Mathematical methods in artificial intelligence
Mathematical methods in artificial intelligence
The computer science and engineering handbook
The computer science and engineering handbook
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Software Engineering Foundations: A Software Science Perspective
Toward Formal Models of the Theoretical Framework of Fundamental Economics
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Software and Intelligent Sciences: New Transdisciplinary Findings
Breakthroughs in Software Science and Computational Intelligence
Breakthroughs in Software Science and Computational Intelligence
Perspectives on Cognitive Informatics and Cognitive Computing
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International Journal of Software Science and Computational Intelligence
Empirical Studies on the Functional Complexity of Software in Large-Scale Software Systems
International Journal of Software Science and Computational Intelligence
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Causal inference is one of the central capabilities of the natural intelligence that plays a crucial role in thinking, perception, and problem solving. Fuzzy inferences are an extended form of formal inferences that provide a denotational mathematical means for rigorously dealing with degrees of matters, uncertainties, and vague semantics of linguistic variables, as well as for rational reasoning the semantics of fuzzy causalities. This paper presents a set of formal rules for causal analyses and fuzzy inferences such as those of deductive, inductive, abductive, and analogical inferences. Rules and methodologies for each of the fuzzy inferences are formally modeled and illustrated with real-world examples and cases of applications. The formalization of fuzzy inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, soft computing, abstract intelligence, and computational intelligence.