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Call For Papers - MIKE Conference Series
Call For Papers - MIKE Conference Series

... Human Intelligence has evolved steadily over several generations and in modern days, human expertise is excelling much better in individual domains and knowledge acquiring tasks. So Mining Human Intelligence becomes an essential and incredible part of human expertise / knowledge exploration. MIKE 20 ...
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AI Introduction PDF document
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Declarative programming
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... • In AI declarative programming is used to express specifications for problem solutions in the form of mathematical logic. • This style of programming is called logic programming. • The resulting programs are often referred to as expert systems since they contain a knowledge database that can be ask ...
Report on the Nineteenth International FLAIRS Conference
Report on the Nineteenth International FLAIRS Conference

... Dublin, “Personalizing Web Search: Communities and Collaboration.” The general conference sessions featured papers ranging from foundational issues in reasoning and knowledge representation, through papers decribing architectures for a range of AI systems, to papers reporting on industrial applicati ...
Components of KBS
Components of KBS

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Multi-Agent Case-Based Diagnosis in the Aircraft Domain
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CHIME: Service-oriented Framework for Adaptive Web

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A Multi-intelligent Agent System for Automatic Construction of Rule
A Multi-intelligent Agent System for Automatic Construction of Rule

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Semantic Memory for Avatars in Cyberspace
Semantic Memory for Avatars in Cyberspace

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Knowledge representation and reasoning

Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.Examples of knowledge representation formalisms include semantic nets, systems architecture, Frames, Rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers.
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