Cognitive Informatics: Towards Cognitive Machine Learning and Autonomous Knowledge Manipulation

Cognitive Informatics: Towards Cognitive Machine Learning and Autonomous Knowledge Manipulation

Yingxu Wang (International Institute of Cognitive Informatics and Cognitive Computing (ICIC), University of Calgary, Calgary, Canada), Newton Howard (University of Oxford, Oxford, UK), Janusz Kacprzyk (Polish Academy of Sciences, Warsaw, Poland), Ophir Frieder (Georgetown University, Washington, DC, USA), Phillip Sheu (University of California, Irvine, CA, USA), Rodolfo A. Fiorini (Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano University, Milano, Italy), Marina L. Gavrilova (Department of Computer Science, University of Calgary, Calgary, Canada), Shushma Patel (Faculty of Business, London South Bank University, London, UK), Jun Peng (Chongqing University of Science and Technology, Chongqing, China) and Bernard Widrow (Stanford University, Stanford, CA, USA)
DOI: 10.4018/IJCINI.2018010101
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Cognitive Informatics (CI) is a contemporary field of basic studies on the brain, computational intelligence theories and underpinning denotational mathematics. Its applications include cognitive systems, cognitive computing, cognitive machine learning and cognitive robotics. IEEE ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This paper reports the plenary panel (Part I) at IEEE ICCI*CC'17 held at Oxford University. The summary is contributed by invited keynote speakers and distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and cognitive computing.
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1. Introduction

Cognitive Informatics (CI) is a transdisciplinary enquiry of the internal information processing processes of the brain and abstract intelligence towards applications in cognitive computing and cognitive engineering (Wang, 2002, 2003, 2007a, 2009a, 2009b, 2009c, 2011b, 2012e, 2013c, 2015a, 2016a, 2017a; Wang et al., 2009, 2010, 2016; Howard et al., 2017). CI is a contemporary field spanning across computer science, information science, cognitive science, brain science, neuroscience, intelligence science, knowledge science, robotics, cognitive linguistics, cognitive philosophy, and cognitive engineering. Cognitive Computing (CC) is a novel paradigm of intelligent computing platforms of cognitive methodologies and systems based on CI, which embodying computational intelligence by cognitive and autonomous systems mimicking the mechanisms of the brain (Wang, 2002, 2007a; Wang et al., 2002, 2007a, 2009a, 2009b, 2016a, 2017a).

The IEEE series of International Conferences on Cognitive Informatics and Cognitive Computing (ICCI*CC) has been established since 2002 (Wang, 2002; Wang et al, 2002). Since its inception, the ICCI*CC series has been growing steadily in its size, scope, and depth. It has attracted worldwide researchers from academia, government agencies, and industry practitioners. The IEEE ICCI*CC series provides a main forum for the exchange and cross-fertilization of ideas in the new research field of CI toward revealing the cognitive mechanisms and processes of human information processing and the approaches to mimic them in cognitive computing. A wide range of breakthroughs have been recognized and a wide range of applications has been developed in CI and CC in the last decade. The representative paradigms and technologies developed in cognitive informatics include cognitive computers, abstract intelligence, cognitive learning engines, cognitive knowledge bases, denotational mathematics and applied cognitive systems.

CI studies the cognitive objects represented in the brain in the categories of data, information, knowledge and intelligence by a hierarchical structure (Berkeley, 1954; Turing, 1950; Shannon, 1948; von Neumann, 1958; McCulloch, 1965; Debenham, 1989; Wang, 2009a, 2014a, 2015f). The relationship between the four categories of cognitive objects in the hierarchical framework of human cognition is illustrated in Figure 1 (Wang, 2016b). It is perceived that data are acquired raw information which are usually a quantitative abstraction of external entities and their relations. Information is meaningful data or an interpretation of data. Knowledge is consumed information related to existing knowledge in the brain. Intelligence is a collection of cognitive abilities of humans or cognitive systems that transforms information into behaviors (Wang, 2012c). Rigorous models and mathematical manipulations on intelligence, knowledge, information and data are formally described in (Wang, 2015f).

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