On cognitive informatics - Cognitive Informatics, 2002 ...

ON COGNITIVE INFORMATICS Yingxu Wang Theoretical and Empirical Software Engineering Research Center (TESERC) Dept. of Electrical & Computer Engineering University of Calgary 2500 University Drive NW , Calgary, Alberta, Canada T2N 1N4 Tel: (403) 220-6141, Fax: (403) 282-6855 Email: [email protected]

Abstract Information is the third essence in modeling the natural world. This paper attempts to explore an emerging discipline know as cognitive informatics. Cognitive Informatics is a profound interdisciplinary research area that tackles the common root problems of modern informatics, computation, software engineering, artificial intelligence (AI), neural psychology, and cognitive science. Cognitive informatics studies the internal information processing mechanisms and natural intelligence of the brain. This paper describes historical development of informatics from the classical information theory, contemporary informatics, to cognitive informatics. The domain of cognitive informatics and its interdisciplinary nature are explored. Foundations of cognitive informatics, particularly the brain vs. the mind, the acquired life functions vs. the inherited ones, and generic relationships between information, mater and energy are investigated. The potential engineering applications of cognitive informatics and perspectives on future research are discussed. It is expected that the investigation into cognitive informatics will result in fundamental findings towards the development of next generation IT and software technologies, such as neural computers, bioinformatics, quantum information processing, new software development approaches, and new architectures of information systems. Keywords: Cognitive informatics, CI, information theory, contemporary informatics, cognitive science, neural psychology, AI, computing, software engineering

1. INTRODUCTION1 Information is the third essence of the natural world

supplementing matter and energy. Informatics, the science of information, studies the nature of information, it’s processing, and ways of transformation between information, matter and energy. Informatics has developed from the classical information theory [1-3, 5-6], contemporary informatics [9, 13, 14], to cognitive informatics [7, 10-12] in the past half century. It is interesting to note that philosophers in the world shared similar perceptions towards cognition, information, and the natural intelligence. A famous historical story (before 500 B.C.) told that when two Chinese philosophers walking around a lake saw fishes swimming and jumping lively and freely in the water, the following dialogue was stimulated: Philosopher A: “The fishes must be very happy because they are lively playing in the lake.” Philosopher B: “Well, you are not a fish. How do you know they are happy?” Philosopher A: “Then, you are not me. How do you know that I don’t know the feeling of the fishes.” This is a good situation to demonstrate the objectives of the study in cognitive informatics – how we, as human beings, acquire, process, interpret, and express information by using the brain, and how we understand the minds of different individuals. Rene Descartes (1596-1650), French philosopher and mathematician, believed that the commonly accepted knowledge would be doubtful because of the subjective nature of human senses. He attempted to rebuild human knowledge structure using the fundamental concept known as 'cogito ergo sum' (I think, therefore I am). Descartes said:

Keynote Lecture Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) ISBN: 0-7695-1724-2 2002 IEEE

“If you would be a real seeker after truth, it is necessary that at least once in your life you doubt, as far as possible, all things.”

34 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE

Erwin Schrodinger, the 1933 Nobel prize laureate in physics, expressed a similar concern: “Is my world really the same as yours? Is there one real world to be distinguished from its pictures introjected by way of perception into every one of us? And if so, are these pictures like unto the real world or is the latter, the world 'in itself' perhaps very different from the one we perceive? Such questions are ingenious, but in my opinion very apt to confuse the issue. There have been no adequate answers.” The author perceives that human beings are living in two worlds. One is the physical or the concrete world; the other is the abstract or the perceived world. We use matter and energy to model the former, and information to the latter. An information-matter-energy (I-M-E) model, as shown in Fig. 1, is developed to describe the generic view towards the physical and information worlds that we are living and doing research.

2. THE CLASSICAL INFORMATICS The classical information theory is commonly regarded to be founded by Shannon during 1948-1949 [5, 6], while the term information was first adopted by Hartley in 1928 [3], and extensively discussed by Bell and Goldman in 1953 [1, 2]. In the classical information theory, Shannon defined information as a probabilistic measure of the quantity of message which can be obtained from a message source [6]. Conventional information theory was modeled based on probability theory, and was focused on information transmission rather than information itself.

The abstract world

I

M

root problem – how the natural intelligence processes information. This perhaps is the last thing in the world yet to be explored and understood. Cognitive informatics can be formally defined as follows: Definition 1. Cognitive informatics (CI) is an emerging disciplinary that studies the internal information processing mechanisms of the brain and their engineering applications, via an interdisciplinary approach. This paper attempts to describe the historical development of informatics from the classical information theory, to contemporary informatics, and to cognitive informatics. Section 2 reviews the classical information theory and their impacts on communication and information transmission techniques. Section 3 describes the contemporary informatics developed in the last three decades. Section 4 addresses the implication and extension of CI, and its domains of study and applications. The foundations of CI and their potential impacts will be explored in Section 5.

E The ph ysical world

Fig. 1. The I-M-E model of the world view

2.1 Information and its Measurement

Models of the natural world have been well studied in physics and other natural sciences. Relationships between matter and energy have been investigated and revealed by Einstein and other scientists. However, the modeling of the abstract world is still a fundamental issue yet to be explored, in informatics, computing, software science, and lift sciences. Especially, the relationships between I-M-E and their transformations are perceived as one of the fundamental questions in cognitive informatics [7, 8]. It is believed that any breakthrough in this area, know as cognitive informatics, will be profoundly significant towards the development of the next generation technologies in informatics, computing, software, and cognitive sciences. One of the most interesting things found in cognitive informatics is that so many science and engineering disciplines, such as informatics, computing, software engineering, and cognitive sciences, sharing a common

In the early 1940s, it was thought that increasing the transmission rate of information over a communication channel increased the probability of error. Shannon surprised the communication theory community by proving that this was not true as long as the communication rate was below channel capacity. The capacity can be simply computed from the noise characteristics of the channel. The classical information theory was probabilitybased. It defined information as follows: Definition 1: Information is a probabilistic measure of the quantity of messages (signals) that can be obtained from a message source via a transmission channel. The information content, Ii, of the ith sign in a message is determined by its unexpectedness, i.e.:

35 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE

Ii = log2 (1/pi)

[bit]

(1)

where pi is the probability that the ith sign is transmitted. The unit of information is bit, shortened from ‘binary digit.’ The total information transmitted by a source or sender, I, is the weighted sum of its n possible signs in the message [6], i.e.:

å å å n

I =

pi • Ii

i =1 n

=

i =1 n

pi • log2 (1/pi ) pi • log2 pi

=-

i =1

[bit]

(2)

Ht + Hi = k

For example, for a binary source that has an alphabet of two equally likely signs, or pi = 0.5, its information content, I, is:

å å 2

I =

i =1

pi • log2 (1/pi)

2

=

i =1

Expression 3 shows that entropy may be measured by the quantity of information. The maximum entropy for a source occurs if the probabilities of all signals are equal [6]. There are two types of entropies: the thermal entropy and information entropy. The former exists in a physical system; the latter exists in an animate system, such as the brain or a human society. The second law of thermodynamics asserts that the thermal entropy in a closed physical system is conservative. When introducing the information entropy, an extension of the 2nd law of thermodynamics [1, 2] can be described as follows: In a closed system, the sum of the information entropy Hi and the thermal entropy Ht is a constant, i.e.:

0.5 • log2 (1/0.5)

(4)

where k is a constant. Expression 4 indicates that for the decrease of the thermal entropy of a system, the information entropy has to be increased. Therefore, the information entropy is also perceived as the negative entropy. In physical systems, entropy can be reserved by inputs of energy to achieve an increased order and organization. In neural and social systems, order and the state of organization can be increased by inputting information.

= 1 [bit] It is noteworthy that for the above binary system, the content of information is always 1 [bit], or generally I is not proportional to the sizes of messages, according to Expression 2. That is, on the basis of the classical information measurement, no matter how many bits message have been transmitted, the value of content of the information will not change for a given transmission system. This result is surprisingly contradictory to the common concept of information in contemporary informatics (see Section 3.1).

2.3 Domain of Classical Information Theory The domain of the classical information theory encompasses communication and coding theories. The structure of the domain of classical information theory can be described as follows:

2.2 Entropy Another important concept in the classical information theory is entropy [1, 2, 6]. Definition 2. Entropy is the extent of the trend of a system towards complete disorder or randomization. The quantity of entropy of a source is defined as its total weighted information transmitted by the source, i.e.:

•

Communication theories • Models of communication channels • Noises • Signal processing

•

Coding theories • Encoding and decoding • Data compression • Data protection • Data encryption

The classical information theory answers two fundamental questions in communication theory:

H=I

å å n

=

i =1 n

=

i =1

pi • Ii pi • log2 (1/pi )

[bit]

(3)

36 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE

•

What is the ultimate transmission rate of communication?

•

What is the maximum rate of data compression?

For the former, Shannon revealed that the ultimate transmission rate of a channel is the maximum channel capacity. For the latter, Shannon answered that the maximum data compression rate is the entropy (information) of the data [6]. In addition to being the foundation of communication theory, the classical information theory has also found a wide range of applications in algorithm complexity analysis in computer science, functional and information sizes measurement in software engineering, and statistic mechanisms in physics. 2.4 The Subjective Dilemma in Classical Information Theory A dilemma in the classical information theory is that the measurement of the quantity of information is dependent on the receiver’s subjective judgment. According to the classical information theory, information is the message that one does not expect and does not know. Therefore, the following corollaries and assertions according to the classical informatics may be doubtable: • If the contents of a message are already known, there is no information. • The same message may have information for some people, and no information for others. • Every time, when one reads the same message, the information that one may get is different. • A message represented in one’s own native language may contain no information; but in another language, it does.

In the last three decades, the domain of information theories has been extended along the development in computer science and the information technology (IT) industry. The modern informatics tends to regard information as entities of messages, rather than a probabilistic measurement of messages as that of the classical information theory. The new perception is found better to explain the theories in computer science and practices in the IT industry. 3.1 Modern Perception on Information It is observed that in applied computer and software sciences and in the IT industries, the term information has a much more practical and concrete meaning that focuses on data and knowledge representation, storage and processing. With this orientation, information is regarded as an entity of messages, rather than a measurement or metric of messages. From this perspective, a definition of information can be derived as follows. Definition 3. Information in modern informatics is defined as any aspect of the natural world that can be abstracted, digitally represented, and mentally processed [8, 9]. From Definition 3 it can be seen that the implication and extension of information has been shifted from probability of signals to organized data that represents messages, knowledge and/or abstracted real-world entities. With this orientation, information is regarded as an independent and essential entity in modeling the natural world. Definition 4. The content of information in modern informatics is measured by the cost of code to abstractly represent a given size of message M in a digital system k [8], i.e.:

• A book to its author has no information; while if the pages of the book are randomly reshuffled, the author may find new information there. The conventional information theory was used to study models of communication channels and coding/decoding systems. Alternative information theories have been developed in the last decades to extend the usage of classical information theory, such as the non probability-based theory, unknown-probability channel theory, decision theory, and belief theory.

3. CONTEMPORARY INFORMATICS As discussed in Section 2, conventional informatics treats information as a probabilistic measure of the quantity of message that can be received from a message source. It was focused on information transmission rather than information itself.

Ik = f: M → Sk = logk M

(5)

where Ik is the content of information in digital system k, and Sk the measurement scale based on k. Expression 5 is a generic information measurement. When a binary digital representation system is adopted, i.e. k = b = 2, it becomes the most useful one: Ib = f: M → Sb = log2 M [bit]

(6)

where the unit of information, Ib, is a bit. However, the bit here is concrete and deterministic, and no longer possesses a property as a value of weighted probability as that in Expression 2. According to Expression 6, for given messages M1 = 2 bits and M2 = 230 bits, their information contents can be determined respectively as follows:

37 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE

Ib1 = log2 M1 = log2 2 = 1 [bit]

Further description of the above properties of information can be found in [10]. The properties and laws of information are helpful to explain the nature of information science and IT technology, which are tackling a wide range of fundamental problems in the interdisciplinary area between conventional natural sciences and modern informatics-based sciences, particularly, in the area of computing and software engineering.

and Ib2 = log2 M2 = log2 230 = 30 [bit] The results show that messages in sizes M1 and M2 contain, respectively, 1 bit and 30 bits information. Or information Ib1 (1 bit) and Ib1 (30 bits) can represent messages in sizes of M1 and M2. With the perception of Definition 3, it is natural and intuitive to perceive IT as any technology that can be used for the processing of information. In the same way, an information system can be defined as a computer-based system for information processing. Where the processing of information includes: acquisition, elicitation, storage, manipulation (adding, deleting, updating), production, presentation, searching, and retrieving. 3.2 Properties of Information In Section 1, information, matter and energy, have been identified as the three fundamental essences of the world. According to the I-M-E model, as shown in Fig. 1, information plays a vital role in connecting the natural world and the abstract world. A set of 12 properties of information and principles of informatics has been identified in [8, 10] as shown below: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Abstract artifacts Cumulativeness Lossless reusable Dimensionless Weightless Transformability between I-M-E Multiple representation forms Multiple carrying media Multiple transmission forms Generality of sources Conservation of information entropy and thermal entropy (12) Quality attributes of informatics From the above list, it can be seen that informatics is a unique discipline in the human knowledge structure. According to the natural law of conservation, matter can neither be reproduced nor destroyed. However, contrasting to the properties of matter, important attributes of information are that it can be reproduced, destroyed and accumulated. The accumulative capability of information is the most significant attribute of information that human beings rely on for evolution.

4. COGNITIVE INFORMATICS According to the I-M-E model, the information theories discussed in Sections 2 and 3 can be classified as the external informatics. Complementary to it, there is a whole range of new research areas known as cognitive informatics [7-8, 10-12]. Cognitive Informatics is a cutting-edge and interdisciplinary research area that tackles the common root problems of modern informatics, computation, software engineering, AI, and life sciences. This section explores fundamentals of cognitive informatics and its potential impacts on, and applications in, informationbased sciences and engineering disciplines. 4.1 External vs. Internal Informatics Cognitive informatics is a new frontier located in an interdisciplinary area, which encompasses informatics, computer science, software engineering, mathematics, knowledge theory, cognition science, neurobiology, psychology, and physiology. Definition 5. Cognitive informatics (CI) is a multidisciplinary branch of informatics that studies the internal information processing mechanisms and processes of the natural intelligence - human brains and minds. CI attempts to solve problems in two connected areas in a bi-directional and multidisciplinary approach. In one direction, CI uses computing techniques to investigate cognitive science problems, such as memory, learning, and thinking; In the other direction, CI uses cognitive theories to investigate informatics, computing, and software engineering. The relationship between the internal and external informatics can be illustrated in Fig. 2. In CI, the brain is perceived as the last thing in the world yet to be explored. To explore it, special recursive research power of human beings is required. This makes CI unique in distinguishing from other natural sciences. CI focuses on the nature of information in the brain, such as information acquisition, memory, categorization, retrieve, generation, representation, and communication. Via an interdisciplinary approach and with the support of modern information and neural science technologies, mechanisms of the brain and the mind will be systematically explored in CI.

38 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE

interdisciplinary research in subject areas including, inter alia, the following: • • • • • • • • • • • • • •

Fig. 2. Relationship between CI and other informatics

Definition 6. Information in CI is defined as abstract artifacts and their relations that can be modeled, processed, and stored by human brains. It is recognized that at the fundamental level, the brain models information by only four meta types [11]: object, attribute, relation, and action, as shown in Table 1. However, the magnitude of connections between them are so high, which can be at the order of trillions times trillions level in the brain [10, 11]. Based on this observation, an object-attribute-relation (OAR) model of human memory is developed in [11], which presents a generic memory model of the brain. Further investigation into models of the brain and cognitive mechanisms of the mind will be one of the focuses in CI.

Intellectual foundations of informatics Internal information processing mechanisms Memory models of the brain Cognitive models of the mind Expressive mathematics Semantics networks Intellectual roots of computing CI foundations of software engineering Informatics laws of software Knowledge representation Extension of human memories New approaches to computing Applications in informatics Applications in cognitive science

The applications of CI in cognitive science will cover a wide range of cognitive phenomena, as shown in Table 2, at the sensation, subconscious, meta cognitive, and higher cognitive function levels. Table 2. Classification of Cognitive Phenomena Subconscious functions Level 0 Sensation

Table 1. The Meta Cognitive Models of the Brain Vision

Cognitive models Object

Attribute

Relation

Action

Description The abstraction of external entities and internal artifacts Detailed properties and characteristics of an object Connections and relationships between object-object, objectattributes, and attributeattribute A sequence of state transitions of sensors and motor-muscles

Mathematical abstraction Set, tuple

Audition Smell Touch - thermal - pressure - weight - texture Tastes - sweet - bitter - sour - salt - pungency

Set, tuple

Relational algebra, combinational logic

Process algebra, predicate logic, automata

Level 1 Subconscious life functions Maintaining consciousness conditions Memory Desires Feeling Personality

Conscious functions Level 2 Meta cognitive functions

Level 3 Higher cognitive functions

abstraction

recognition

search sort remember knowledge

imagination comprehension reasoning correlation learn think math operation induction deduction determination summarization invention problem solving

The applications of CI in informatics, computer science, and software engineering are potential in the following areas:

4.2 Domain of Cognitive Informatics CI, the internal information theory, studies the following topics: cognitive mechanism of the brain, cognitive processes of the mind, brain memory organization, knowledge representation, perception of external information, memory, learning, thinking, intelligence, autonomy, knowledge networking, and knowledge engineering. CI covers a whole range of

39 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE

• • • • • •

Nature of software Software engineering methodologies Artificial intelligence Knowledge representation Knowledge engineering Bioinformatics

• • • • • • • • •

Table 3. Foundations of CI

Quantum information processing Fuzzy logic Machine learning Neural networks Pattern recognition Agent technologies Generic algorithms Information systems modeling Web-based information systems

Therefore, the interdisciplinary nature of CI will not only benefit conventional informatics and computing research by understanding the natural intelligence and its mechanisms, but also benefit cognitive and life sciences by informatics theories and formal methods.

5. FOUNDATIONS OF COGNITIVE INFORMATICS Based on the exploration of the domain of CI in Section 4, this section investigates the foundations of CI. 5.1 A Multidisciplinary Approach CI is a discipline that forges links between a number of natural science and life science disciplines with informatics and computing science. The relationship between CI and other natural sciences can be perceived as shown in Fig. 3.

Category No. 1 1.1 1.2 1.3 1.4 2 2.1 2.2 2.3 2.4 2.5 3 3.1 3.2

Discipline of sciences

Modern information theory Computing theory Software science Artificial intelligence Natural sciences Cognitive science Neurobiology Psychology Physiology Mathematics Humanity Philosophy Linguistics

Table 5 is useful to identify if a given object can be categorized as a type of information in the abstract world, or type of an entity in the physical world. For example, the nature of software has been argued since the emerging of the first generation computers in the 1940s. Is software a physical entity or a type of information? According to the classification of abstract levels of cognitive information in Table 4, it can be seen that software is a kind of information at abstract levels 3 or 4, rather than a physical entity. Table 4. Abstract Levels of Cognitive Information Level 1 2

Category Analogue objects Natural languages

3

Special notation systems

4

Mathematics

4.1

Formulae

4.2 4.3

Theorems Corollaries

5

Philosophies

Fig. 3. Relationship between CI and natural sciences

Fig. 3 shows that the foundations of CI are based on multidisciplinary knowledge, such as those of informatics, natural sciences, and humanity. These foundations can be classified into three categories as shown in Table 3. In CI the cognitive information and knowledge modeled in the brain can be divided into different abstraction levels, such as analogue objects, natural languages, professional notation systems, mathematics, and philosophies. A classification of the cognitive abstract levels is shown in Table 5.

Category of sciences Informatics

Description Empirical artifacts Empirical methods, heuristic rules Professional languages, formal methods High-level abstraction of objects, attributes, and their relations and rules, particular those that are time and space independent. Mathematical description of relations or rules Proved relations or rules Derived conclusions based on known theorems or rules The highest-order abstraction of generic objects and their relations and rules, particular those that are time and space independent.

5.2 The Brain and the Mind The brain is an information processing organ of human beings and a real-time controller of the body. The mind is an abstract model of oneself and a thinking engine. The relationship between the brain and the mind can be analogized by:

40 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE

Brain : Mind = Computer : Program

(7)

The existence of the virtual model of human beings, the mind, can be proven by the following mental phenomena: (a) If you close your eyes, you may still imagine or ‘see’ everything you learnd and remembered, particularly, the visual information, such as the hands; and (b) It’s reported that patients who lost a leg or arm may think or feel, from time to time, that they still have it as before. The mind, as a virtual model of a person in the brain, is partially programmed and partially wired. The former is evolved for the flexibility of life functions, while the latter is formed for the efficiency of frequently conducted activities, such as eating, writing, and driving.

The relationship between NI-OS and NI-App can be illustrated in Fig. 4. Corresponding to Table 3, the levels 0 and 1 life functions in Fig. 4 belong to NI-OS, and the levels 2 and 3 life functions are a part of NI-App.

NI-App (Conscious)

Level 2 Meta cognitive functions

NI-OS (Subconscious)

5.3 NI-OS and NI-App

The NI-Sys

Fig. 4 Relationship between NI-OS and NI-App

5.4 Generic Relationships between I-M-E (8)

where, NI-OS represents the inherited life functions, NIApp the developed life functions, and || a parallel relation. NI-Sys functions as a set of parallel interactions between NI-OS and NI-App. NI-Sys can response to internal and external information in event-driven, timedriven, and desire-driven approaches. The characteristics of NI-OS have been observed as follows: • • • • • • • •

Inherited Wired (by neural networks) A real-time system Running subconsciously and automatically Person-independent, common and similar Highly parallel and Fault-tolerant Event/time/desire –driven A born visual processor

According to the information-matter-energy (I-M-E) model, as shown in Fig. 1, the three basic essences of the world are predicated to be transformable between each other. Fig. 1 demonstrates that there are six possible relations between the three essences in the natural and information worlds. They can be described by the following generic functions f1 to f6: I = f1 (M) I = f2 (E) M = f3 (I) M = f4 (E) E = f5 (I) E = f6 (M)

• • • •

(9.1) (9.2) (9.3) (9.4) (9.5) (9.6)

Albert Einstein has revealed function f6, the relationship between matter (m) and energy (E), in the following form: E = mC2

In contradiction, the characteristics of NI-App have been identified as follows: • •

Level 1 Subconscious lift functions

Level 0 Sensation

CI studies the brain as a real-time natural intelligent (NI) information processing system, NI-Sys [11], which is configured by a predetermined operating system (thinking engine) NI-OS and a set of acquired life applications NIApp, i.e.: NI-Sys 식 NI-OS || NI-App

Level 3 Higher level cognitive funcs.

Acquired Partially wired (frequently used functions) and partially programmed (temporary functions) A trained notation processor Running consciously Can be trained and programmed Person-specific

It will be interesting to expect what the remaining relationships and forms of transformations between I-M-E are. In this case, cognitive informatics, in a certain extent, is the science to seek possible solutions for f1 to f6. A clue to explore the relations and transformability between I-ME is believed in the understanding of the natural intelligence and its information processing mechanisms in the emerging area of cognitive informatics.

41 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE

6. CONCLUSIONS AND PERSPECTIVES 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. Cognitive informatics has been described as a profound interdisciplinary research area that tackles the common root problems of modern informatics, computation, software engineering, AI, and life sciences. The development of informatics from the classical information theory, contemporary informatics, to cognitive informatics has been reviewed. Based on this, the foundations of CI and its potential applications have been explored. This paper has demonstrated that the brain is the last thing in the world yet to be studied, and CI is a special area that requires recursive research power of human beings. The exploration and revealment of relationships and ways of transformation between information, matter, and energy, the three essences of the world, will be one of the potential breakthrough-making areas in CI. Major problems yet to be solved in CI are such as the architectures of the brain, mechanisms of the natural intelligence, cognitive processes, mental phenomena, and personality. It is particularly interested in computing and software engineering to explain the mechanisms and processes of memory, learning and thinking. It is expected that any breakthrough in CI will profoundly significant towards the development of the next generation technologies in informatics, computing, software, and cognitive sciences.

Acknowledgements This work is sponsored by the Natural Sciences and Engineering Research Council of Canada (NSERC) and BCT-Telus. The author would like to acknowledge the support of the funding organizations. The author would like to thank the valuable comments and suggestions of the reviewers.

References

[3]

Hartley, R.V.L. (1928), Transmission of Information, Bell System technical Journal, July, pp.535.

[4]

Patel, D. and Wang, Y. eds. (2000), Comparative Studies of Engineering Approaches for Software Engineering, Annals of Software Engineering: An International Journal, Baltzer Science Publishers, Oxford, Vol.10, 391pp.

[5]

Shannon, C.E. (1948), A Mathematical Theory of Communication, Bell System Technical Journal, Vol.27, pp.379-423 and 623-656.

[6]

Shannon, C.E. and Weaver, W. (1949), The Mathematical Theory of Communication, Illinois University Press, Urbana, IL, USA.

[7]

Wang, Y. (2001), On a New Frontier: Cognitive Informatics, Invited Talk, Proceedings of the 7th International Conference on Object-Oriented Information Systems (OOIS’01), Calgary, Canada, Springer, August.

[8]

Wang, Y. (2001), Lecture Notes on Theoretical Foundations of Software Engineering, SENG 609.19, Univ. of Calgary, Canada, pp.1-728.

[9]

Wang, Y., S. Patel, and R. Johnston eds. (2001), Proceedings of the 7th International Conference on Object-Oriented Information Systems (OOIS’01), Calgary, Canada, August, Springer London, 552pp.

[10] Wang, Y. (2002), On the Informatics Laws of Software, Keynote Lecture, Proceedings of the 1st IEEE International Conference on Cognitive Informatics (ICCI’02), Calgary, Canada, IEEE CS Press, August. [11] Wang, Y. and Y. Wang (2002), Cognitive Models of the Brain, Proceedings of the 1st IEEE International Conference on Cognitive Informatics (ICCI’02), Calgary, Canada, IEEE CS Press, August. [12] Wang, Y. (2002), A New Mathematics for Describing Notions and Thoughts in Software Design, Proceedings of the 1st IEEE International Conference on Cognitive Informatics (ICCI’02), Calgary, Canada, IEEE CS Press, August.

[1]

Bell, D.A. (1953), Information Theory, Pitman, London.

[13] Wilhelm, R. ed. (2001), Informatics : 10 Years Back, 10 Years Ahead, LNCS 2000, Springer Verlag, Berlin.

[2]

Goldman, S. (1953), Information Theory, PrenticeHall, Englewood Cliffs, NJ, USA.

[14] Zhong, Y. (1996), Principles of Information Science (in Chinese), BUPT Press, Beijing.

42 Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02) 0-7695-1724-2/02 $17.00 © 2002 IEEE