scholarly journals Review of Artificial Intelligence

2020 ◽  
pp. 143-171
Author(s):  
K. P. V. Sai Aakarsh ◽  
Adwin Manhar
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Medical Diagnosis ◽  
Computer Application ◽  
Knowledge Based Systems ◽  
Human Race ◽  
Knowledge Based ◽  
Ordinary Person ◽  
Digital Computers

Over many centuries, tools of increasing sophistication have been developed to serve the human race Digital computers are, in many respects, just another tool. They can perform the same sort of numerical and symbolic manipulations that an ordinary person can, but faster and more reliably. This paper represents review of artificial intelligence algorithms applying in computer application and software. Include knowledge-based systems; computational intelligence, which leads to Artificial intelligence, is the science of mimicking human mental faculties in a computer. That assists Physician to make dissection in medical diagnosis.

RIGOROUS ENGINEERING PRACTICE AND FORMAL REASONING OF DEEP DOMAIN KNOWLEDGE—THE BASIS OF DEPENDABLE KNOWLEDGE BASED SYSTEMS FOR PROCESS PLANT CONTROL

1993 ◽  
Vol 03 (01) ◽  
pp. 53-98 ◽  
Author(s):  
SANDRO BOLOGNA ◽  
TERJE SIVERTSEN ◽  
HEIKKI VÄLISUO
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Engineering Practice ◽  
Knowledge Based Systems ◽  
Control Engineering ◽  
Engineering Software ◽  
Knowledge Based ◽  
Process Plant ◽  
Verification Process ◽  
The Many

Knowledge based systems are often used to replace humans in solving problems for which only heuristic knowledge on the solution is available. However, there are also important application areas where nonheuristic knowledge is available e.g. in technical documents but where efficient use of the knowledge is impossible without the techniques provided by artificial intelligence. High dependability of these kinds of applications can be achieved if domain knowledge can be represented in a language providing both adequate representational constructs and the required level of formality. In addition, the language should be supported by powerful tools assisting in the verification process. Knowledge Based Systems, despite the different technology employed, are still nothing more than a computer program. Unfortunately, quite a few people building knowledge based systems seem to ignore the many good programming practices that have evolved over the years for producing traditional computer programs. What we need is a framework for the modelling of the KBSs development. In our work, it is claimed that these requirements can be met by utilizing and combining ideas from control engineering, software engineering and artificial intelligence.

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.

Knowledge-Based Systems

2011 ◽  
pp. 989-995 ◽  
Author(s):  
Adrian A. Hopgood
Keyword(s):  
Computational Intelligence ◽  
Early Research ◽  
Computer Hardware ◽  
Case Based Reasoning ◽  
Major Theme ◽  
Knowledge Based Systems ◽  
Rule Based ◽  
Knowledge Based ◽  
Explicit Representations ◽  
Case Based

The tools of artificial intelligence (AI) can be divided into two broad types: knowledge-based systems (KBSs) and computational intelligence (CI). KBSs use explicit representations of knowledge in the form of words and symbols. This explicit representation makes the knowledge more easily read and understood by a human than the numerically derived implicit models in computational intelligence. KBSs include techniques such as rule-based, modelbased, and case-based reasoning. They were among the first forms of investigation into AI and remain a major theme. Early research focused on specialist applications in areas such as chemistry, medicine, and computer hardware. These early successes generated great optimism in AI, but more broad-based representations of human intelligence have remained difficult to achieve (Hopgood, 2003; Hopgood, 2005).

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