Cognitive Informatics

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Cognitive Informatics (CI) is an emerging discipline that studies the natural intelligence and internal information processing mechanisms of the brain, as well as the processes involved in perception and cognition. CI provides a coherent set of fundamental theories, and contemporary mathematics, which form the foundation for most information- and knowledge-based science and engineering disciplines such as computer science, cognitive science, neuropsychology, systems science, cybernetics, software engineering, and knowledge engineering.

The development of classical and contemporary informatics, the cross fertilization between computer science, systems science, cybernetics, computer/software engineering, cognitive science, neuropsychology, knowledge engineering, and life science, has led to an entire range of the extremely interesting new research field known as CI [Wang, 2002a, 2003, 2004, 2006a, 2006b, 2007b; Wang et al., 2002, Wang and Kinsner, 2006; Patel et al., 2003, Chan et al., 2004, Kinsner et al., 2005, Yao et al., 2006]. CI is a transdisciplinary study of cognitive and information sciences that investigates the internal information processing mechanisms and processes of the natural intelligence generated by the human brain. CI is a cutting-edge and profound interdisciplinary research area that tackles the fundamental problems shared among aforementioned disciplines. Almost all of the hard problems yet to be solved in the above areas can be deduced onto the common root for understanding the mechanisms of natural intelligence and cognitive processes of the brain.

Contents 1 A New Transdisciplinary Research Field 1.1 Informatics: A Study on the Third Essence of the World 1.2 From Conventional Information Theory to CI 2 The Theoretical Framework of CI 2.1 The Fundamental Theories of CI 2.2 Denotational Mathematics for CI 2.3 The Key Application Areas of CI 3 Applications of CI 3.1 The Architecture of Future Generation Computers 3.2 Estimation the Capacity of Human Memory 3.3 Autonomic Computing 3.4 Simulation of Human Cognitive Behaviors using the Denotational Mathematics 4 Conclusion 5 References

[edit] A New Transdisciplinary Research Field

CI is a new transdisciplinary field of research that investigates into the most common problems of how the brain processes information shared by almost all science and engineering disciplines. This section examines the nature of information and the historical development of the three-generation informatics that lead to the establishment of CI.

[edit] Informatics: A Study on the Third Essence of the World

The basic characteristic of the human brain is information processing. Therefore, information is recognized as the third essence supplementing matter and energy to model the natural world [Wang, 2002a, 2003, 2007b].

Definition 1. Information is any property or attribute of the natural world that can be distinctly elicited, generally abstracted, quantitatively represented, and mentally processed by the brain.

Definition 2. Informatics is the science of information that studies the nature of information, it’s processing, and ways of transformation between information, matter and energy. Theorem 1. A generic world view known as the Information-Matter-Energy (IME) model [Wang, 2007a, 2007b] states that the natural world (NW), which forms the context of human cognitive activities and the natural intelligence, is a dual world: one aspect of it is the physical or the concrete world (PW); the other is the abstract or the perceptive world (AW), where matter (M) and energy (E) are used to model the former, and information (I) to the latter.

According to the IME model, information plays a vital role in connecting the physical world and the abstract world. Models of the natural world have been well studied in physics and other natural sciences. However, the modeling of the abstract world is still a fundamental issue yet to be explored in cognitive informatics, computing, software science, cognitive science, and brain sciences. Especially, the relationships between I-M-E and their transformations are perceived as one of the fundamental questions in CI.

[edit] From Conventional Information Theory to CI

The theories of informatics and their perceptions on the object of information have evolved from the conventional information theory, to modern informatics, and to cognitive informatics in the last six decades. Conventional information theories [Shannon, 1948, Bell, 1953], particularly Shannon’s information theory [Shannon, 1948] known as the first-generation informatics, study signals and channel behaviors, and they are statistics- and probability-based. Modern informatics [Wang, 2007a] known as the second-generation informatics studies information as properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed. The first- and second-generation informatics put emphases on external information processing, which overlook the fundamental fact that human brains are the original sources and final destinations of information, and any information must be cognized by human beings before it is understood. This observation leads to the establishment of the third-generation informatics, a term coined as CI by Wang in 2002 [Wang, 2002a, 2003, 2007b].

Definition 3. CI is the transdisciplinary enquiry of cognitive and information sciences that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach.

The definitions of information and their measurement in the three-generation informatics are summarized in [Wang, 2003]. It is noteworthy that the bit in the 2nd- and 3rd-generation definitions has been shifted from a weighted sum of probability of signals to a more concrete and deterministic entity, and it is no longer probability-based as that of the conventional information theory. It is recognized in CI that cognitive information and knowledge being processing in the brain can be divided into five abstract levels, such as the levels of analogue objects, diagrams, natural languages, professional notation systems, and mathematics (philosophy) from the button-up.

[edit] The Theoretical Framework of CI

In many disciplines of human knowledge, almost all of the hard problems yet to be solved share a common root in the understanding of the mechanisms of natural intelligence and the cognitive processes of the brain. Therefore, CI is a discipline that forges links between a number of natural science and life science disciplines with informatics and computing science. The structure of the theoretical framework of CI encompasses the fundamental theories of CI, descriptive mathematics for CI, and the key application areas of CI.

[edit] The Fundamental Theories of CI

The fundamental theories of CI have been developed in ten aspects resulted in the basic and transdisciplinary research in CI [Wang, 2007b], which encompass the Information-Matter-Energy (IME) model, the Layered Reference Model of the Brain (LRMB) [Wang et al., 2006], the Object-Attribute-Relation (OAR) model [Wang, 2007c] of information representation in the brain, the cognitive informatics model of the brain, Natural Intelligence (NI), Neural Intelligence (NeI) [Wang, 2007b], the CI laws of software, the mechanism of human perception processes , the cognitive processes of formal inferences, and the formal knowledge system [Wang, 2007a].

[edit] Denotational Mathematics for CI

Three new types of denotational mathematics, Concept Algebra (CA) [Wang, 2006d], Real-Time Process Algebra (RTPA) [Wang, 2002b], and System Algebra (SA) [Wang, 2006c], are created for CI to enable rigorous treatment of knowledge representation and manipulation in a formal and coherent framework. The new structures of contemporary mathematics have extended the abstract objects under study in mathematics to a higher level, i.e. concepts, behavioral processes, and systems. A wide range of applications of the descriptive mathematics in the context of CI has been identified [Wang, 2006b].

[edit] The Key Application Areas of CI

The key application areas of CI can be divided into two categories [Wang, 2007b]. The first category of applications uses informatics and computing techniques to investigate cognitive science problems, such as memory, learning, and reasoning. The second category uses cognitive theories to investigate problems in informatics, computing, and software/knowledge engineering. CI focuses on the nature of information processing in the brain, such as information acquisition, representation, memory, retrieve, generation, and communication. Through the interdisciplinary approach and with the support of modern information and neuroscience technologies, mechanisms of the brain and the mind may be systematically explored within the framework of CI.

[edit] Applications of CI

A wide range of applications of CI has been identified in multidisciplinary and transdisciplinary areas, such as: (1) The architecture of future generation computers; (2) Estimation the capacity of human memory; (3) Autonomic computing; (4) Simulation of human cognitive behaviors using denotational mathematics.

[edit] The Architecture of Future Generation Computers

Conventional machines are invented to extend human physical capability, while modern information processing machines, such as computers, communication networks, and robots, are developed for extending human intelligence, memory, and the capacity for information processing [Wang, 2004]. Recent advances in CI provide formal description of an entire set of cognitive processes of the brain [Wang et al., 2006]. The fundamental research in CI also creates an enriched set of contemporary denotational mathematics [Wang, 2006c], for dealing with the extremely complicated objects and problems in natural intelligence, neural informatics, and knowledge manipulation.

The theory and philosophy behind the next generation computers and computing methodologies are CI [Wang, 2004]. It is commonly believed that the future-generation computers, known as the cognitive computers, will adopt non-von Neumann [von Neumann, 1946] architectures. The key requirements for implementing a conventional stored-program controlled computer are the generalization of common computing architectures and the computer is able to interpret the data loaded in memory as computing instructions. These are the essences of stored-program controlled computers known as the von Neumann architecture, which encompasses five essential components to implement general-purpose programmable digital computers.

Definition 4. A von Neumann Architecture (VNA) of computers is a 5-tuple that consists of the components: (a) the arithmetic-logic unit (ALU), (b) the control unit (CU) with a program counter (PC), (c) a memory (M), (d) a set of input/output (I/O) devices, and (e) a bus (B) that provides the data path between these components.

Definition 5. Conventional computers with VNA are aimed at stored-program-controlled data processing based on mathematical logic and Boolean algebra.

A VNA computer is centric by the bus and characterized by the all purpose memory for both data and instructions. A VNA machine is an extended Turing machine (TM), where the power and functionality of all components of TM including the control unit (with wired instructions), the tape (memory), and the head of I/O, are greatly enhanced and extended with more powerful instructions and I/O capacity.

Definition 6. A Wang Architecture (WA) of computers, known as the Cognitive Computers is a parallel structure encompassing an Inference Engine (IE) and a Perception Engine (PE) [Wang, 2006a].

The future generation cognitive computers based on WA are not centered by a CPU for data manipulation as the VNA computers do. The WA computers are centered by the concurrent IE and PE for cognitive learning and autonomic perception based on abstract concept inferences and empirical stimuli perception. The IE is designed for concept/knowledge manipulation according to concept algebra [Wang, 2006d], particularly the 9 concept operations for knowledge acquisition, creation, and manipulation. The PE is designed for sensory and perception processing according to RTPA [Wang, 2002b] and the formally described cognitive process models of the perception layers as defined in the LRMB model [Wang et al., 2006].

Definition 7. Cognitive computers with WA are aimed at cognitive and perceptive concept/knowledge processing based on contemporary denotational mathematics, i.e. concept algebra, Real-Time Process Algebra (RTPA), and system algebra.

As that of mathematical logic and Boolean algebra are the mathematical foundations of VNA computers. The mathematical foundations of WA computers are based on denotational mathematics [Wang, 2006b, 2006c]. As described in the LRMB reference model [Wang et al., 2006], since all the 39 fundamental cognitive processes of human brains can be formally described in CA and RTPA [Wang, 2002b, 2006e]. In other words, they are simulatable and executable by the WA-based cognitive computers.

[edit] Estimation the Capacity of Human Memory

Despite the fact that the number of neurons in the brain has been identified in cognitive and neural sciences, the magnitude of human memory capacity is still unknown. According to the Object-Attribute-Relation (OAR) model [Wang, 2007c], a recent discovery in CI is that the upper bound of memory capacity of the human brain is in the order of 108,432 bits [Wang et al., 2003]. The determination of the magnitude of human memory capacity is not only theoretically significant in CI, but also practically useful to explain the human potential, as well as the gaps between the natural and machine intelligence. This work indicates that the next generation computer memory systems may be built according to the OAR model rather than the traditional container metaphor, because the former is more powerful, flexible, and efficient to generate a tremendous memory capacity by using limited number of neurons in the brain or hardware cells in the next generation computers.

[edit] Autonomic Computing

The approaches to implement intelligent systems can be classified into those of biological organisms, silicon automata, and computing systems. Based on CI studies, autonomic computing [Wang, 2004] is proposed as a new and advanced computing technique built upon the routine, algorithmic, and adaptive systems. The approaches to computing can be classified into two categories known as imperative and autonomic computing. Corresponding to these, computing systems may be implemented as imperative or autonomic computing systems.

Definition 8. An imperative computing system is a passive system that implements deterministic, context-free, and stored-program controlled behaviors.

Definition 9. An autonomic computing system is an intelligent system that autonomously carries out robotic and interactive actions based on goal- and event-driven mechanisms.

The imperative computing system is a traditional passive system that implements deterministic, context-free, and stored-program controlled behaviors, where a behavior is defined as a set of observable actions of a given computing system. The autonomic computing system is an active system that implements nondeterministic, context-dependent, and adaptive behaviors, which do not rely on instructive and procedural information, but are dependent on internal status and willingness that formed by long-term historical events and current rational or emotional goals.

The first three categories of computing techniques as shown in Table 3 are imperative. In contrast, the autonomic computing systems are an active system that implements nondeterministic, context-sensitive, and adaptive behaviors. Autonomic computing does not rely on imperative and procedural instructions, but are dependent on perceptions and inferences based on internal goals as revealed in CI.

[edit] Simulation of Human Cognitive Behaviors using the Denotational Mathematics

The contemporary denotational mathematics for CI, particularly concept algebra and RTPA, may be used to simulate the cognitive processes of the brain as modeled in LRMB [Wang et al., 2006]. Most of the 39 cognitive processes identified in LRMB, such as the learning [Wang, 2007d], inference [Wang, 2007e], and decision making [Wang and Ruhe, 2007] processes, have been rigorously modeled and described in RTPA and cognitive algebra. Based on the fundamental work, the inference engine and perception engine of a virtual brain can be implemented on cognitive computers or be simulated on conventional computers. In the former case, a working prototype of a fully autonomic computer will be realized on the basis of CI theories.

[edit] Conclusion

Cognitive informatics (CI) has been recognized as a new frontier that studies internal information processing mechanisms and processes of the brain, and their applications in computing and the IT industry. This paper has provided an insightful perspective on the past, present, and future of CI, which reviews the development of informatics from the classical information theory, contemporary informatics, to cognitive informatics. Based on this, the foundations of cognitive informatics and its potential applications have been explored. Cognitive informatics has been described as a profound interdisciplinary research area that tackles the common root problems of modern informatics, computation, software engineering, AI, cognitive science, nueropsychology, and life sciences. The future generation computers such as cognitive computers will be developed on the basis of cognitive informatics theories and models.

[edit] References

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