On Cognitive Models of Causal Inferences and Causation Networks

2011 ◽  
Vol 3 (1) ◽  
pp. 50-60 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Problem Solving ◽  
Mathematical Models ◽  
Causal Inference ◽  
Computational Intelligence ◽  
Theoretical Foundation ◽  
Cognitive Models ◽  
Human Reasoning ◽  
Cognitive Computing ◽  
Causal Inferences ◽  
Human Thought

Human thought, perception, reasoning, and problem solving are highly dependent on causal inferences. This paper presents a set of cognitive models for causation analyses and causal inferences. The taxonomy and mathematical models of causations are created. The framework and properties of causal inferences are elaborated. Methodologies for uncertain causal inferences are discussed. The theoretical foundation of humor and jokes as false causality is revealed. The formalization of causal inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, and computational intelligence.

On Cognitive Models of Causal Inferences and Causation Networks

2013 ◽  
pp. 103-113
Author(s):  
Yingxu Wang
Keyword(s):  
Problem Solving ◽  
Mathematical Models ◽  
Causal Inference ◽  
Computational Intelligence ◽  
Theoretical Foundation ◽  
Cognitive Models ◽  
Human Reasoning ◽  
Cognitive Computing ◽  
Causal Inferences ◽  
Human Thought

Human thought, perception, reasoning, and problem solving are highly dependent on causal inferences. This paper presents a set of cognitive models for causation analyses and causal inferences. The taxonomy and mathematical models of causations are created. The framework and properties of causal inferences are elaborated. Methodologies for uncertain causal inferences are discussed. The theoretical foundation of humor and jokes as false causality is revealed. The formalization of causal inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, and computational intelligence.

Formal Rules for Fuzzy Causal Analyses and Fuzzy Inferences

2012 ◽  
Vol 4 (4) ◽  
pp. 70-86 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Computational Intelligence ◽  
Fuzzy Inference ◽  
Human Reasoning ◽  
Linguistic Variables ◽  
Cognitive Computing ◽  
Extended Form ◽  
Rational Reasoning ◽  
Formal Rules

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.

scholarly journals Abstract Intelligence

2017 ◽  
Vol 11 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Yingxu Wang ◽  
Lotfi A. Zadeh ◽  
Bernard Widrow ◽  
Newton Howard ◽  
Françoise Beaufays ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mathematical Models ◽  
Computational Intelligence ◽  
Cognitive Learning ◽  
Cognitive Systems ◽  
Stanford University ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Basic Studies ◽  
Cognitive Robots

Basic studies in denotational mathematics and mathematical engineering have led to the theory of abstract intelligence (aI), which is a set of mathematical models of natural and computational intelligence in cognitive informatics (CI) and cognitive computing (CC). Abstract intelligence triggers the recent breakthroughs in cognitive systems such as cognitive computers, cognitive robots, cognitive neural networks, and cognitive learning. This paper reports a set of position statements presented in the plenary panel (Part II) of IEEE ICCI*CC'16 on Cognitive Informatics and Cognitive Computing at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

Abstract Intelligence

2020 ◽  
pp. 52-69
Author(s):  
Yingxu Wang ◽  
Lotfi A. Zadeh ◽  
Bernard Widrow ◽  
Newton Howard ◽  
Françoise Beaufays ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mathematical Models ◽  
Computational Intelligence ◽  
Cognitive Learning ◽  
Cognitive Systems ◽  
Stanford University ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Basic Studies ◽  
Cognitive Robots

Basic studies in denotational mathematics and mathematical engineering have led to the theory of abstract intelligence (aI), which is a set of mathematical models of natural and computational intelligence in cognitive informatics (CI) and cognitive computing (CC). Abstract intelligence triggers the recent breakthroughs in cognitive systems such as cognitive computers, cognitive robots, cognitive neural networks, and cognitive learning. This paper reports a set of position statements presented in the plenary panel (Part II) of IEEE ICCI*CC'16 on Cognitive Informatics and Cognitive Computing at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

Certain models of granular computing based on rough fuzzy approximations

2020 ◽  
Vol 39 (3) ◽  
pp. 2797-2816
Author(s):  
Muhammad Akram ◽  
Anam Luqman ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Granular Computing ◽  
Research Study ◽  
Network Models ◽  
Mathematical Framework ◽  
Human Reasoning ◽  
Accuracy Measures ◽  
Lower And Upper Approximations

An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

Perspectives on Cognitive Informatics and Cognitive Computing

2010 ◽  
Vol 4 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Yingxu Wang ◽  
George Baciu ◽  
Yiyu Yao ◽  
Witold Kinsner ◽  
Keith Chan ◽  
...  
Keyword(s):  
Computational Linguistics ◽  
Computational Intelligence ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain ◽  
Science Information ◽  
Intelligence Science

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

Perspectives on Cognitive Informatics and Cognitive Computing

2012 ◽  
pp. 1-24
Author(s):  
Yingxu Wang ◽  
George Baciu ◽  
Yiyu Yao ◽  
Witold Kinsner ◽  
Keith Chan ◽  
...  
Keyword(s):  
Computational Linguistics ◽  
Computational Intelligence ◽  
Social Computing ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain ◽  
Science Information

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

On Cognitive Computing

2012 ◽  
pp. 83-97
Author(s):  
Yingxu Wang
Keyword(s):  
Computational Intelligence ◽  
Autonomic Computing ◽  
Theoretical Framework ◽  
Autonomous Agent ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Agent Systems ◽  
The Brain ◽  
Theoretical Foundations

Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing.

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