International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011 1
Perspectives on the Field of
Cognitive Informatics and
its Future Development
Yingxu Wang, University of Calgary, Canada
Bernard Widrow, Stanford University, USA
Bo Zhang, Tsinghua University, China
Witold Kinsner, University of Manitoba, Canada
Kenji Sugawara, Chiba Institute of Technology, Japan
Fuchun Sun, Tsinghua University, China
Jianhua Lu, Tsinghua University, China
Thomas Weise, University of Science and Technology of China, China
Du Zhang, California State University, USA
ABSTRACT
The contemporary wonder of sciences and engineering has recently refocused on the beginning point of: how
the brain processes internal and external information autonomously and cognitively rather than imperatively
like conventional computers. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science,
information sciences, cognitive science, and intelligence science that investigates the internal information
processing mechanisms and processes of the brain and natural intelligence, as well as their engineering
applications in cognitive computing. This paper reports a set of eight position statements presented in the
plenary panel of IEEE ICCI’10 on Cognitive Informatics and Its Future Development contributed from invited
panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics
and cognitive computing.
Keywords:
Algebra, Artificial Intelligence, Cognitive Informatics, Computational Intelligence, Denotational
Mathematics, Ebrain, Engineering Applications, Machinable Intelligence, Natural Intelligence
1. INTRODUCTION
Cognitive informatics is a transdisciplinary enquiry of computer science, information science,
cognitive science, and intelligence science, which investigates into the internal information processDOI: 10.4018/jcini.2011010101
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2 International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011
ing mechanisms and processes of the brain and natural intelligence, as well as their engineering
applications in cognitive computing (Wang, 2002a, 2003, 2006, 2007c, 2009c, 2009d; Wang &
Kinsner, 2006; Wang & Wang, 2006; Wang & Chiew, 2010; Wang, Kinsner, & Zhang, 2009;
Wang, Johnston, & Smith, 2002; Wang, Wang, Patel, & Patel, 2006; Wang, Zhang, Latombe,
& Kinsner, 2008, Wang et al., 2009; Baciu et al., 2009; Chan et al., 2004; Kinsner et al., 2005;
Patel et al., 2003; Yao et al., 2006; Zhang et al., 2007; Sun et al., 2010). Cognitive informatics is
a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared
by computational intelligence, modern informatics, computer science, AI, cybernetics, cognitive science, neuropsychology, medical science, philosophy, formal linguistics, and life science
(Wang, 2002a, 2003, 2007c, 2009c, 2010b, 2010d). The development and the cross fertilization
among the aforementioned science and engineering disciplines have led to a whole range of
extremely interesting new research areas known as Cognitive informatics, which investigates the
internal information processing mechanisms and processes of the natural intelligence – human
brains and minds – and their engineering applications in computational intelligence. Cognitive
informatics studies the natural intelligence and internal information processing mechanisms
of the brain, as well as processes involved in perception and cognition. Cognitive informatics
forges links between a number of natural science and life science disciplines with informatics
and computing science.
Definition 1. 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 in cognitive computing.
The IEEE series of International Conferences on Cognitive Informatics (ICCI) has been established since 2002 (Wang, 2002a). The inaugural ICCI event in 2002 was held at University of
Calgary, Canada (ICCI’02) (Wang, Johnston, & Smith, 2002), followed by the events in London,
UK (ICCI’03) (Patel et al., 2003); Victoria, Canada (ICCI’04) (Chan et al., 2004); Irvine, USA
(ICCI’05) (Kinsner et al., 2005); Beijing, China (ICCI’06) (Yao et al., 2006); Lake Tahoe, USA
(ICCI’07) (Zhang et al., 2007); Stanford University, USA (ICCI’08) (Wang, Zhang, Latombe,
& Kinsner, 2008); Hong Kong (ICCI’09) (Baciu et al., 2009); and Tsinghua University, Beijing
(ICCI’10) (Sun et al., 2010). Since its inception, ICCI has been growing steadily in its size, scope,
and depth. It attracts worldwide researchers from academia, government agencies, and industry
practitioners. The conference 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.
The latest advances and engineering applications of CI have led to the emergence of cognitive computing (CC) and the development of cognitive computers that think, perceive, learn,
and reason (Wang, 2006, 2009e, 2010a, 2010b; Wang et al., 2009; Wang, Kinsner, & Zhang,
2009). CI has also fundamentally contributed to autonomous agent systems (Wang, 2009a) and
cognitive robots (Wang, 2010b). A wide range of applications of CI has been identified such as
in the development of cognitive computers, cognitive robots, cognitive agent systems, cognitive
search engines, cognitive learning systems, and artificial brains. The work in CI may also lead to
a fundamental solution to computational linguistics, Computing with Natural Language (CNL),
and Computing with Words (CWW) (Zedeh, 1975, 1999).
This paper is a summary of the position statements of panellists presented in the Plenary
Panel on Cognitive Informatics and Its Future Development in IEEE ICCI 2010 at Tsinghua
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International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011 3
University held in July 2010 (Sun et al., 2010). It is noteworthy that the individual statements
and opinions included in this paper may not necessarily be shared by all panellists.
2. COGNITIVE INFORMATICS (CI): THE SCIENCE OF
ABSTRACT INTELLIGENCE AND COGNITIVE COMPUTING
CI establishes a systematical framework for brain and computational intelligence studies as well
as their applications at hierarchical levels known as the functional level, the mathematical (logical)
level, the cognitive level, and the neural (physiological) level. Corresponding to the four reductive
levels as shown in Figure 1, the theoretical framework of CI (Wang, 2007c) encompasses four
main areas of basic and applied research on: a) Abstract intelligence: fundamental theories of
natural intelligence; b) Denotational mathematics for modeling abstract intelligence; c) Cognitive models of the brain; d) Neural informatics; and e) Cognitive computing. These areas of CI
are elaborated in the following subsections.
Abstract Intelligence (αI): The studies on αI form a human enquiry of both natural and artificial intelligence at the reductive neural, cognitive, logical, and functional levels from the
bottom up (Wang, 2009c). The paradigms of αI are such as natural, artificial, machinable,
and computational intelligence. The studies in CI and αI lay a theoretical foundation toward
revealing the basic mechanisms of different forms of intelligence (Wang, 2010a). As a
result, cognitive computers may be developed, which are characterized as knowledge
processors beyond those of data processors in conventional computing.
Cognitive Models of the Brain: Fundamental theories developed in CI covers the InformationMatter-Energy (IME) model (Wang, 2007a), the Layered Reference Model of the Brain
(LRMB) (Wang, Wang, Patel, & Patel, 2006), the Object-Attribute-Relation (OAR) model
of information/knowledge representation in the brain (Wang, 2007c), the cognitive inforFigure 1. The architecture and theoretical framework of Cognitive Informatics (CI)
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4 International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011
matics model of the brain (Wang, 2010b; Wang & Wang, 2006), and Natural Intelligence
(NI) (Wang, 2007c). 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, metacognitive, meta-inference, and higher cognitive layers (Wang, Wang, Patel, & Patel, 2006).
Neural Informatics (NeI): Neural informatics is an emerging interdisciplinary enquiry of the
biological and physiological representation of information and knowledge in the brain at
the neuron level and their abstract mathematical models (Wang, 2007c). NeI is an important branch of cognitive informatics, which reduces cognitive informatics theories and the
studies on the internal information processing mechanisms of the brain onto the neuron
and physiological level. In neural informatics, memory is recognized as the foundation and
platform of any natural or machine intelligence based on the OAR model (Wang, 2007b;
Wang & Wang, 2006) of information/knowledge representation. The theories of neural
informatics explain a number of important questions in the study of natural intelligence.
Enlightening findings in neural informatics are such as (a) Long-Tem Memory (LTM)
establishment is a subconscious process; (b) The long-term memory is established during
sleeping; (c) The major mechanism for LTM establishment is by sleeping; (d) The general
acquisition cycle of LTM is equal to or longer than 24 hours; (e) The mechanism of LTM
establishment is to update the entire memory of information represented as an OAR model
in the brain (Wang, 2007b); and (f) Eye movement and dreams play an important role in
LTM creation.
Denotational Mathematics (DM):Denotational Mathematics (DM) is a category of expressive
mathematical structures that deals with high-level mathematical entities beyond numbers and
sets, such as abstract objects, complex relations, perceptual information, abstract concepts,
knowledge, intelligent behaviors, behavioral processes, and systems (Wang, 2008a). A
number of DMs have been created and developed such as concept algebra (Wang, 2008b,
2010c), system algebra (Wang, 2008c; Wang, Zadeh, & Yao, 2009), real-time process
algebra (RTPA) (Wang, 2002b, 2008d), granular algebra (Wang, 2010c), visual semantic algebra (VSA) (Wang, 2009e), and formal causal inference methodologies (Wang, in
press). It is recognized that the maturity of a scientific discipline is characterized by the
maturity of its mathematical (meta-methodological) means. DM provides a coherent set
of contemporary mathematical means and explicit expressive power for CI, αI, NeI, CC,
AI, and computational intelligence.
Applications of CI, αI, NeI, and DM: The key applications of the fundamental theories and
technologies of CI can be divided into two categories. The first category of applications
uses informatics and computing techniques to investigate intelligence science, cognitive
science, and knowledge science problems, such as abstract intelligence, memory, learning, and reasoning. The second category includes the areas that use cognitive informatics
theories to investigate problems in informatics, computing, software engineering, knowledge engineering, and computational intelligence. CI focuses on the nature of information
processing in the brain, such as information acquisition, representation, memory, retrieval,
creation, 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 (Wang & Chiew, 2010) within the framework of CI.
A key and exiting application of CI is its inspiration to and theoretical preparation for cognitive computers (cCs), which is an intelligent computer for knowledge processing as that of a
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International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011 5
conventional von Neumann computer for data processing. A cC is driven by a cognitive CPU
with a cognitive learning engine and formal inference engine for intelligent operations on abstract
concepts as the basic unit of human knowledge (Wang, 2009d, 2010c, 2010d). cCs are designed
based on contemporary denotational mathematics (Wang, 2008a, 2009f), particularly concept
algebra, as that of Boolean algebra for the conventional von Neumann architecture computers.
3. CI/CC AND COGNITIVE MEMORY
Cognitive computing is brain-like computing. Emulating mental processing is inherently difficult,
since the overall working of the human brain is more or less unknown. Instead of trying to model
the functioning of the entire brain, it is proposed that a portion be studied, an important portion,
long-term memory. Without memory we can live, but we would not really exist as human beings.
Human memory is exceedingly complex. However, by observing our own behavior and
that of fellow humans, electrical engineers can devise memory systems that exhibit human-like
behavior. In my laboratory at Stanford University (Widrow & Aragon, 2010), we have constructed
an elementary form of cognitive memory consisting of both software and hardware. We have
used this memory to solve problems in the fields of pattern recognition, face recognition and
control systems.
Taking inspiration from life experience, we have devised a new form of computer memory
(Widrow & Aragon, 2010). Certain conjectures about human memory are keys to the central idea.
We present the design of a practical and useful “cognitive” memory system. The new memory
does not function like a computer memory where specific data is stored in specific numbered
registers and retrieval is done by reading the contents of the specified memory register, or done
by matching key words as with a document search. Incoming sensory data would be stored at
the next available empty memory location, and indeed could be stored redundantly at several
empty locations. The stored sensory data would neither have key words nor would it be located
in known or specified memory locations. Retrieval would be initiated by a prompt signal from
a current set of sensory inputs or patterns. The search would be done by a retrieval system that
makes use of auto-associative artificial neural networks. A practical application of this cognitive
memory system to human facial recognition has been implemented.
4. THE MULTIDISCIPLINARY RESEARCH
FOR COGNITIVE COMPUTATION
Cognitive informatics should benefit from the multidisciplinary research among information
science, cognitive science and brain science, etc. (Zhang, 2010). Taking computer vision as
an example, human visual performances are much better than computer vision in many cases
recently. Therefore, computer vision should learn something from the visual mechanism of human cognition in the brain.
As we known, the main approaches adopted in recent computer object recognition are
statistics-based and data-driven. The objects (images) are represented in the data space composed
by the computer’s robustly detectable features. These features are generally less meaningful, i.e.,
so called low-level features, such as colors, textures, etc. Due to the big gap between semantics
and low level features, image recognition, classification, and etc. are difficult to implement in
computer vision. But human brain processes visual information in the conceptual space where
the semantically meaningful features are extracted such as line-segments, boundaries, shapes,
etc. Therefore, there is no semantic gap in human visual information processing. In order to
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6 International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011
endow computers with the human capacity, we need to learn from the human visual information
processing mechanisms (Zhang, 2010).
In order to promote the multidisciplinary research, the Center for Neural and Cognitive
Computation was established at Tsinghua University in 2009. It includes the fields of computational neuroscience, system neuroscience, information science, psychology, neural information,
brain-computer interface, learning, and memory, etc.
5. EVOLUTION OF COGNITIVE DYNAMICAL
SYSTEMS AND COGNITIVE INFORMATICS
Many developments of the last century focused on adaptation and adaptive systems. The focus
in this century appears to be shifting towards cognition and cognitive dynamical systems with
emergence. Although cognitive dynamical systems are always adaptive to various conditions
in the environment where they operate, adaptive systems of the past have not been cognitive.
The evolving formulation of cognitive informatics (CI) (Wang, 2002a, 2003, 2007c; Wang
& Kinsner, 2006; Wang, Kinsner, & Zhang, 2009; Wang, Zhang, & Tsumoto, 2009) has been
an important step in bringing the diverse areas of science, engineering, and technology required
to develop such cognitive systems. Current examples of various cognitive systems include
autonomic computing, memetic computing, cognitive radio, cognitive radar, cognitive robots,
cognitive networks, cognitive computers, cognitive cars, cognitive factories, as well as brainmachine interfaces for physically-impaired persons, and cognitive binaural hearing instruments.
The phenomenal interest in this area may be due to the recognition that perfect solutions to
large-scale scientific and engineering problems may not be feasible, and we should seek the
best solution for the task at hand. The “best” means suboptimal and the most reliable (robust)
solution, given not only limited resources (financial and environmental) but also incomplete
knowledge of the problem and partial observability of the environment. Many exciting new
theoretical, computational and technological accomplishments have been described at IEEE
ICCI conferences and related journals.
The challenges in the evolving cognitive systems can be grouped into several categories: (a)
theoretical, (b) technological, and (c) sociological. The first group of theoretical issues includes
modeling, reformulation of information and entropy, multiscale measures and metrics, and
management of uncertainty. Modeling of cognitive systems requires radically new approaches.
Reductionism has dominated our scientific worldview for the last 350 years, since the times
of Descartes, Galileo, Newton, and Laplace. In that approach, all reality can be understood in
terms of particles (or strings) in motion. However, in this unfolding emergent universe with
agency, meaning, values and purpose, we cannot prestate or predict all that will happen. Since
cognitive systems rely on perceiving the world by agents, learning from it, remembering and
developing the experience of self-awareness, feelings, intentions, and deciding how to control
not only tasks but also communication with other agents, and to create new ideas, CI cannot
only rely on the reductionist approach of describing nature. In fact, CI tries to expand the modeling in order to deal with the emergent universe where no laws of physics are violated, and
yet ceaseless unforeseeable creativity arises and surrounds us all the time. This new approach
requires many new ideas to be developed, including reformulation of the concept of cognitive
information, entropy, and associated measures, as well as management of uncertainty, and new
forms of cognitive computing.
As we have seen over the last decade, cognitive informatics is multidisciplinary (Wang,
2003, 2007c; Wang & Kinsner, 2006; Wang, Zhang, & Tsumoto, 2009), and requires cooperation
between many subjects, including sciences (e.g., cognitive science, evolutionary computing,
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International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011 7
granular computing, computer science, game theory, crisp and fuzzy sets, mathematics, physics, chemistry, biology, psychology, humanities, and social sciences), as well as engineering
and technology (computer, electrical, mechanical, information theory, control theory, intelligent
signal processing, neural networks, learning machines, sensor networks, wireless communications, and computer networks). Many of the new algorithms replace the conventional concepts
of second order statistics (covariance, L2 distances, and correlation functions) with scalars and
functions based on information theoretic underpinnings (such as entropy, mutual information and
correntropy) defined not only on a single scale, but also on multiple scales. Two recent special
issues of the IEEE Proceedings are dedicated to cognitive systems with their practical perspectives (April 2009), and fundamental issues (May 2009).
6. COGNITIVE COMPUTING AND SYMBIOTIC COMPUTING
Cognitive Computing (CC) is an emerging paradigm of intelligent computing methodologies
and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perception mimicking of the brain (Wang, 2009d; Wang et al., 2009).
The Layered Reference Model of the Brain (LRMB) was proposed as a seven-layered model
of the function layers of the brain for the fundamental cognitive mechanisms and processes of
natural intelligence (Wang, Wang, Patel, & Patel, 2006).
Symbiotic Computing (SC) is a methodology based on cognitive computing, ubiquitous
computing and agent-based computing to develop systems that have their significance of existence
for serving humans, their goals of actions for obtaining the trust of them through their services,
and their desire for growing up their capabilities to serve them, along with them through practice collaborating with them. The framework of SC is based on an agent framework that works
on the Symbiotic-computing-based System Platform (SSP), which consists of partner tracking
function, symbiotic zone control function, symbiotic zone sensing function and perception function corresponding to the low layers of the LRMB. The intelligent agents which work on the
platform have functions of cognition corresponding to the upper layers of the LRMB and social
knowledge to cooperate with humans and with other intelligent agents working on the platform
and providing social norm, customs, conveniences and risks for humans.
The Symbiotic Zone (Symbiozone or SYZ) is a conceptual space surrounding a person with
ubiquitous and wearable sensors and effectors in order for an intelligent agent called a Partner
Agent (PA) to communicate with only person called a partner of the PA. The PA follows the
partner by tracking him/her, finding sensors and effectors around him/her and making a network
of the devices dynamically wherever possible. The partner can communicate with the PA when
she/he wants and the PA gives advices if it considers that it should do it then for the partner.
The PA is an intelligent and complex software system based on a multi-agent system architecture, in which the following disciplines of behavior are implemented:
(1) Significance of existence to serve its partner
(2) Goals of action to serve its partner
(3) Desire of co-growing with its partner
In SYZ, a partner and a PA maintain close contact like the Licklider’s view of Man-Computer
symbiosis (Licklider, 1960) and promote mutual understanding to achieve the categories of (1) (3). To do so in the SYZ, a PA has the following functions inside, (a) perceiving poses and actions
of the partner, (b) perceiving surroundings of the partner, (c) acquiring partner’s requirements,
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8 International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011
(d) understanding partner’s intension, (e) serving the partner based on the intension and requirements, (f) advising the partner against risky actions based on assessment function of actions of
the partner from social point of view.
In order to assess actions of both a partner and its PA from the social point of view, a Group
Agent (GA) cooperates with PAs for a group or a community, which supports group dynamics
(Grundin, 2002) using the Social Informatics (Wang, Carley, Zeng, & Mao, 2007). The social
informatics stored in a GA is acquired by mining information from Web and is learned through
actions that a PA and a partner have made in the group. This SC project aims at developing cognitive systems which work in an area ranging from network systems to physical places where
ubiquitous devices are embedded, working like the cognitive machine (Kinsner, 2002).
7. ROBOTICS AND COGNITIVE INFORMATION PROCESSING
As an ideal platform for research on cognitive information processing, robots have been paid
strong attention for many years and many successful progresses have been achieved (Sun et al.,
2010; Wang, 2010b). In the National Laboratory of Information Science and Technology (TNLIST) at Tsinghua University, the only state laboratory in the field of information sciences at
the national level in China, various types of robots have been developed, such as mobile robots
(also named as intelligent vehicles), flexible-link manipulators, space robots, unmanned aerial
vehicles, soccer robots, and so on. Based on these well-equipped platforms, researchers in this
laboratory have developed comprehensive theory and approaches for robot sensing and control.
Many novel approaches are deeply rooted at cognitive informatics, such as fuzzy control, neural
network control, path planning using genetic algorithms and estimation distributed algorithm,
visual serving using cognitive approach, object tracking based on machine learning.
Recently, a more collaborative project is being conducted in TNLIST, which is supported
by the National Science Foundation of China. This project aims at controlling a mobile robot
using brain-computer-interface (BCI). The BCI signal processing module is provided by the
Medical School of Tsinghua University. Researchers in TNLIST will develop more rich local
environmental information to aid human for teleoperation of the remote mobile robots. This
project integrates many research fields including robots, computer vision, and brain sciences.
We hope the advanced studies reported above provide more insights of TNLIST towards the
future development of cognitive informatics and its engineering applications.
8. CI/CC AND EVOLUTIONARY COMPUTATION
Evolutionary Computation (EC) (Weise, 2009) comprises all Monte Carlo metaheuristics which
iteratively refine sets (populations) of multiple candidate solutions. Most EC approaches are
either Swarm Intelligence (SI) methods or Evolutionary Algorithms (EAs). SI is inspired by fact
that natural systems of many independent, simple agents (such as ants or birds) are of tenable to
find pieces of food or shortest distance routes very efficiently. EAs, on the other hand, copy the
behavior of natural evolution and treat candidate solutions as individuals which compete and
reproduce in a virtual environment defined by the user-provided objective function(s). Generation
after generation, these individuals adapt to the environment and thus, tend to become suitable
solutions for the problem at hand.
Past: The roots of EC go back to the mid-1950s, where the biologist Barricelli (1954) began to
apply computer-aided simulations in order to gain more insight into the natural evolution.
Bremermann (1962) and Bledsoe (1961) were the first ones to use evolutionary approaches
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International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011 9
for solving optimization problems. In the early 1980s Genetic Programming emerged as
the youngest member of the EA family (1980). The most common SI methods followed
in the 1990s (Dorigo et al., 1996; Eberhart & Kennedy, 1995).
Present: Evolutionary Computation now exists for almost 50 years. When taking a look on the
current situation of this area, I get the impression that (1) countless algorithm variants and
analyses have been published and EC became widely accepted in the research community.
(2) Most of the evidence of the efficiency of EAs is based on experiments and empirical
studies. Due to the many configuration parameters of EAs and the wide range of existing
optimization problems, it is very hard to define meaningful boundaries for performance
or required runtime. (3) A tendency towards hybridizing optimization techniques can be
observed, resulting from this lack of knowledge about which algorithm is “good” for which
problem. This trend began in the 1970s (Bosworth, 1972), lead to the development of
Memetic Algorithms (Moscato, 1989), and now culminates in the emergence of portfolio
methods (Peng et al., 2010), which choose the best methods from an algorithm portfolio
during the actual process of solving a given problem. (4) Despite the available evidence for
the high utility of EAs, practitioners who solve real-world optimization problems appear
to often prefer traditional, exact methods. Large-scale problems, which these approaches
cannot handle any more due to their computational complexity, are often approached manually instead of using meta-heuristics which could have provided much better solutions in
shorter time (Weise et al., 2009). (5) The communication between researchers working on
meta-heuristic optimization and those working on traditional, exact methods is low; both
communities appear to be separated.
Future: My humble opinion about the future development in the EC area is that (1) in the next
ten to twenty years, metaheuristic optimization should undergo a slow transition from a
research area to a service. Virtually every decision or design task in engineering and business is an optimization problem. Yet currently, only the fewest of them are recognized as
such and even fewer are actually solved using a suitable technique. More joint projects
between research and economy targeting real-world applications are necessary to improve
the awareness and trust of practitioners in EC. (2) EA research should thus focus on tasks
which are interesting for practitioners, such as large-scale real-world problems (Weise et
al., 2009), in order to become more attractive for them. (3) Up to date, in my opinion, no
framework exists for analyzing EAs theoretically which provides results that are actually
useful in practice. The development of a robust and simple analysis approach would be
highly desirable since it would further increase the acceptance of EC. (4) A closer cooperation
between the EC community and traditional/emerging areas, such as cognitive informatics
(Wang, 2002a, 2007c) and cognitive computing (Wang, 2009d), should be pursued, since
an exchange of ideas would be beneficial for both sides.
9. INCONSISTENCY, MEMORY, AND
INCONSISTENCY-INDUCED LEARNING
The focus of this section of the position paper is on the interplay among inconsistency, long-term
memory, and inconsistency-induced learning in a cognitive system. There are five conclusions
that are summarized as follows.
Two important types of memories exist in a human memory model: short-term memory
(STM) and long-term memory (LTM) (Wang, 2007b; Wang & Wang, 2006; Wang, Kinsner, &
Zhang, 2009). STM is a working memory where reasoning takes place with activated beliefs.
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10 International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011
The reasoning in STM results in actions taken that will affect a human being’s behavior. STM
has limited storage capacity and duration. Activated beliefs in STM come from either sensory
memory or LTM.
LTM, on the other hand, is where beliefs are retained for long-term purpose. In general, LTM
does not have practical capacity limit and some information can be stored in LTM indefinitely
(Cherniak, 1983). Beliefs in LTM do not have direct impact on a person’s behavior unless they are
recalled or retrieved to STM. In traditional models, beliefs in LTM are referred to as “relatively
inert” (Cherniak, 1983). Some recent constructive memory models regard beliefs in LTM not as
completely inactive once retained, but as undergoing some sort of transformation after acquisition and before recall. According to (McGuire, 1960), beliefs in LTM are organized into various
“compartments” in human thinking apparatus. Cognitions and beliefs that are not recalled or
activated contemporaneously tend to be separated into different compartments (Cherniak, 1983).
LTM invariably contains inconsistent beliefs. There are several reasons for this. The vast
number of beliefs a person possesses makes it a daunting and next to impossible task to keep track
of inconsistent beliefs. The compartmentalized structure in LTM is such that logical relations
and inconsistencies between beliefs in different compartments are far less likely to be identified
than those of beliefs belonging to the same compartment. The following observation in (Shastri
& Grannes, 1996) succinctly summarizes the phenomenon: “we often hold inconsistent beliefs
in our long-term memory without being explicitly aware of such inconsistencies. But at the
same time, we often recognize contradictions in our beliefs when we try to bring inconsistent
knowledge to bear on a particular task.”
The second sentence of the aforementioned quote from (Shastri & Grannes, 1996) describes
the fact that inconsistent beliefs in LTM manifest their influence on a person’s behavior through
STM, because items from LTM cannot directly affect behavior, they have to be recalled or
retrieved to STM to influence one’s behavior. In general, inconsistency detection is intractable
(Johnson-Laird et al., 2004). Detecting inconsistencies in STM hinges on several conditions: the
presence of a triggering event from sensory memory that causes additional beliefs to be recalled
from LTM; efficient recall, and organization of compartmentalized beliefs in LTM. Failure of
detecting inconsistency at STM may have detrimental or costly consequence.
To facilitate inconsistency detection process in a cognitive system, we propose to augment
the constructive memory model by tagging potential inconsistent beliefs in LTM. The tagging
approach relies on (1) obtaining a fixpoint representation for each compartment of beliefs (Zhang,
2005, 2007, 2008); (2) fusing fixpoints for individual compartments into one that allows us to
establish relevant inter-compartment logical relations and inconsistencies between or among
beliefs of different compartments; (3) tagging a set of beliefs that are conflicting with each other
according to the nature of the inconsistency involved; and (3) organizing tags with regard to
different types of inconsistency (Zhang, 2009). We call such a transformed LTM a tagged LTM,
denoted as tLTM.
Assume that two beliefs Pi and Pk in tLTM belong to either the same compartment or two
different compartments and that Pi and Pk are conflicting with each other. When recalling beliefs
from tLTM into STM for a particular task at hand, even though one of Pi and Pk, but not both
contemporaneously, is in STM, the tag, say, Pi has would alert the detection mechanism at STM
to ascertain if the reasoning and subsequent actions should proceed with tagged Pi in STM. This
would help improve the inconsistency detection and subsequent handling process. Of course the
upfront price we pay is the transformation of LTM into tLTM.
Finally, identified inconsistency at STM can serve as an impetus to belief revisions at LTM.
As quoted in (Gotesky, 1968), Henri Poincare once said that contradiction is the prime stimulus
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International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011 11
for scientific research. So to conclude, we would like to end with the follow slogan: inconsistency
in LTM is a terrible thing to waste.
10. CONCLUSION
Cognitive Informatics (CI) has been described as a transdisciplinary enquiry of computer science,
information sciences, 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 in cognitive computing. This paper summarizes the presentations
of a set of eight position papers in the IEEE ICCI’10 Panel on Cognitive Informatics and Its
Future Development contributed from invited panelists who are part of the world’s preeminent
researchers and scholars in the field of cognitive informatics and cognitive computing.
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Yingxu Wang is professor of cognitive informatics and software engineering, President of International Institute of Cognitive Informatics and Cognitive Computing (IICICC), and Director of
Theoretical and Empirical Software Engineering Research Center (TESERC) at the University of
Calgary. He is a Fellow of WIF, a P.Eng of Canada, a Senior Member of IEEE and ACM, and a
member of ISO/IEC JTC1 and the Canadian Advisory Committee (CAC) for ISO. He received a
PhD in Software Engineering from The Nottingham Trent University, UK, in 1997, and a BSc in
Electrical Engineering from Shanghai Tiedao University in 1983. He has industrial experience
since 1972 and has been a full professor since 1994. He was a visiting professor in the Computing Laboratory at Oxford University in 1995, Dept. of Computer Science at Stanford University
in 2008, and the Berkeley Initiative in Soft Computing (BISC) Lab at University of California,
Berkeley in 2008, respectively. He is the founder and steering committee chair of the annual
IEEE International Conference on Cognitive Informatics (ICCI). He is founding Editor-in-Chief
of International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), founding
Editor-in-Chief of International Journal of Software Science and Computational Intelligence
(IJSSCI), Associate Editor of IEEE Transactions on System, Man, and Cybernetics (Part A), Associate Editor of ASP Journal on Advanced Mathematics and Applications, and Editor-in-Chief
of CRC Book Series in Software Engineering. He is the initiator of a number of cutting-edge
research fields such as cognitive informatics, denotational mathematics (such as concept algebra,
system algebra, real-time process algebra (RTPA), granular algebra, visual semantic algebra,
and inference algebra), abstract intelligence, theoretical software engineering, and built-in
tests. He has published over 110 peer reviewed journal papers, 200+ peer reviewed full conference papers, and 12 books in cognitive informatics, software engineering, and computational
intelligence. He is the recipient of dozens of leadership, research achievement, best paper, and
teaching awards in the last 35 years.
Bernard Widrow received the S.B., S.M., and Sc.D. degrees in Electrical Engineering from the
Massachusetts Institute of Technology in 1951, 1953, and 1956, respectively. He joined the MIT
faculty and taught there from 1956 to 1959. In 1959, he joined the faculty of Stanford University,
where he is currently Professor of Electrical Engineering. Dr. Widrow began research on adaptive filters, learning processes, and artificial neural models in 1957. Together with M.E. Hoff,
Jr., his first doctoral student at Stanford, he invented the LMS algorithm in the autumn of 1959.
Today, this is the most widely used learning algorithm, used in every MODEM in the world. He
has continued working on adaptive signal processing, adaptive controls, and neural networks
since that time. Dr. Widrow is a Life Fellow of the IEEE and a Fellow of AAAS. He received the
IEEE Centennial Medal in 1984, the IEEE Alexander Graham Bell Medal in 1986, the IEEE
Signal Processing Society Medal in 1986, the IEEE Neural Networks Pioneer Medal in 1991,
the IEEE Millennium Medal in 2000, and the Benjamin Franklin Medal for Engineering from
the Franklin Institute of Philadelphia in 2001. He was inducted into the National Academy of
Engineering in 1995 and into the Silicon Valley Engineering Council Hall of Fame in 1999.
Dr. Widrow is a past president and member of the Governing Board of the International Neural Network Society. He is associate editor of several journals and is the author of over 125
technical papers and 21 patents. He is co-author of Adaptive Signal Processing and Adaptive
Inverse Control, both Prentice-Hall books. A new book, Quantization Noise, was published by
Cambridge University Press in June 2008.
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16 International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011
Bo Zhang is professor in the Department of Computer Science and Technology, Tsinghua
University and a Fellow of Chinese Academy of Sciences, Beijing, China. He graduated from
the Tsinghua University, Beijing, China, in 1958. His main research interests include artificial
intelligence, neural networks and pattern recognition. He has published about 150 papers and
three books in these fields.
Witold Kinsner is Professor and Associate Head at the Department of Electrical and Computer
Engineering, University of Manitoba, Winnipeg, Canada. He is also Affiliate Professor at the
Institute of Industrial Mathematical Sciences, Winnipeg, and Adjunct Scientist at the Telecommunications Research Laboratories (TRLabs), Winnipeg. He obtained his PhD in electrical
and computer engineering from McMaster University, Hamilton in 1974. He has authored and
co-authored over 630 publications in his research areas. Dr. Kinsner is a life member of the Institute of Electrical & Electronics Engineers (IEEE), a member of the Association of Computing
Machinery (ACM), a member the Mathematical and Computer Modelling Society, a member of
Sigma Xi, and a life member of the Radio Amateur of Canada (RAC).
Kenji Sugawara is a professor of the Department of Information and Network Science, and the
dean of Faculty of Information and Computer Science, Chiba Institute of Technology, Chiba,
Japan. He received a doctoral degree in engineering (1983) from Tohoku University, Japan. His
research interests include Multi-agent System, Artificial Intelligence, Ubiquitous Computing and
Symbiotic Computing. He is a fellow of IEICE Japan and a member of IEEE, ACM and IPSJ.
Fuchun Sun is a professor of computer science with the Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua
University, Beijing, China. He was the Program Committee Co-Chair of the 9th IEEE International Conference on Cognitive Informatics (ICCI 2010).
Jianhua Lu is a professor of electronic engineering and aerospace information systems. He
is the Associate Dean in the School of Aerospace at Tsinghua University, Beijing, China. He
was the Program Committee Co-Chair of the 9th IEEE International Conference on Cognitive
Informatics (ICCI 2010).
Thomas Weise received the Diplom Informatiker (equivalent to M.Sc.) degree from the Department of Computer Science, Chemnitz University of Technology, Chemnitz, Germany, in 2005,
and the Ph.D. degree at the Distributed Systems Group of the Fachbereich Elektrotechnik and Informatik, University of Kassel, Kassel, Germany in 2009. Since 2009, he has been researcher
at the Nature Inspired Computation and Applications Laboratory, School of Computer Science
and Technology, University of Science and Technology of China, Hefei, Anhui, China where he
became Associate Professor in 2011. His major research interests include Evolutionary Computation, Genetic Programming (GP), and real-world applications of optimization algorithms. His
experience ranges from applying GP to distributed systems and multi-agent systems, efficient
web service composition for Service Oriented Architectures, to solving large-scale real-world
vehicle routing problems for multimodal logistics and transportation. Besides being the author/
co-author of over 50 refereed publications, Dr. Weise also authors the electronic book "Global
Optimization Algorithms -- Theory and Application" which is freely available at his website
http://www.it-weise.de/.
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International Journal of Cognitive Informatics and Natural Intelligence, 5(1), 1-17, January-March 2011 17
Du Zhang received his Ph.D. degree in Computer Science from the University of Illinois. He is a
Professor of the Computer Science Department at California State University, Sacramento. His
current research interests include: knowledge base inconsistency, machine learning in software
engineering, and knowledge-based systems and multi-agent systems. He has authored or coauthored over 150 publications in journals, conference proceedings, and book chapters, in these
and other areas. In addition, he has edited or co-edited eleven books and conference proceedings. He has served as the conference general chair, the program committee chair, a program
committee co-chair, or a program vice chair/area chair for 22 international conferences, most
of which are IEEE sponsored international conferences. Currently, he is an Associate Editor for
International Journal on Artificial Intelligence Tools, a member of editorial board for International
Journal of Cognitive Informatics and Natural Intelligence, and a member of editorial board for
International Journal of Software Science and Computational Intelligence. In addition, he has
served as a guest editor for special issues of International Journal of Software Engineering and
Knowledge Engineering, Software Quality Journal, IEEE Transactions on SMC-Part B, EATCS
Fundamenta Informaticae, and International Journal of Computer Applications in Technology.
Du Zhang is a senior member of IEEE and a senior member of ACM.
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