A novel definition of expert knowledge in expert systems

2002 ◽  
Author(s):  
I.E. Russell ◽  
H. Fatmi
Keyword(s):  
Expert Systems ◽  
Expert Knowledge ◽  
Definition Of

Expert Systems in Distance Education

2018 ◽  
pp. 1410-1423
Author(s):  
Duygu Mutlu-Bayraktar ◽  
Esad Esgin
Keyword(s):  
Artificial Intelligence ◽  
Distance Education ◽  
Natural Language ◽  
Expert Systems ◽  
Expert Knowledge ◽  
Computer Systems ◽  
Educational Environments ◽  
Artificial Environment ◽  
Present Solution ◽  
Definition Of

Computers have been used in educational environments to carry out applications that need expertise, such as compiling, storing, presentation, and evaluation of information. In some teaching environments that need expert knowledge, capturing and imitating the knowledge of the expert in an artificial environment and utilizing computer systems that have the ability to communicate with people using natural language might reduce the need for the expert and provide fast results. Expert systems are a study area of artificial intelligence and can be defined as computer systems that can approach a problem for which an answer is being sought like an expert and present solution recommendations. In this chapter, the definition of expert systems and their characteristics, information about the expert systems in teaching environments, and especially their utilization in distance education are given.

Expert Systems in Distance Education

2015 ◽  
pp. 136-148 ◽  
Author(s):  
Duygu Mutlu-Bayraktar ◽  
Esad Esgin
Keyword(s):  
Artificial Intelligence ◽  
Distance Education ◽  
Natural Language ◽  
Expert Systems ◽  
Expert Knowledge ◽  
Computer Systems ◽  
Educational Environments ◽  
Artificial Environment ◽  
Present Solution ◽  
Definition Of

Computers have been used in educational environments to carry out applications that need expertise, such as compiling, storing, presentation, and evaluation of information. In some teaching environments that need expert knowledge, capturing and imitating the knowledge of the expert in an artificial environment and utilizing computer systems that have the ability to communicate with people using natural language might reduce the need for the expert and provide fast results. Expert systems are a study area of artificial intelligence and can be defined as computer systems that can approach a problem for which an answer is being sought like an expert and present solution recommendations. In this chapter, the definition of expert systems and their characteristics, information about the expert systems in teaching environments, and especially their utilization in distance education are given.

The SWORFISH Project Approach for Modeling Wood Material Modifications in Timber Structures

2013 ◽  
Vol 778 ◽  
pp. 418-423
Author(s):  
Jakub Sandak ◽  
Anna Sandak ◽  
Mariapaola Riggio ◽  
Ilaria Santoni ◽  
Dusan Pauliny
Keyword(s):  
Experimental Data ◽  
Expert Knowledge ◽  
Surface Finishing ◽  
Special Focus ◽  
Original Material ◽  
Timber Structures ◽  
Wood Material ◽  
Project Approach ◽  
Special Software ◽  
Definition Of

A special software simulating changes to wood due to various processes (either treatment or degradation) has been developed within the SWORFISH (Superb Wood Surface Finishing) project. The definition of the material modifications due to processes is based on the expert knowledge and/or experimental data. The dedicated algorithm simulates material modifications (with a special focus on surface) taking into account original material characteristics (evaluated by means of NDT techniques) and setting of process parameters. In this way, it is possible to analyze the sequence of processes (i.e. material modifications) and to estimate properties of the resulting product. Two case studies are presented for illustration of the potential uses of the SWORFISH approach in the field of timber structures.

Characterizing Opioid Overdoses Using Emergency Medical Services Data

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 62S-71S
Author(s):  
Josie J. Sivaraman ◽  
Scott K. Proescholdbell ◽  
David Ezzell ◽  
Meghan E. Shanahan
Keyword(s):  
Machine Learning ◽  
North Carolina ◽  
Emergency Medical Services ◽  
Expert Knowledge ◽  
Case Definition ◽  
Medical Services ◽  
Opioid Overdose ◽  
Machine Learning Classification ◽  
Emergency Medical ◽  
Definition Of

Objectives Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (EMS) data, with flexible methodology for application to other states’ data. Methods This study used de-identified North Carolina EMS encounter data from 2010-2015 for patients aged >12 years to develop a case definition of opioid overdose using an expert knowledge, rule-based algorithm reflecting whether key variables identified drug use/poisoning or overdose or whether the patient received naloxone. We text mined EMS narratives and applied a machine-learning classification tree model to the text to predict cases of opioid overdose. We trained models on the basis of whether the chief concern identified opioid overdose. Results Using a random sample from the data, we found the positive predictive value of this case definition to be 90.0%, as compared with 82.7% using a previously published case definition. Using our case definition, the number of unresponsive opioid overdoses increased from 3412 in 2010 to 7194 in 2015. The corresponding monthly rate increased by a factor of 1.7 from January 2010 (3.0 per 1000 encounters; n = 261 encounters) to December 2015 (5.1 per 1000 encounters; n = 622 encounters). Among EMS responses for unresponsive opioid overdose, the prevalence of naloxone use was 83%. Conclusions This study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based approach to create a case definition for opioid overdose in EMS data.

On techniques of expert systems on the example of the Akinator program

2020 ◽  
Vol 25 (2) ◽  
pp. 7-13
Author(s):  
Zhangozha A.R. ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Expert Systems ◽  
Knowledge Base ◽  
Hybrid Systems ◽  
Online Game ◽  
Basic Principles ◽  
The Moment ◽  
Definition Of

On the example of the online game Akinator, the basic principles on which programs of this type are built are considered. Effective technics have been proposed by which artificial intelligence systems can build logical inferences that allow to identify an unknown subject from its description (predicate). To confirm the considered hypotheses, the terminological analysis of definition of the program "Akinator" offered by the author is carried out. Starting from the assumptions given by the author's definition, the article complements their definitions presented by other researchers and analyzes their constituent theses. Finally, some proposals are made for the next steps in improving the program. The Akinator program, at one time, became one of the most famous online games using artificial intelligence. And although this was not directly stated, it was clear to the experts in the field of artificial intelligence that the program uses the techniques of expert systems and is built on inference rules. At the moment, expert systems have lost their positions in comparison with the direction of neural networks in the field of artificial intelligence, however, in the case considered in the article, we are talking about techniques using both directions – hybrid systems. Games for filling semantics interact with the user, expanding their semantic base (knowledge base) and use certain strategies to achieve the best result. The playful form of such semantics filling programs is beneficial for researchers by involving a large number of players. The article examines the techniques used by the Akinator program, and also suggests possible modifications to it in the future. This study, first of all, focuses on how the knowledge base of the Akinator program is built, it consists of incomplete sets, which can be filled and adjusted as a result of further iterations of the program launches. It is important to note our assumption that the order of questions used by the program during the game plays a key role, because it determines its strategy. It was identified that the program is guided by the principles of nonmonotonic logic – the assumptions constructed by the program are not final and can be rejected by it during the game. The three main approaches to acquisite semantics proposed by Jakub Šimko and Mária Bieliková are considered, namely, expert work, crowdsourcing and machine learning. Paying attention to machine learning, the Akinator program using machine learning to build an effective strategy in the game presents a class of hybrid systems that combine the principles of two main areas in artificial intelligence programs – expert systems and neural networks.

Evaluation of Quantified Statements Using Gradual Numbers

2011 ◽  
pp. 246-269 ◽  
Author(s):  
Ludovic Liétard ◽  
Daniel Rocacher
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Relational Databases ◽  
Fuzzy Numbers ◽  
Theoretical Background ◽  
Definition Of ◽  
Flexible Querying

This chapter is devoted to the evaluation of quantified statements which can be found in many applications as decision making, expert systems, or flexible querying of relational databases using fuzzy set theory. Its contribution is to introduce the main techniques to evaluate such statements and to propose a new theoretical background for the evaluation of quantified statements of type “Q X are A” and “Q B X are A.” In this context, quantified statements are interpreted using an arithmetic on gradual numbers from Nf, Zf, and Qf. It is shown that the context of fuzzy numbers provides a framework to unify previous approaches and can be the base for the definition of new approaches.

Techniques for capturing expert knowledge: an expert systems/hypertext approach

1990 ◽  
Author(s):  
Lawrence Lafferty ◽  
Albert M. Koller, Jr. ◽  
Greg Taylor ◽  
Robin S. Schumann ◽  
Randy Evans
Keyword(s):  
Expert Systems ◽  
Expert Knowledge

Artificial intelligence: a new tool for forest management

1990 ◽  
Vol 20 (4) ◽  
pp. 428-437 ◽  
Author(s):  
Peter Kourtz
Keyword(s):  
Artificial Intelligence ◽  
Forest Management ◽  
Expert Systems ◽  
Computer Science ◽  
Solution Structure ◽  
Visual Recognition ◽  
Knowledge Engineering ◽  
Expert Knowledge ◽  
Low Cost ◽  
Process Models

Articicial intelligence is a new science that deals with the representation, automatic acquisition, and use of knowledge. Artificial intelligence programs attempt to emulate human thought processes such as deduction, inference, language, and visual recognition. The goal of artificial intelligence is to make computers more useful for reasoning, planning, acting, and communicating with humans. Development of artificial intelligence applications involves the integration of advanced computer science, psychology, and sometimes robotics. Of the subfields that artificial intelligence can be broken into, the one of most immediate interest to forest management is expert systems. Expert systems involve encoding knowledge usually derived from an expert in a narrow subject area and using this knowledge to mimic his decision making. The knowledge is represented usually in the form of facts and rules, involving symbols such as English words. At the core of these systems is a mechanism that automatically searches for and pieces together the facts and rules necessary to solve a specific problem. Small expert systems can be developed on common microcomputers using existing low-cost commercial expert shells. Shells are general expert systems empty of knowledge. The user merely defines the solution structure and adds the desired knowledge. Larger systems usually require integration with existing forestry data bases and models. Their development requires either the relatively expensive expert system development tool kits or the use of one of the artificial intelligence development languages such as lisp or PROLOG. Large systems are expensive to develop, require a high degree of skill in knowledge engineering and computer science, and can require years of testing and modification before they become operational. Expert systems have a major role in all aspects of Canadian forestry. They can be used in conjunction with conventional process models to add currently lacking expert knowledge or as pure knowledge-based systems to solve problems never before tackled. They can preserve and accumulate forestry knowledge by encoding it. Expert systems allow us to package our forestry knowlege into a transportable and saleable product. They are a means to ensure consistent application of policies and operational procedures. There is a sense of urgency associated with the integration of artificial intelligence tools into Canadian forestry. Canada must awaken to the potential of this technology. Such systems are essential to improve industrial efficiency. A possible spin-off will be a resource knowledge business that can market our forestry knowledge worldwide. If we act decisively, we can easily compete with other countries such as Japan to fill this niche. A consortium of resource companies, provincial resource agencies, universities, and federal government laboratories is required to advance this goal.

Critical Review of Expert System Validation in Transportation

1997 ◽  
Vol 1588 (1) ◽  
pp. 104-109 ◽  
Author(s):  
Gary S. Spring
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Critical Review ◽  
Ad Hoc ◽  
Evaluation Process ◽  
Performance Levels ◽  
System Validation ◽  
Overall Evaluation ◽  
Definition Of ◽  
Made In

Expert system validation—that is, testing systems to ascertain whether they achieve acceptable performance levels—has with few exceptions been ad hoc, informal, and of dubious value. Very few efforts have been made in this regard in the transportation area. A discussion of the major issues involved in validating expert systems is provided, as is a review of the work that has been done in this area. The review includes a definition of validation within the context of the overall evaluation process, descriptions and critiques of several approaches to validation, and descriptions of guidelines that have been developed for this purpose.

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