scholarly journals Automated knowledge base management: A survey

2017 ◽  
Author(s):  
Jorge Martinez-Gil
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Knowledge Base ◽  
Computer Systems ◽  
Knowledge Bases ◽  
Significant Progress ◽  
Knowledge Source ◽  
Fundamental Challenge ◽  
Base Management ◽  
Automated Knowledge

A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods to automatically manage Knowledge Bases which can serve as a knowledge source for computer systems trying to replicate the decision-making ability of human experts. Despite of most of the tasks involved in the building, exploitation and maintenance of KBs are far from being trivial, and significant progress has been made during the last years. However, there are still a number of challenges that remain open. In fact, there are some issues to be addressed in order to empirically prove the technology for systems of this kind to be mature and reliable.

Research on Management System of Automatic Driver Decision-Making Knowledge Base for Unmanned Vehicle

2019 ◽  
Vol 33 (04) ◽  
pp. 1959013 ◽  
Author(s):  
Zhaoxia Zhang ◽  
Qing Jiang ◽  
Rujing Wang ◽  
Liangtu Song ◽  
Zhengyong Zhang ◽  
...  
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Management System ◽  
Autonomous Driving ◽  
Unmanned Vehicles ◽  
Knowledge Model ◽  
Unmanned Vehicle ◽  
Base Management System ◽  
Decision Making System ◽  
Base Management

The acquisition, presentation and management of autonomous driving decision-making knowledge of unmanned vehicles are the key and difficult issues in the autonomous driving decision-making system of unmanned vehicles. This paper presents a knowledge model, which includes problem description layer and problem-solving knowledge layer. The automatic driving decision knowledge base of unmanned vehicle is composed of a set of knowledge models. Knowledge model supports knowledge representation and reasoning. Based on the WEB visualization knowledge modeling tool and visualization knowledge service tool, we construct the decision-making knowledge base management system for autonomous driving of unmanned vehicles and then construct the autonomous driving decision-making system of unmanned vehicles. The reasoning example shows that the knowledge base management system can effectively improve the knowledge acquisition, representation and maintenance efficiency of autonomous driving decision-making system, which is of great significance in enhancing the intelligence level of autonomous driving decision-making system.

Artificial Intelligence in Medical Science

2014 ◽  
pp. 11-23 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Decision Making ◽  
Signal Processing ◽  
Expert System ◽  
Knowledge Base ◽  
Medical Diagnosis ◽  
Medical Science ◽  
Inference Engine ◽  
Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

Decision Making Modeled as a Theorem Proving Process

2012 ◽  
Vol 4 (3) ◽  
pp. 1-11
Author(s):  
Jacques Calmet ◽  
Marvin Oliver Schneider
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Information Technology ◽  
Multiagent Systems ◽  
Theorem Proving ◽  
Mathematical Knowledge ◽  
Knowledge Bases ◽  
Advanced Mathematics ◽  
Knowledge Communities ◽  
Computational Systems

The authors introduce a theoretical framework enabling to process decisions making along some of the lines and methodologies used to mechanize mathematics and more specifically to mechanize the proofs of theorems. An underlying goal of Decision Support Systems is to trust the decision that is designed. This is also the main goal of their framework. Indeed, the proof of a theorem is always trustworthy. By analogy, this implies that a decision validated through theorem proving methodologies brings trust. To reach such a goal the authors have to rely on a series of abstractions enabling to process all of the knowledge involved in decision making. They deal with an Agent Oriented Abstraction for Multiagent Systems, Object Mechanized Computational Systems, Abstraction Based Information Technology, Virtual Knowledge Communities, topological specification of knowledge bases using Logical Fibering. This approach considers some underlying hypothesis such that knowledge is at the heart of any decision making and that trust transcends the concept of belief. This introduces methodologies from Artificial Intelligence. Another overall goal is to build tools using advanced mathematics for users without specific mathematical knowledge.

Experience in developing and using intelligent graphical computer systems

2021 ◽  
pp. 26-41
Author(s):  
Vladimir Kuznetsov ◽  
Irina Chizhova
Keyword(s):  
Artificial Intelligence ◽  
Computer Systems ◽  
Knowledge Bases ◽  
Parametric Models ◽  
Evaluation Systems ◽  
Building Information ◽  
Graphical System ◽  
Use Of Knowledge ◽  
Ore District

The article discusses the conceptual, methodological and practical aspects of building information and analytical systems, including forecasting and evaluation systems based on artificial intelligence and the use of knowledge bases, as well as the implementation of these principles in the research version of the developed intellectual and graphical system based on parametric models of deposits and ore fields of the Rudno-Altai mineragenic zone, used to assess the prospects of ore fields of the Zmeinogorsky ore district.

Computer architectures for logic-oriented data/knowledge bases

1989 ◽  
Vol 4 (1) ◽  
pp. 1-29
Author(s):  
Donghoon Shin ◽  
P. Bruce Berra
Keyword(s):  
Logic Programming ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Computer Architectures ◽  
Management Systems ◽  
Shared Knowledge ◽  
Base System ◽  
Knowledge Base System ◽  
Base Management ◽  
Knowledge Base Management Systems

AbstractKnowledge base management systems (KBMS) are designed to efficiently retrieve and manipulate large shared knowledge bases. A significant subclass of KBMS consisting of a combination of logic programming and database is often called a logic oriented knowledge base system (LOKBS). These systems must possess considerable processing and I/O capabilities so many approaches have been taken to the improvement of their performance. In this paper we review the current performance enhancing hardware approaches for LOKBS. We include parallelism, both in processing and I/O, algorithms, caching, and physical data organizations.

scholarly journals Knowledge-Based Systems Manufacturing With Viability Ensuring

2021 ◽  
Author(s):  
Valeriya V. Gribova ◽  
Elena A. Shalfeeva
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Product Family ◽  
Knowledge Bases ◽  
Knowledge Based Systems ◽  
Manufacturing Environment ◽  
Development Environment ◽  
Systematic Analysis ◽  
Knowledge Based ◽  
Medical Software

Abstract With highly increased competition, intelligent product manufacturing based on interpretable knowledge bases has been recognized as an effective method for building applications of explainable Artificial Intelligence that is the hottest topic in the field of Artificial Intelligence. The success of product family directly depends on how effective the viability mechanisms are laid down in its design. In this paper, a systematic cloud-based set of tool family is proposed to develop viable knowledge-based systems. For productive participation of domain and cognitive specialists in manufacturing, the knowledge base should be declarative, testable and integratable with other architectural components. Mechanisms to ensure KBS viability are provided in an ontology-oriented development environment, where each component is formed in terms of domain ontology by using the adaptable instrumental support. Due to the explicit separation of ontology from knowledge, it became possible to divide competencies between specialists creating an ontology and specialists creating a knowledge base. We rely on the fact that the activity of creating an ontology is significantly different from the activity of creating a knowledge base. Creating an ontology is a creative process that requires a systematic analysis of the domain area in order to identify common patterns among its knowledge.The characteristic properties of knowledge-based systems related to viability are described. It is explained, how these properties are provided in development environments implemented on cloud platform. The concept of a specialized manufacturing environment for knowledge-based system is introduced. The necessary set of tools for such ontology-oriented environment construction is determined. The example of tools for creating specialized manufacturing environments is the instruments implemented on the «IACPaaS» platform. The IACPaaS is already used for collective development of thematic cloud knowledge portals with viable knowledge-based systems. This specialized manufacturing environment has enabled the creation of multi-purpose medical software services to support specialist solutions based on knowledge being remotely improved by experts.

A Model Approach to Infer the Quality in Agricultural Sprayers Supported by Knowledge Bases and Experimental Measurements

2017 ◽  
Vol 11 (03) ◽  
pp. 279-292 ◽  
Author(s):  
Elmer A. G. Peñaloza ◽  
Paulo E. Cruvinel ◽  
Vilma A. Oliveira ◽  
Augusto G. F. Costa
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Experimental Tests ◽  
Knowledge Bases ◽  
Operating Conditions ◽  
Semantic Interpretation ◽  
Data Consistency ◽  
Flow Measurements ◽  
Spraying Process

This paper presents a method to infer the quality of sprayers based on data collection of the drop spectra and their physical descriptors, which are used to generate a knowledge base to support decision-making in agriculture. The knowledge base is formed by collected experimental data, obtained in a controlled environment under specific operating conditions, and the semantics used in the spraying process to infer the quality in the application. The electro-hydraulic operating conditions of the sprayer system, which include speed and flow measurements, are used to define experimental tests, perform calibration of the spray booms and select the nozzle types. Using the Grubbs test and the quartile-quartile plot an exploratory analysis of the collected data was made in order to determine the data consistency, the deviation of atypical values, the independence between the data of each test, the repeatability and the normal representation of them. Therefore, integrating measurements to a knowledge base it was possible to improve the decision-making in relation to the quality of the spraying process defined in terms of a distribution function. Results shown that the use of advanced models and semantic interpretation improved the decision-making processes related to the quality of the agricultural sprayers.

Diabetes Mellitus - Evaluating the Diagnostic Probabilities

2011 ◽  
pp. 169-177
Author(s):  
Adi Armoni
Keyword(s):  
Diabetes Mellitus ◽  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Decision Maker ◽  
Knowledge Bases ◽  
Bayesian Theory ◽  
Logical Conclusion ◽  
Human Decision

The article examines the behavior of the human decision-maker. It surveys research in which about 90 physicians specializing in various fields and with different degrees of seniority participated. It tackles the question of whether it is possible to found the majority of the knowledge bases of the expert systems on the Bayesian theory. We will discuss the way of decision making conforming to the probabilities evaluated according to the Bayesian theory. The logical conclusion, therefore, is that the development of a knowledge base for an expert system founded on probabilities calculated in accordance with the Bayesian theory must be carried out in a controlled manner and depend on the parameters mentioned above.

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