Download Advanced Intelligence: Definition, Approach, and Progresses

International Journal of Advanced Intelligence
Volume 2, Number 1, pp.15-23, July, 2010.
c AIA International Advanced Information Institute
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Advanced Intelligence: Definition, Approach, and Progresses∗
Yixin Zhong
Beijing University of Posts and Telecommunications,
Beijing, 100876 China
yxzhong@ieee.org
Received (Januaray 2010)
Revised (May 2010)
It is believed that the research of Artificial Intelligence, AI for short, has been facing
a turning point in its history of development. A new program and the related paradigm
named “Advanced Intelligence” has been clearly proposed in 2006 and widely spread
soon after. The purpose of the paper is, therefore, to make a necessary clarification on
its background, definition, approach, and initial progress and make an explanation on the
significance of the program of Advanced Intelligence so that better advancement can be
expected in the years to come.
Keywords: Artificial intelligence; Advanced intelligence; Mechanism approach; Information conversion.
1. Introduction
Intelligence has been regarded as one of the most mysterious, yet most important,
subjects in all areas of science and Artificial Intelligence as one of the most difficult, yet most significant, disciplines in technology. As consequence, the research
on Natural Intelligence and Artificial Intelligence has been received more and more
concerns from academic and engineering circles during past decades. This paper
will make emphasis on Artificial Intelligence.
Due to its high complexity, there have been many different understandings, and
therefore different approaches, to the study of Artificial Intelligence in the world.
The earliest approach, starting from 1943, is to simulate the structure of cortex
in human brain and thus gain the name as Structuralism Approach. The technological representative for this approach is the artificial neural networks and the
well-known achievements include pattern recognition, associative memory, combinatorial optimization solution, and fault diagnosing, and so on 1,2,3,4 .
The second approach, beginning from 1956, is to simulate the functions of logical
thinking in human brain and hence earn the name as Functionalism Approach. The
technological representative for this approach is the physical symbolism systems in
∗ This
paper is supported in part by National Science Foundation of China with Project No:
60873001.
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Y. X. Zhong
which expert systems are typical. The outstanding progresses made by this approach
include the general problem solving, theorem proving, game playing, and many kinds
of specific expert systems 5,6,7,8 .
The third approach, incepting in 1990, is to simulate the behavior of intelligent
beings and therefore receive the name as Behaviorism Approach. Technological representative for this approach is the sensor-motor systems and the good achievement
is the walking robot 9,10 .
It should be good for the discipline to have more groups of researchers to study
from different angles of view and with different approaches as the hard nut may
need more crackers.
However, there has been lack of coordination among them. Even worse, mutual
attacks and argues among them happened many times. The Functionalism group
names itself as “Artificial Intelligence” and does not accept the other two so that
the Structuralism group has to use the name of “Computational Intelligence” and
the third group has no similar name till the present. Some researchers have made
efforts for improving the situation during the recent years 11,12 , but there is no
essential improvement.
It is apparent that the unharmonious efforts in artificial intelligence study have
prevented more progresses to achieve and a new program and paradigm named
“Advanced Intelligence” was thus proposed 13 and accepted in 2006 International
Conference on Artificial Intelligence, ICAI’2006, which was organized by both Chinese Association for Artificial Intelligence, CAAI, and European Coordination for
Artificial Intelligence, ECAI, for the cerebration of the 50th anniversary of AI. For
the purpose of better promoting the study and exchange on Advanced Intelligence,
the organizers of the ICAI’2006 asked CAAI to organize a serial conference on
Advanced Intelligence starting from 2008 with the frequency of once two years.
What is Advanced Intelligence? What is the relationship between Advanced
Intelligence and Artificial Intelligence? Why do we need Advanced Intelligence today
in addition to Artificial Intelligence? How should we deal with Advanced Intelligence
in the future? As one of the major contributors to the proposal, this paper would
like to provide necessary explanations to the questions.
2. Background and Definition
As have been clearly pointed out in the proposal at ICAI’2006 mentioned above,
there have been some problems facing the Al research which have, to an extent,
prevented more progresses to be made in the field. It would be even more serious if
no changes being taken
The first problem AI research faced is its less success in methodology.
During the past few decades, within the internal family of AI research, each of the
three schools, i.e., Structuralism, Functionalism, and Behaviorism, firmly insists on
its respective point of views and does not want to, or is unable to, be integrated
into one unified theory harmoniously. The fact of no unified theory itself strongly
Advanced Intelligence: Definition, Approach, and Progresses
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indicates that AI research is at least not yet matured enough in methodology till
the present stage. It is obvious that new methodology in AI research should happen.
Otherwise, the unified theory of AI research would never be possible according to
the current situation in AI study.
The second problem AI research faced is its limitation in the sources
of research. Also during the past decades, externally speaking, AI research has
almost very little to do with the research in Natural Intelligence (NI for short)
research, such as Brain Science and Cognitive Science. Whether clearly separating
from or closely interacting with the NI research will made big difference for AI
research as the major line of thoughts in AI is to simulate something, which has
been well understood in NI. In other words, NI is the sources for AI research.
Without the sources from NI, AI research cannot go far. Therefore, the practical
cooperation between AI research and NI research should be greatly strengthened.
The third problem AI research faced is its ignoring of some fundamental issues. Again, during the past decades, the research in AI has ignored some
fundamentally important issues such as emotion, consciousness, cognition, and the
interrelationships among the consciousness, emotion, cognition and intelligence. As
a matter of fact, consciousness is so fundamental that emotion, cognition and intelligence would be empty for any human beings if he, or she, is no consciousness.
Therefore, it is unavoidable for AI researchers to consider these issues together with
intelligence.
There may be many more problems beside what were mentioned above. What
we want to point out, nevertheless, is that the three problems facing AI research
should be drastically changed in the future. Otherwise, it would be too difficult
to make more progresses significantly in the years to come. These are the basic
background for our proposing of the new program of “Advanced Intelligence”.
As a new thematic program and new paradigm in AI research for the future, and
with respect to the three problems AI research faced, the concept of “Advanced
Intelligence” is defined as, and featured with, (1) the unified theory of
AI, (2) closed interaction with NI, and (3) the integrative research on
consciousness, emotion, cognition and intelligence. In other words, Advanced
Intelligence, the new thematic program and paradigm for the future, is intended to
make great efforts to overcome the problems the traditional AI research faced so
that new progresses can be expected.
3. Methodology and Approach
As the first feature of the definition of Advanced Intelligence is concerned, the
finding of the unified theory of AI should be put on the most urgent place in
Advanced Intelligence research and yet the mutually separation among the current
schools on AI study indicates that new methodology and approach to AI is the first
need.
It has been proved 14 that the mechanism of intelligence formation is the most
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essential way than any other ways, like structure, function, or behavior of a system,
in terms of touching the nucleus of intelligence and it is thus much more rational
to take the “common core of mechanism for intelligence formation” as the central
approach to AI, which may reasonably be named the Mechanism Approach.
It was also proved in 14 that the Information-Knowledge-Intelligence Transformation can serve very well as the specific implementation of the Mechanism Approach if looking more closely at the model of intelligence formation from the point
of views of Natural Intelligence. This can very clearly be explained from Fig.1.
Fig. 1. Model of intelligence formation from the view of natural intelligence.
As can be seen from Fig.1 that the formation of intelligence of any intelligent
systems, human beings in particular, should typically consist of the following steps:
Step (1) Information Acquisition
For any given problem (P), constraints (C) to which the problem solving
should observe, and the prescribed goal (G) for the problem solving, the
first step for any intelligent systems to take is to acquire the necessary
information concerning with P-C-G;
Step (2) Information Transferring and Processing
The information obtained in step (1) should be transferred with a fidelity
to certain point where information can be processed so that the easy-to-use
version of the information can be available;
Step (3) Knowledge Extraction
In accordance with the information concerning with P-C-G in the easyto-use version, the corresponding knowledge should be formed, either from
cognition or from other measures, as information is phenomena while knowledge is the essence;
Step (4) Strategy (Intelligence) Producing
Advanced Intelligence: Definition, Approach, and Progresses
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For effective solving of the problem, the knowledge and information should
be conversed into strategy under the guidance of the goal;
Step (5) Action Generation
The strategy is in an abstract guideline, which cannot solve the problem
practically. Therefore it should be conversed into action through which the
problem may be solved and the goal be reached;
Step (6) Feedback and Optimization
If there is difference between the results of the previous step and the goal
given (namely the error), then the difference served as a new information
should be feedback to step (1) so that the information, knowledge and
strategy can gradually be improved until the error can be accepted.
As can be seen from the steps above that the information-knowledge-intelligence
transform covers the steps (2)-(4), the core of the mechanism of intelligence formation: from information to intelligent strategy. This can also been seen in Fig.1. As
a matter of fact, this mechanism of intelligence formation is the same for all kinds
of intelligent systems with no exception.
Therefore, for any given P-C-G, a new approach, the Mechanism Approach, to
AI study is presented as follows:
The first part is to acquire the information on P-C-G – the preparation;
The second part is to perform the Information-Knowledge-Intelligence transform
– the core;
The third part is to converse the strategy to action – the execution.
The first part is the information acquisition and the third part is the strategy
execution and both parts are relatively well-known and matured. The only part,
which is new and does really matter, is the second one. For conciseness, only the
Information-Knowledge-Intelligence Transformation will be mentioned as the crucial means for implementing the Mechanism Approach. For brevity, sometimes, the
Information-Knowledge-Intelligence Transform is also termed as Information Conversion – information is conversed to knowledge and further to intelligence.
It is obvious that the Information Conversion in Mechanism Approach needs
the supports from Information Theory. However, the internationally well-known
information theory, which is Shannon Theory 15 , would not be able to meet the
needs from AI study as it ignored the content and value factors of information
which are very critical to intelligence study. In 16 , a new information theory, named
Comprehensive Information Theory, has been established for effectively support the
needs for intelligence research.
At the same time, the information conversion also needs the support from knowledge theory. However, there is no such theory available for the time being. In 17 , a
framework on knowledge theory was set up. Particularly, a new theory called Knowledge Ecology has been firstly presented in 18 . The Knowledge Ecology is expressed
as Empirical Knowledge-Regular Knowledge-Commonsense Knowledge Conversion
supported by innate Knowledge as is pictorially expressed in Fig.2.
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Y. X. Zhong
Fig. 2. Model of knowledge ecology.
Having integrated the discussions we have had above, particularly the theory of
Knowledge Ecology, the Mechanism Approach can further be specified, and classified, into the following three types, Type A, Type B, and Type C, as indicated
in Table 1, which shows that, facing the same information on problem, there will
be three specific form of Mechanism Approach, depending on whether empirical,
regular, or commonsense knowledge is utilized.
Table 1. Classification of mechanism approach.
Mechanism Approach
Type A
Type B
Type C
Information
Information
Information
Information
Knowledge
Empirical Knowledge
Regular Knowledge
Commonsense Knowledge
Intelligence Strategy
Empirical Strategy
Regular Strategy
Commonsense Strategy
Compared with the Structuralism, Functionalism, and the Behaviorism approaches, an apparent impression is that only the Mechanism Approach touches
directly the core essence of intelligence. On the other hand, the Mechanism Approach is also feasible in implementation. In one words, the Mechanism Approach
is much more rational and thus powerful in terms of AI research.
4. Progress and Significance
The first progress that has been achieved by Mechanism Approach, featured with
information-knowledge-Intelligence Transformation, is the establishment of Unified
Theory of AI.
The three schools in AI study, the Structuralism Approach based neural network theory, the Functionalism Approach based expert system theory, and the Behaviorism Approach based sensor-motor systems theory, have long been mutually
separated from each other. Even worse, sometimes they may mutually attack each
other.
Interestingly enough, it is discovered that the three schools can well be unified
Advanced Intelligence: Definition, Approach, and Progresses
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within the framework of Mechanism Approach. This is because of the fact that neural networks can be regarded as systems which converse information into empirical
knowledge, via training, and further into empirical strategy; expert systems is the
systems which converse information into regular knowledge, via system designers,
and further into regular strategy; and the sensor-motor systems is a kinds of systems
which converse information into commonsense knowledge, via humans, and further
into commonsense strategy. This can also be shown in Table 2 below.
Table 2. Classification of mechanism approach.
Mechanism
Approach
Type A
Type B
Type C
Information
Received
Information
Information
Information
Knowledge Conversed
From Information
Empirical Knowledge
Regular Knowledge
Commonsense Knowledge
Intelligence Strategy
Generated
Empirical Strategy
Regular Strategy
Commonsense Strategy
Examples
Existed Already
Neural Networks
Expert Systems
Sensor-motor Systems
Therefore, Structuralism Approach based neural network theory, Functionalism
Approach based expert system theory, and Behaviorism Approach based sensormotor theory, are respectively the specific, and harmonious, examples of Mechanism Approach based artificial intelligence theory. The three separated, and inharmonious, theories has now become a unified, and harmonious, theory of Artificial
Intelligence.
This is a very encouraging, and really meaningful, result for Mechanism Approach and Advanced Intelligent research.
It is also important to note that, the coverage of Mechanism Approach should be
much larger that that of neural networks, expert systems, and senor-motor systems.
This is because that neural network is only one way for extracting empirical knowledge from information; expert system is only one way for using regular knowledge;
and sensor-motor system is only one way for using commonsense knowledge. This
is also an important matter.
It is believed that Mechanism Approach is not only workable and effective for
AI study but also workable and effective for the study on emotion and consciousness because the latter two are also some kinds of information conversion based
on Comprehensive Information Theory as well as Knowledge Ecology. Therefore,
Mechanism Approach, instead of other approaches, like Structuralism, Functionalism, and Behaviorism alone, can be the effective approach to the study of Advanced
Intelligence. For the further results in this respect, we would like to make report in
next paper.
5. Remarks and Conclusions
Although the research in Artificial Intelligence has made good progress during the
past half century, it has been facing a number of problems which are mainly resulted
from research methodology, instead of technical skills. Some of the typical problems
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include 1) internally separation among the three major schools, 2) externally separation with Natural Intelligence research, and 3) ignoring some important issues,
such as consciousness, emotion, and their relations with intelligence. Therefore, new,
and more rational methodology in the research is necessarily and urgently needed.
In response to the need stated above, this paper presented a new thematic program, namely “Advanced Intelligence”, and at the same time a new methodological
paradigm, termed “Mechanism Approach”, with the purpose for making change and
improving the situation in Artificial Intelligence research.
The Advanced Intelligence is defined and featured with 1) internally harmoniousness, 2) externally interaction with Natural Intelligence research, and 3) seeking the integration among consciousness, emotion and intelligence. In the meantime,
the Mechanism Approach is defined by Information-Knowledge-Intelligence Transformation.
As just in the beginning, it is rather impressive that the Mechanism Approach
has already provided a good framework that changes the “mutual separation”
among the Structuralism, Functionalism, and Behaviorism approaches into “mutually harmonious unification”, bringing in reality a Unified Theory of Artificial
Intelligence and achieving the first goal defined in the research of Advanced Intelligence. This is a good progress achieved based on the interaction between Artificial
and Natural Intelligence.
The further efforts to make in the future is to apply the Mechanism Approach
for achieving the other goals, the research of consciousness, emotion, and their
relation with intelligence, during which more closed cooperation with the research
of Natural Intelligence will also be emphasized. The author of the paper hope to see
more researchers worldwide in the field will join in the promising, although difficult,
field of research in the years to come.
Lastly, but not the least, it is interesting to see that the basic line of thought
for Advanced Intelligence is almost in agreement with the one in Artificial General
Intelligence (AGI), which is a newly borne school in artificial intelligence research in
united states of America 18 . It will be mutually beneficial to have close cooperation
between researchers from AGI and those from Advanced Intelligence.
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Yixin Zhong
(Member)
Received BE degree in 1962 and ME degree in 1965
both from the Department of Radio Engineering, Beijing
University of Posts and Telecommunications (BUPT),
Beijing, China. He was an academic visitor at Imperial
College of Science and Technology, University of London,
U.K from 1979 to 1981, and is now a professor of BUPT.
He is currently the president of Chinese Association of Artificial Intelligence. The areas of his interests in research
include Information Theory, Information Science, Artificial Intelligence, Neural Networks, Information Networks,
Information Economics, and Decision-making Theory.