scholarly journals ПРОЕКТИРОВАНИЕ ЭКСПЕРТНЫХ СИСТЕМ НА ОСНОВЕ ВЫСОКОУРОВНЕВЫХ ЗНАНИЕОРИЕНТИРОВАННЫХ МОДЕЛЕЙ

2018 ◽  
pp. 63-70
Author(s):  
Константин Владиславович Головань
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Domain Knowledge ◽  
Electrochemical Protection ◽  
Specific Domain ◽  
Representation Model ◽  
Hybrid Knowledge ◽  
Functional Blocks ◽  
Functional Knowledge ◽  
High Level

The main points related to the design of the integrated decision supported expert systems are analyzed in the paper. The perspectives of hybrid knowledge representation model are considered. In order to represent the domain knowledge it is proposed to use a high level knowledge oriented model that makes it possible to describe the processes of analysis, mining, and processing of domain knowledge in a form of interaction of some typical predefined functional blocks. The main advantages of the developed functional knowledge-oriented model are: modularity (representation of monitoring, diagnostics and control processes of complex technological systems and objects in a form of separate knowledge-oriented components interaction); universality of the typical functional blocks library (solution of typical tasks, arising in the process of technological object control); adaptability (easy adaptation to a specific domain); openness (gives the user a possibility to set the custom behavior); activity (interaction of typical functional blocks with each other that makes it possible to automate the process of knowledge acquisition and processing and also interaction of functional blocks with a hybrid production-frame model that makes possible to increase the efficiency of knowledge procession during the decision making process). Every typical intelligent element is a functional block with a set of inputs {IN} and outputs {OUT}. The behavior of such block is defined by its purpose. The whole set of typical intelligent blocks that is used in construction of functional knowledge-oriented model according to the block purpose can be divided into several different classes. On the basis of the selected representation model the processes of knowledge formalization are described. The main advantages of the selected approach to formalize the domain knowledge are stated. On the basis of the represented instrumental tool structure the computer the system has been made. The main stages of expert system creation and their key features are considered. The editor of functional knowledge-oriented model has been presented. The basic functions of the editor are model visualization and debugging. Instrumental tool make possible to build control decision expert systems in different domains. The example of expert system in domain of electrochemical protection of pipelines from corrosion has been considered. The basic directions of possible updating of mathematical model and instrumental tools are described in conclusion

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

A PROPOSED ARCHITECTURE FOR SELF-ADAPTIVE EXPERT SYSTEMS

2009 ◽  
Vol 19 (02) ◽  
pp. 213-248 ◽  
Author(s):  
TSUNG-TENG CHEN ◽  
CHENG-SEEN HO
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Domain Knowledge ◽  
Semantic Networks ◽  
Inference Process ◽  
Operating Environment ◽  
Traditional System ◽  
User Input ◽  
Adaptive Capability ◽  
Self Adaptive

The pre-built knowledge of traditional expert systems is only capable of limited responses to changes in the operating environment. If the data input is imperfect, a traditional system may fail to reach any rational conclusions. In this paper, we introduce the concept of self-adaptability to the inference process of an expert system, and propose a model that is capable of handling unexpected user input effectively and efficiently. Such a system can formulate operational knowledge on the move for inference. With this self-adaptive capability, an expert system can reach useful conclusions, even when the input data is insufficient. The architecture of the proposed system encodes domain knowledge with semantic networks. It also defines four types of adaptation, namely, condition knowledge adaptation, operational knowledge adaptation, conclusion knowledge adaptation, and presentation adaptation, and focuses on how the first three contribute to the adaptive capability of the system. In addition, to enable a self-adaptive expert system to effectively produce better conclusions, two entropy-based measuring mechanisms are proposed: one minimizes the information loss during knowledge adaptation, while the other selects the best attribute relation during the generation of operational knowledge. We have proved that a self-adaptive expert system based on this architecture can always reach a regular conclusion or an abstract conclusion, which is a more meaningful conclusion by automatically modifying its operational knowledge in response to user feedback during the inference process, even in unexpected situations.

scholarly journals APPLICATION OF EXPERT SYSTEMS OR DECISION-MAKING SYSTEMS IN THE FIELD OF EDUCATION

2021 ◽  
Vol 9 (1) ◽  
pp. 1396-1405
Author(s):  
Biju Theruvil Sayed
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Students With Disabilities ◽  
Knowledge Engineer ◽  
Specific Domain ◽  
Expert System Shell ◽  
Computer Based ◽  
Explanation System ◽  
Quality Learning

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.

Design of an Android-Based Tropical Disease Diagnosis Expert System Design Using a Combined Method of Forward Chanining and Backward Chaining

2015 ◽  
Vol 1 (1) ◽  
pp. 43-50
Author(s):  
Muhammad Fahmi Hidayah
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Disease Diagnosis ◽  
Reference Book ◽  
Combined Method ◽  
Specific Domain ◽  
Backward Chaining ◽  
Forward Chaining ◽  
Knowledge Based ◽  
Specific Symptoms

A doctor or medical scholar needs a reference book to learn how to diagnose tropical diseases. This reference book is sometimes a hassle if you have to carry it everywhere. This reference book is also impractical if you have to search it first to find the symptoms and diseases you want to study. So that we need a system to make it easier for doctors and medical scholars to study the science of diagnosis and look for symptoms and diseases. Expert systems are knowledge-based programs that provide expert quality solutions to problems in a specific domain. This expert system is used in the fields of medicine, agriculture, business, and others. Expert systems in the field of medicine make it easy to identify diseases suffered by patients through the symptoms present in the patient. This expert system helps doctors make diagnoses to convince doctors about the results of the diagnosis. The expert system in this study uses a combined method. The combined method is forward chaining and backward chaining. The forward chaining method is used to determine specific symptoms that appear, while the backward chaining method is used to trace general symptoms that arise from specific symptoms that have been previously selected. The result of combining these methods provides a diagnostic percentage of 100%. Meanwhile, the user's assessment of the system gives a good response.

Criteria for Choosing Representation Languages and Control Regimes for Expert Systems

1984 ◽  
Vol 1 (4) ◽  
pp. 2-17 ◽  
Author(s):  
Han Reichgelt ◽  
Frank van Harmelen
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Programming Language ◽  
Knowledge Engineering ◽  
Knowledge Representations ◽  
High Level ◽  
And Control ◽  
Representation Formalism ◽  
Control Regime

AbstractShells and high-level programming language environments suffer from a number of shortcomings as knowledge engineering tools. We conclude that a variety of knowledge representation formalisms and a variety of controls regimes are needed. In addition guidelines should be provided about when to choose which knowledge representation formalism and which control regime. The guidelines should be based on properties of the task and the domain of the expert system. In order to arrive at these guidelines we first critically review some of the classifications of expert systems in the literature. We then give our own list of criteria. We test this list applying our criteria to a number of existing expert systems. As a caveat, we have not yet made a systematic attempt at correlating the criteria and different knowledge representations formalisms and control regimes, although we make some preliminary remarks throughout the paper.

ACCESSING DATA MINING RULES THROUGH EXPERT SYSTEMS

2002 ◽  
Vol 01 (04) ◽  
pp. 657-672 ◽  
Author(s):  
BASILIS BOUTSINAS
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Discovery ◽  
Domain Knowledge ◽  
Research Area ◽  
General Purpose ◽  
Related Data ◽  
Different Types

Data mining is an emerging research area that develops techniques for knowledge discovery in huge volumes of data. Usually, data mining rules can be used either to classify data into predefined classes, or to partition a set of patterns into disjoint and homogeneous clusters, or to reveal frequent dependencies among data. The discovery of data mining rules would not be very useful unless there are mechanisms to help analysts access them in a meaningful way. Actually, documenting and reporting the extracted knowledge is of considerable importance for the successful application of data mining in practice. In this paper, we propose a methodology for accessing data mining rules, which is based on using an expert system. We present how the different types of data mining rules can be transformed into the domain knowledge of any general-purpose expert system. Then, we present how certain attribute values given by the user as facts and/or goals can determine, through a forward and/or backward chaining, the related data mining rules. In this paper, we also present a case study that demonstrates the applicability of the proposed methodology.

scholarly journals Tuning Expert Systems for Cost-Sensitive Decisions

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Atish P. Sinha ◽  
Huimin Zhao
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Expert Systems ◽  
Domain Knowledge ◽  
Training Data ◽  
Business Decisions ◽  
Misclassification Cost ◽  
Knowledge Based ◽  
The Impact ◽  
And Training

There is currently a growing body of research examining the effects of the fusion of domain knowledge and data mining. This paper examines the impact of such fusion in a novel way by applying validation techniques and training data to enhance the performance of knowledge-based expert systems. We present an algorithm for tuning an expert system to minimize the expected misclassification cost. The algorithm employs data reserved for training data mining models to determine the decision cutoff of the expert system, in terms of the certainty factor of a prediction, for optimal performance. We evaluate the proposed algorithm and find that tuning the expert system results in significantly lower costs. Our approach could be extended to enhance the performance of any intelligent or knowledge system that makes cost-sensitive business decisions.

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Siti Nurhena ◽  
Nelly Astuti Hasibuan ◽  
Kurnia Ulfa
Keyword(s):  
Artificial Intelligence ◽  
Immune System ◽  
Expert System ◽  
Expert Systems ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Visual Basic ◽  
Artificial Intelligence Techniques ◽  
Individual Needs ◽  
Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT ANAK YANG DIDUKUNG OLEH BAHASA PEMOGRAMAN VISUAL

2017 ◽  
Vol 3 (2) ◽  
pp. 108
Author(s):  
Dian Permata Sari
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Human Knowledge ◽  
Inference Method ◽  
Medical Field ◽  
Forward Chaining ◽  
At Home

<p>Sistem pakar merupakan sistem yang mengadopsi pengetahuan manusia ke komputer yang dirancang untuk memodelkan kemampuan menyelesaikan masalah seperti layaknya seorang pakar. Dengan sistem pakar ini, orang awam pun dapat menyelesaikan masalahnya atau hanya sekedar mencari suatu informasi berkualitas yang sebenarnya hanya dapat diperoleh dengan bantuan para ahli di bidangnya. Salah satunya yaitu dibidang medis untuk mendiagnosapenyakit anak. Mengetahui gejala dari suatu penyakit secara dini dapat menjadi bantuan pertama yang dapat dilakukan para orang tua di rumah jika anak mereka terserang penyakit.Basis pengetahuan disusun sedemikian rupa kedalam database dengan beberapa tabel. Penarikan kesimpulan dalam sistem pakar ini menggunakan metode inferensi <em>forward chaining</em>. Sistem pakar akan memberikan pertanyaan-pertanyaan kepada user berupa gejala dari beberapa penyakit dan user akan menjawab pertanyaan tersebut. Hingga <em>user</em> akan mendapatkan solusi dari hasil pertanyaan tadi. </p><p><em><br /></em></p><p><em>Expert systems are systems that adopt human knowledge into computers designed to model the ability to resolve problems like an expert. Through thisexpert systems,commoner cansolvetheproblem orjustlookingfor a qualityinformationthat can onlybeobtainedwiththehelpofexperts in thefield. One ofthemis in the medical field to diagnosethe children's illness.Knowingthesymptomsofanillnessearly can bethefirstaidto parents if their children stricken withthedisease at home.</em><em>Knowledgebase is arranged into a highlystructureddatabasewithmultipletables. Inferences in this expert system uses forward chaining inference method. Expert systems will provide questions to the user in the form of the symptoms of some diseases and the user will answer that question. Until the user will get the solution of the question.</em></p>

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