Cognitive Computing and machinable thought

Cognitive Computing (CC) is an emerging paradigm of intelligent computing methodologies and systems that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain [1, 3, 4, 5, 6, 12, 13, 15, 16, 18, 20, 22, 23]. CC is emerged and developed based on the transdisciplinary research in cognitive informatics and abstract intelligence. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information science, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications [1, 3, 6, 12, 13, 20, 22]. The theoretical framework of cognitive informatics [6] covers the Information-Matter-Energy (IME) model [5], the Layered Reference Model of the Brain (LRMB) [19], the Object-Attribute-Relation (OAR) model of information representation in the brain [7], the cognitive informatics model of the brain [17], Natural Intelligence (NI) [6], and neuroinformatics [6]. Recent studies on LRMB in cognitive informatics reveal an entire set of cognitive functions of the brain and their cognitive process models, which explain the functional mechanisms and cognitive processes of the natural intelligence with 43 cognitive processes at seven layers known as the sensation, memory, perception, action, meta-cognitive, meta-inference, and higher cognitive layers from the bottom up [19].

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[5]  Yingxu Wang,et al.  A layered reference model of the brain , 2003, The Second IEEE International Conference on Cognitive Informatics, 2003. Proceedings..

[6]  Yingxu Wang,et al.  Paradigms of Denotational Mathematics for Cognitive Informatics and Cognitive Computing , 2009, Fundam. Informaticae.

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[8]  Yingxu Wang.  Cognitive Informatics: Towards Future Generation Computers that Think and Feel , 2006, 2006 5th IEEE International Conference on Cognitive Informatics.

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[13]  Yingxu Wang,et al.  On Abstract Intelligence: Toward a Unifying Theory of Natural, Artificial, Machinable, and Computational Intelligence , 2009, Int. J. Softw. Sci. Comput. Intell..

[14]  Yingxu Wang,et al.  Cognitive informatics models of the brain , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Shushma Patel,et al.  A layered reference model of the brain (LRMB) , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Yingxu Wang,et al.  The OAR Model of Neural Informatics for Internal Knowledge Representation in the Brain , 2007, Int. J. Cogn. Informatics Nat. Intell..

[17]  Witold Pedrycz,et al.  A Doctrine of Cognitive Informatics (CI) , 2009, Fundam. Informaticae.

[18]  Yingxu Wang,et al.  The Real-Time Process Algebra (RTPA) , 2002, Ann. Softw. Eng..

[19]  Yingxu Wang.  On Cognitive Informatics , 2003 .

[20]  Yingxu Wang,et al.  The Theoretical Framework of Cognitive Informatics , 2007, Int. J. Cogn. Informatics Nat. Intell..

[21]  Yingxu Wang,et al.  Process-Based Software Engineering: Building the Infrastructures , 2002, Ann. Softw. Eng..

[22]  Yingxu Wang,et al.  On Cognitive Computing , 2009, Int. J. Softw. Sci. Comput. Intell..

[23]  Yingxu Wang,et al.  Editorial Recent Advances in Cognitive Informatics , 2006, IEEE Trans. Syst. Man Cybern. Syst..

[24]  Yingxu Wang,et al.  On Concept Algebra: A Denotational Mathematical Structure for Knowledge and Software Modeling , 2008, Int. J. Cogn. Informatics Nat. Intell..

[25]  Yingxu Wang,et al.  On Visual Semantic Algebra (VSA): A Denotational Mathematical Structure for Modeling and Manipulating Visual Objects and Patterns , 2009, Int. J. Softw. Sci. Comput. Intell..