a mentor, providing undergraduate students with guidance and recommendations for suitable subjects based on their completed courses, prerequisites, and project scope, as defined by the student themselves. According to the empirical findings, the utilization of the proposed model for an undergraduate advisory expert system resulted in a noteworthy enhancement in performance.

Development of Rule-Based Expert System for Variable Cost and Gross Profit Calculations of Agricultural Products: A Case Study

Cost and gross profit calculations of agricultural products are a comprehensive and difficult process. Many different parameters, such as soil cultivation, pruning, spraying, irrigation, storage, drying, harvesting, transportation, and marketing, need to be evaluated together. Currently, cost calculations of agricultural products are made with classical programs and simple mathematical methods. For this reason, the results vary depending on the person and organization making the evaluation. This creates some limitations, making cost planning, which is a dynamic process, unreliable. In the study, a rule-based expert system was developed for the cost and gross profit calculation of apricots. The expert system developed is called Apricot Information System (APRIS). The developed expert system aims to provide more accurate solutions by adding the precision and speed of the computer to the calculation process. The main contribution of this study is the development of a complete decision support set covering all processes from tillage to marketing and the design of rule-based user-system interaction. The designed system found the apricot variable cost calculation to be 1126.36 TL/da for 2020. This value largely overlaps with the values obtained from the field in the previous year. The developed expert system will be a platform that can be used in cost calculations of many agricultural products.

THE CONSTRUCTION OF INTELLIGENT RULE-BASED SYSTEM WITH DEEP-LEARNING FUNCTIONALITY FOR OIL AND GAS WELLS DRILLING

The article is devoted to solving the problems of predictive modeling using machine learning methods under conditions of uncertainty in huge data sets created in various sectors of the economy, in particular, the oil and gas industry, due to the spread of IoT and cloud services. The author notes that existing machine learning methods, including widely used deep learning methods, face limitations in achieving high prediction accuracy due to various uncertainties. The article explores fuzzy learning approaches to overcome uncertainties such as imprecision and ambiguity, but emphasizes their limitations of incompleteness and vagueness. It is argued that rule-based expert systems augmented with deep learning offer a more robust framework for dealing with such uncertainties by integrating associative memory, helping to produce more accurate predictive models, especially in data-intensive scenarios such as oil and gas drilling processes. The study focuses on improving predictive modeling under conditions of uncertainty in large data sets. We discuss the limitations of fuzzy learning when processing incomplete data and propose a hybrid approach to the problem. Integration of the associative memory learning method into the logical inference procedures allows to identify accurate data models, thereby improving the quality of forecasting under uncertainty. This integrated approach aims to improve forecasting accuracy by leveraging the strengths of deep learning in pattern recognition and the ability of rule-based systems to cope with uncertainty. The main result of the research work is the development of a new method that integrates a level of deep processing with associative memory into a rule-based system to improve forecasting accuracy under uncertainty. This was achieved by using a multilayer neural network and adding additional parameters. This method significantly improved the accuracy of predicting the dynamics of operational parameters during drilling. Thus, the presented study is a new contribution to the task of developing forecasting methods under conditions of uncertainty, which is especially relevant for oil and gas drilling.

Dynamic Knowledge Management in an Agent-Based Extended Green Cloud Simulator

Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” may “disappear” and “come back”, specifically in scenarios where the lack/return of green energy leads to shutting down/booting back servers at a given location. Considered use cases may involve introducing new types of resources (e.g., adding containers with server racks with “next-generation processors”). All such situations require the dynamic adaptation of “system knowledge”, i.e., runtime system adaptation. In this context, an agent-based digital twin of the extended green cloud infrastructure is proposed. Here, knowledge management is facilitated with an explainable Rule-Based Expert System, combined with Expression Languages. The tests were run using Extended Green Cloud Simulator, which allows the modelling of cloud infrastructures powered (partially) by renewable energy sources. Specifically, the work describes scenarios in which: (1) a new hardware resource is introduced in the system; (2) the system component changes its resource; and (3) system user changes energy-related preferences. The case study demonstrates how rules can facilitate control of energy efficiency with an example of an adaptable compromise between pricing and energy consumption.

Application and existing problems of artificial intelligence technology in the agricultural field

In recent years, the application of artificial intelligence technology in the field of agriculture has been rapidly developed. This paper summarizes the application of artificial intelligence in agriculture and divides it into two main directions: monitoring system and expert system. This paper analyzes the soil monitoring, pest monitoring, and plant growth detection of the monitoring system, the simple decision chain of the expert system, and the complex expert system combined with artificial intelligence technology. Utilizing sensor networks, image processing, and machine learning techniques, artificial intelligence enables real-time monitoring of soil parameters, automatic identification of pest and disease, analysis of plant growth status, and provision of tailored management recommendations. By employing rule-based expert systems, artificial intelligence assists farmers in making informed decisions. These applications have significantly advanced resource management optimization, pest control, precise growth monitoring, and intelligent decision-making in agriculture. At the end of the article, this paper summarizes the full text and looks forward to the future trend.

Rule-based expert system for the diagnosis of maternal complications during pregnancy: For low resource settings.

Maternal complications are health challenges linked to pregnancy, encompassing conditions like gestational diabetes, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. The diagnosis of common pregnancy complications is challenging due to the similarity in signs and symptoms with general pregnancy indicators, especially in settings with scarce resources where access to healthcare professionals, diagnostic tools, and patient record management is limited. This paper presents a rule-based expert system tailored for diagnosing three prevalent maternal complications: preeclampsia, gestational diabetes mellitus (GDM), and maternal sepsis. The risk factors associated with each disease were identified from various sources, including local health facilities and literature reviews. Attributes and rules were then formulated for diagnosing the disease, with a Mamdani-style fuzzy inference system serving as the inference engine. To enhance usability and accessibility, a web-based user interface has been also developed for the expert system. This interface allows users to interact with the system seamlessly, making it easy for them to input relevant information and obtain accurate disease diagnose. The proposed expert system demonstrated a 94% accuracy rate in identifying the three maternal complications (preeclampsia, GDM, and maternal sepsis) using a set of risk factors. The system was deployed to a custom-designed web-based user interface to improve ease of use. With the potential to support health services provided during antenatal care visits and improve pregnant women's health outcomes, this system can be a significant advancement in low-resource setting maternal healthcare.

Paving the way for automated factory planning – applying rule-based expert systems to capacity planning

Today, numerous different software systems aid factory planners in their tasks. Nevertheless, due to their lacking interoperability and project-specific approaches, generic support for automated decision-making is still missing. Investigating the state of the art, we conclude that knowledge-based information modeling is needed for decision-making support. However, as the identified approaches of previous works propose no general automation concepts. Therefore, we define the guiding research question as how to model processual domain knowledge for automating factory planning processes. In this paper, we propose a planning assistance on rule-based expert systems. The planning assistance is composed of an ontology-based information model, a planning model consisting of individual planning functions, and a domain-specific inference engine. We implement the planning assistance with Semantic Web technologies and validate the solution using an application example from capacity planning. Thereby, we demonstrate the applicability of rule-based expert systems for automated factory planning. Finally, implications for future research are drawn for exploring further application areas and developing anticipated hybrid solution concepts.