International Journal of Software Science and Computational Intelligence 

About: International Journal of Software Science and Computational Intelligence is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Computer science & Cognition. It has an ISSN identifier of 1942-9045. Over the lifetime, 317 publications have been published receiving 3075 citations. The journal is also known as: IJSSCI.

On Cognitive Computing

Abstract: 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. [Article copies are available for purchase from InfoSci-on-Demand.com]

On Abstract Intelligence: Toward a Unifying Theory of Natural, Artificial, Machinable, and Computational Intelligence

Abstract: intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. This paper describes the taxonomy and nature of intelligence. It analyzes roles of information in the evolution of human intelligence, and the needs for logical abstraction in modeling the brain and natural intelligence. A formal model of intelligence is developed known as the Generic Intelligence Mode (GAIM), which provides a foundation to explain the mechanisms of advanced natural intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is comparatively studied in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of the GAIM model and the abstract intelligence theories, the compatibility of natural and machine intelligence is revealed the compatibility of natural and machine intelligence is revealed in order to investigate into a wide range of paradigms of abstract intelligence such as natural, artificial, machinable intelligence, and their engineering applications.

On the System Algebra Foundations for Granular Computing

Abstract: Granular computing studies a novel approach to computing system modeling and information processing. Although a rich set of work has advanced the understanding of granular computing in dealing with the “to be” and “to have” problems of systems, the “to do” aspect of system modeling and behavioral implementation has been relatively overlooked. On the basis of a recent development in denotational mathematics known as system algebra, this paper presents a system metaphor of granules and explores the theoretical and mathematical foundations of granular computing. An abstract system model of granules is proposed in this paper. Rigorous manipulations of granular systems in computing are modeled by system algebra. The properties of granular systems are analyzed, which helps to explain the magnitudes and complexities of granular systems. Formal representation of granular systems for computing is demonstrated by real-world case studies, where concrete granules and their algebraic operations are explained.

On Visual Semantic Algebra (VSA): A Denotational Mathematical Structure for Modeling and Manipulating Visual Objects and Patterns

Abstract: A new form of denotational mathematics known as Visual Semantic Algebra (VSA) is presented for abstract visual object and architecture manipulations. A set of cognitive theories for pattern recognition is explored such as cognitive principles of visual perception and basic mechanisms of object and pattern recognition. The cognitive process of pattern recognition is rigorously modeled using VSA and Real-Time Process Algebra (RTPA), which reveals the fundamental mechanisms of natural pattern recognition by the brain. Case studies on VSA in pattern recognition are presented to demonstrate VAS’ expressive power for algebraic manipulations of visual objects. VSA can be applied not only in machinable visual and spatial reasoning, but also in computational intelligence as a powerful man-machine language for representing and manipulating visual objects and patterns. On the basis of VSA, computational intelligent systems such as robots and cognitive computers may process and inference visual and image objects rigorously and efficiently. DOI: 10.4018/jssci.2009062501 IGI PUBLISHING This paper appears in the publication, International Journal of Software Science and Computational Intelligence, Volume 1, Issue 4 edited by Yingxu Wang © 2009, IGI Global 701 E. Chocolate Avenue, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.igi-global.com ITJ 5373 2 International Journal of Software Science and Computational Intelligence, 1(4), 1-16, October-December 2009 Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. recognition, particularly how the natural intelligence processes visual objects and patterns (Wang, 2008d), as well as their denotational mathematical models (Wang, 2008a, 2008b). The gestalt (holistic) principles of visual perception were developed in Germany based on experiments conducted in the 1920s and 1930s (Gray, 1994; Westen, 1999). Five gestalt principles for object and pattern perception were elicited (Kanizsa, 1979), such as similarity, proximity, good continuation, simplicity, closure, and background contrast. The gestalt principles reveal a set of important natural tendencies of human visual perception. Another set of seven cognitive informatics principles of visual object perception is identified in (Wang, 2009c) known as association, symmetry, perfection, abstraction, categorization, analysis, and appreciation, which are used in perception and identification of human figures, physical objects, abstract structure, mathematics entities, and nature. A variety of theories and approaches are proposed for visual object and pattern recognition. Marr proposed a method for object recognition in the algorithmic approach known as the computational method (Marr, 1982). Biederman developed a method for object recognition in the analytic approach called recognition by components (Biedeman, 1987). Various methods and technologies are developed for pattern recognition in the fields of cognitive psychology (payne and Wenger, 1998; Reed, 1972; Wilson and Keil, 2001), computer science (Bender, 2000; bow, 1992; Miclet, 1986; Storer, 2002), and robotics (Horn, 1986; Murry et al., 1993). Wang presents a cognitive theory of visual information processing as well as the unified framework of human visual processing systems (Wang, 2009c) in the development of cognitive informatics – a formal theory for explaining the natural and computational intelligence (Wang, 2002a, 2003, 2007b; Wang and Kinsner, 2006; Wang et al., 2002, 2008b, 2009a, 2009b). A set of denotational mathematics (Wang, 2006, 2008a), such as concept algebra (Wang, 2008c), system algebra (Wang, 2008d), Real-Time Process Algebra (RTPA) (Wang, 2002b, 2007a, 2008b), and granular algebra (Wang, 2009d), are created in order to rigorously manipulate complex mental processes and computational intelligence. This article presents the cognitive process of pattern recognition and the denotational mathematical means known as Visual Semantic Algebra (VSA). The cognitive informatics theories for pattern recognition are explored such as cognitive principles of visual perception and basic mechanisms of object and pattern recognition. A generic denotational mathematical means, VSA, is developed to manipulate basic geometric shapes and figures, as well as their compositions by a set of algebraic operations. A number of case studies are provided to explain the expressive power of VSA and its applications. COGNITIVE INFORMATICS THEORIES FOR PATTERN RECOGNITION It is recognized that the brain tends to perform inference and reasoning using abstract semantic objects rather than direct visual (diagram-based) objects (Coaen et al., 1994; Wang, 2009c). This is evidenced by that the brain cannot carry out concrete image inference in Short-Term Memory (STM) without looking at them in external media such as figures or pictures on article, because this cognitive process requires too large memory beyond the capacity of STM in the brain basic Mechanisms of Object Recognition Definition 1. Object recognition is a special type of pattern recognition where the patterns are frequently used 2-D shapes, 3-D solid figures, and their compositions. 14 more pages are available in the full version of this document, which may be purchased using the \"Add to Cart\" button on the product's webpage: www.igi-global.com/article/visual-semantic-algebra-

A Formal Knowledge Representation System FKRS for the Intelligent Knowledge Base of a Cognitive Learning Engine

Abstract: It is recognized that the generic form of machine learning is a knowledge acquisition and manipulation process mimicking the brain. Therefore, knowledge representation as a dynamic concept network is centric in the design and implementation of the intelligent knowledge base of a Cognitive Learning Engine CLE. This paper presents a Formal Knowledge Representation System FKRS for autonomous concept formation and manipulation based on concept algebra. The Object-Attribute-Relation OAR model for knowledge representation is adopted in the design of FKRS. The conceptual model, architectural model, and behavioral models of the FKRS system is formally designed and specified in Real-Time Process Algebra RTPA. The FKRS system is implemented in Java as a core component towards the development of the CLE and other knowledge-based systems in cognitive computing and computational intelligence.