ArticlePDF Available

Abstract

Chinese researchers have been conducting AI research for more than four decades. Here, we sample some of the most promising areas Chinese AI researchers are studying and predict related future activities. The AI areas research area follows: automatic geometrical theorem proving: beyond mathematical mechanization, intelligence science: toward a molecular-level understanding, large-scale knowledge processing: an open approach, computer-facilitated art and animation: from research to industry, knowledge as a commodity: from software to Knowware
Editor: Alun Preece
University of Aberdeen
apreece@csd.abdn.ac.uk
Global IS
areas Chinese AI researchers are studying and predict related
future activities.
Automatic geometrical theorem proving:
Beyond mathematical mechanization
In 1997, Wen-Tsun Wu, a mathematics professor and
an academician from the Chinese Academy of Sciences
(CAS), won the Herbrand Award for Distinguished Contri-
butions to Automated Reasoning, the highest recognition
for researchers in that field. Other recipients of this award
include pioneers such as Larry Wos, Woody Bledsoe, and
Alan Robinson. Wu invented Wu’s method in the 1970s,
initiating the use of algebraic geometrical methods to obtain
analytical solutions to complex mathematical problems.1
As Deepak Kapur, editor in chief of the Journal of Auto-
mated Reasoning, pointed out, “Wu’s work re-invigorated
the field of Automated Geometrical Theorem Proving.”2
The cornerstone of Wu’s method is Wu-Ritt’s zero de-
composition algorithm, which Wu and others have used
to develop a theoretical framework that scientists and en-
gineers can apply to solve a range of problems, including
algebraic-equation systems and differential-equation sys-
tems. Researchers have also applied Wu’s method and
related frameworks to areas such as intelligent CAD/
CAM, computer vision, and robot design. In 1990, Wu
founded the Center of Mathematical Mechanization Re-
search at CAS to pursue this wide range of research direc-
tions and application innovations.
We predict that in the next 50 years, researchers will ex-
tend mathematical mechanization based on Wu’s method,
applying it in a much broader set of mathematical studies.
In addition, Wu’s method will likely play an important role
in an increasing number of application areas. In particular,
we expect wide use of the intelligent CAD/CAM platform
based on Wu’s framework, and we predict that its commer-
cialization will bring about a new generation of CAD/CAM
technologies.
China is also investing intensively in robot research, so
we wouldn’t be surprised to see Wu’s method applied to
industrial, underwater, servant, or even lunar robots.
Intelligence science: Toward a molecular-
level understanding
Intelligence science intersects with a range of fields,
including brain science, cognitive science, neural science,
psychology, molecular biology, biological physics, mathe-
matical and physical sciences, computer science, and infor-
mation science. The highly complex nature of human brain
functions requires a cross-disciplinary approach to explore
and understand the nature of intelligence and cognition at
various levels, from molecules and cells to the entire brain
and its functions. Clearly, research methodologies and re-
sults from intelligence science and AI can mutually benefit
each other.
The Chinese National Basic Research Program (also
called the 973 Program) has funded several major projects
investigating brain-related research and intelligence science
(see www.973.gov.cn/English/ReadItem.aspx?itemid=147;
also see itemid=247, 159, and 240). Another recently com-
pleted project, headed by CAS academician Xiongli Yang,
is “Fundamental Research on Brain Functions and Major
Brain Diseases.” This project studied cell and molecular
mechanisms of basic neural activities and the mechanism
underlying the formation of several important brain diseases.
Another project, “Fundamental Research on Brain Develop-
ment and Adaptation, headed by CAS academician Aike
Guo, focused on neural nutrition factors regulating neural
cell division, survival, migration, and growth, as well as the
development and adaptation of perception, memory, and
visual cognition. A third project, Advanced Interdiscipli-
nary Research on Intelligence Science,” funded by CAS, led
to interesting findings in selective attention and perception
and resulted in new advances in brain-imaging technology.
In yet another project, headed by CAS academician Chaoyi
Li, researchers studied cats’ brains and revealed a new low-
level cognitive structure that consists of many small spher-
AI Research in China:
50 Years down the Road
Ruqian Lu, Chinese Academy of Sciences
Daniel Zeng and Fei-Yue Wang, Chinese Academy of Sciences and University of Arizona
MAY/JUNE 2006 1541-1672/06/$20.00 © 2006 IEEE 91
Published by the IEEE Computer Society
Year 2006 marks the 50th anniversary of the birth of
modern artificial intelligence research. Chinese re-
searchers have been conducting AI research for more than
four decades. Here, we sample some of the most promising
oids for processing complex images. This
structure differs from all known brains’cog-
nitive structures and has proven important in
image processing.3
The recently published call for proposals
from the Intelligent Information Processing
unit of the 973 Program substantially empha-
sizes cognitive science and brain science.
The goal is to further explore the nature
of human brain functions and intelligence,
develop the computational theories of cog-
nitive science and intelligent systems, and
investigate new theoretical and technological
foundations enabling next-generation intelli-
gence system design and development.
In the next 50 years, we expect China to
make major advances in intelligence science
research in the study of various types of brain
activities such as consciousness, attention,
learning, memory, language, thinking, rea-
soning, and even emotion. Some particularly
promising research directions include
how brains integrate and coordinate
nerve cell cluster activities,
how nerve cell clusters perceive, express,
transmit, and reconstruct visual signals
and awareness,
how we can use experimental methods
such as nuclear magnetic resonance to
observe nerve cell cluster activities, and
how we can develop and evaluate mathe-
matical and computational methods to
model and simulate nerve cell cluster
activities.
Other relevant research areas include mod-
eling the creative thinking process, study-
ing the mapping of thoughts to language,
and studying the coevolution of reasoning
and language skills.
Large-scale knowledge
processing: An open approach
In “Some Challenges and Grand Chal-
lenges for Computational Intelligence,”4
Edward Feigenbaum posed three major
challenges to future computer science
development:
developing computers that can pass the
Feigenbaum test, a restricted version of
the Turing test in a given subject domain,
developing computers that can read docu-
ments and automatically construct large-
scale knowledge bases to significantly
reduce the complexity of knowledge-
engineering efforts, and
developing computers that can compre-
hend Web contents and automatically
construct related knowledge bases.
Researchers in China are tackling the last
two challenges. Although both are essen-
tially a massive knowledge-engineering
challenge, the difference between the two
is that the third challenge involves an open
environment. Openness typically refers to
a lack of standardization in knowledge
representation and semantic understanding,
the dynamic nature (that is, emergence and
disappearance) of knowledge sources, and
contradictions, ambiguities, noise, incom-
pleteness, and nonmonotonicity in knowl-
edge.
Answering these challenges calls for
new methodological and technological
innovations. One ongoing project in China
addresses this issue. In 1995, Cungen Cao
proposed building a National Knowledge
Infrastructure; in 2000, the Chinese NKI
project officially started.5The CNKI’s
early effort, inspired by the Cyc project,6
had focused on developing a nationwide
network-based knowledge-service system,
serving scientific education, research, and
social services needs. This system has three
components:
an internal platform providing the core
usable services and software libraries,
external applications developed by part-
nering organizations in various applica-
tion domains, and
a module interfacing with the outside
world (including the Web and Semantic
Web).
The CNKI’s knowledge base has approxi-
mately 3 million knowledge records; when
completed, it will contain approximately
100 million records. CNKI aims to provide
knowledge to anyone who needs it at any
time, anywhere, and to support community
knowledge, communications, and coordi-
nation needs.
The Internet has become an indispens-
able source of knowledge for people around
the globe. According to recent surveys,7
China has over 100 million Internet users,
and the number is still growing rapidly.
Closely related to Feigenbaum’s third chal-
lenge, despite the Internet and Web’s exten-
sive use and development in the past decade,
many critical and pressing challenges remain
in Web-based information management.
First, the effectiveness of Web-based in-
formation retrieval measured by recall and
precision is still low. Second, knowledge
management on the Web is still based on
semistructured Web pages as opposed to
semantics-driven knowledge items. Third,
current Web technology supports access to
raw information but not to processed, refined,
customized knowledge. For instance, after
you enter one or more keywords into a search
engine such as Google, the search presents
you with hundreds of thousands of poten-
tially relevant Web pages. Clearly, we need
more advanced knowledge-driven online
search and browsing technology to address
these challenges.
Such new technology should demonstrate
both high recall and precision and support
online knowledge processing and mining.
It should have adequate natural language
processing capabilities to process and inte-
grate potentially conflicting, uncertain, and
ambiguous knowledge. In addition, it should
be able to organize, edit, refine, and mine
data and knowledge and facilitate the transi-
tion from information to knowledge. Fudan
University computer scientists are developing
elements of this technology. They plan to
provide a generic online knowledge acquisi-
tion and management tool to users as part of
their Web environment.
Computer-facilitated art and
animation: From research to
industry
The Chinese animation industry is experi-
encing significant growth. Approximately
3,600 television channels nationwide broad-
cast cartoon programs, and another 50 chan-
nels dedicated specifically to cartoons are
expected in the near future (see www.
Chinanim.com/dh1 (in Chinese)). According
to a recent estimate from www.Chinanim.
com, the total air time of cartoon programs
exceeds one million minutes annually in
China. Such a huge demand is creating a
serious shortage of professional animators
(the current gap is approximately 150,000).
Nationally, more than 200 colleges and uni-
versities offer majors related to cartoon art
and production.
Computers are playing an increasingly
important role in the animation industry. In
particular, computer-facilitated animation
is becoming a necessity. The CAS Acad-
emy of Mathematics and Systems Science
92 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS
MAY/JUNE 2006 www.computer.org/intelligent 93
has been conducting research in this area
since 1989 and is cooperating with the Bei-
jing University of Technology to commer-
cialize its research results.8
In the next 50 years, computer-facilitated
art and animation will find more extensive
and wider applications in the real world. For
example, a computer will be able to automat-
ically translate 80 percent of a natural lan-
guage script into animation. It will be able to
1. analyze the natural language script,
2. understand the story’s plot,
3. decompose the script into a detailed
scene-by-scene script, and
4. plan and execute animation produc-
tion according to the movie director’s
interpretation, including scenarios,
roles, actions, cameras, color, and
light planning.
Obviously, each of these steps confronts
many AI challenges, and many relate to
image reasoning and common-sense rea-
soning. Research in these areas could pos-
sibly benefit other applications as well.
Knowledge as a commodity:
From software to knowware
Although knowledge-based software
engineering has become a widely accepted
R&D area, knowledge is still largely embed-
ded in software products. Recent research
suggests that isolating knowledge from soft-
ware and treating the two as independent
entities for both research and system devel-
opment will bring about many advantages.9
This might also address many of the diffi-
culties that current software and intelligent
system development efforts face.
For example, following this isolation prin-
ciple, complicated software might be devel-
oped jointly by two teams: the software engi-
neering team and the knowledge engineering
team. Software engineers don’t need to
learn new domain knowledge when they
switch from one application domain to
another, and domain experts can play a more
active role in software development by con-
tributing their knowledge directly to system
development. Following this emerging
framework, knowware is an independent,
standards-compliant, and executable knowl-
edge module. An early example of knowware
was the Promis object, where domain models
(preliminary forms of knowware) are dealt
with independently from the information
management systems.10
In the next 50 years, we expect the IT
industry to comprise three parts: hardware,
software, and knowware. Knowware devel-
opment will exploit AI and software engi-
neering research. Similar to the establishment
of software engineering as a field, we expect
knowware engineering to emerge as a study
of scientific principles, engineering guidance,
and best practices for managing knowware
development. In effect, knowware engineer-
ing will be the large-scale-production form of
knowledge engineering. It will have its own
model of knowware development life cycles,
practical guidelines on industry-strength
knowledge acquisition, and suggestions for
efficiently maintaining and updating knowl-
edge and for resolving conflicts, ambiguities,
noise, redundancies, and incompleteness.
We don’t have space here to cover all
the important AI topics that Chinese re-
searchers are actively studying (including
machine learning, pattern recognition, and
emerging social computing and informatics).
For a comprehensive picture of Chinese AI
research, please refer to related Chinese jour-
nals, such as the Journal of Pattern Recogni-
tion and Artificial Intelligence (in Chinese),
the Journal of Computer Science and Tech-
nology (in English), the Journal of Software
(in Chinese), and the Journal of Computer (in
Chinese).
References
1. W. Wu, “On the Decision Problem and the
Mechanization of Theorem-Proving in Ele-
mentary Geometry, Scientia Sinica, vol.21 ,
1978, pp. 159–172. (Republished as “Some
Recent Advances in Mechanical Theorem
Proving of Geometries,” Automated Theorem
Proving: After 25 Years, W.W. Bledsoe and
D.W. Loveland, eds., 1984, pp. 235–242.)
2. D. Kapur, “Geometry Theorem Proving
Using Hilbert’s Nullstellensatz, Proc. Symp.
Symbolic and Algebraic Manipulation (SYM-
SAC 86), ACM Press, 1986, pp. 202–208.
3. W. Xu, Z.-M. Sheng, and C.-Y. Li, “Spatial
Phase Sensitivity of V1 Neurons in Alert
Monkey, Cerebral Cortex, vol. 15, no. 11,
2005, pp. 1697–1702.
4. E.A. Feigenbaum, “Some Challenges and
Grand Challenges for Computational Intelli-
gence,” J. ACM, vol. 50, no. 1, 2003, pp.
32–40.
5. C. Cao et al., “Progress of National Knowl-
edge Infrastructure,” J. of Computer Science
and Technology, vol. 17, 2002, pp. 523–534.
6. D.B. Lenat and P.V. Guha, “Building Large
Knowledge Based Systems, Representation
and Inference in the CYC Project, Addison
Wesley, 1990.
7. “The 17th CNNIC Chinese Internet State-of-
Art Report,” 2006, www.cnnic.net.cn/html/
Dir/2006/01/17/3508.htm (in Chinese).
8. R. Lu and S. Zhang, Automatic Generation of
Computer Animation, LNAI 2160, Springer,
2002.
9. R. Lu, “From Hardware to Software to
Knowware: IT’s Third Liberation?” IEEE
Intelligent Systems, Mar./Apr. 2005, pp. 82–85.
10. R. Lu and Z. Jin, Domain Modeling Based
Software Engineering: A Formal Approach,
Kluwer Academic Publishers, 2000.
Ruqian Lu is an
academician in the
Chinese Academy of
Sciences and a pro-
fessor of computer
science at the Acad-
emy of Mathematics
and Systems Sci-
ences, the Institute
of Computing Technology, and Fudan Uni-
versity. Contact him at rqlu@math.ac.cn.
Daniel Zeng is an
associate professor
in the University of
Arizona’s Depart-
ment of Manage-
ment Information
Systems and the
director of the
Intelligent Systems
and Decisions Laboratory. He’s also affili-
ated with the Institute of Automation at
the Chinese Academy of Sciences. Con-
tact him at zeng@eller.arizona.edu.
Fei-Yue Wang is a
professor in the
University of Ari-
zona’s Systems &
Industrial Engi-
neering Depart-
ment and the direc-
tor of the Program
in Advanced
Research of Complex Systems. He’s also
director of the Key Laboratory of Com-
plex Systems and Intelligence Science at
the Chinese Academy of Sciences. Con-
tact him at feiyue@sie.arizona.edu.
... If the user changes the input, the system would immediately respond and generate a new animation with the updated building in it. This text-to-scene conversion is enabled with the support of a knowledge-based approach (Lu, et al., 2002;Lu, et al., 2006). The core is a model base where architectural structures and components are organized in a hierarchy. ...
Article
Ancient Chinese architecture represents a unique contribution to the world’s architectural heritage. However, there has been a serious lack of computer supported tools for representing, modeling and designing different forms of ancient Chinese buildings. We present ICA3D, an intelligent computer-aided system for designing ancient Chinese-style architecture. With ICA3D, the user has multiple choices of interaction from interactive menus to Chinese natural language description. The Semantic Web technologies are used to represent the domain knowledge including complex rules for composing complete timber structures from individual pieces of wood and rules for inferring construction sequences. Compared with other CAD technologies, the approaches presented in this paper feature a combination of user interaction and automation, a text-to-scene pipeline, and a scalable knowledge infrastructure supported by automated reasoning.
... Intelligent rail transportation systems represent a critical enabling framework. [1][2][3][4] T he Chinese rail transportation system has been going through a period of rapid improvement and ...
Article
The Chinese rail transportation system has been going through a period of rapid improvement and innovation. Despite this rapid development, the railroad lines are far from meeting the country's expanding travel and freight transportation needs. According to some recent estimates, the current systems meet only 35 percent of the freight orders on a typical day. The shortfall has significant negative economic impact on many sectors of the economy. During major national holidays and festivals, getting a railway ticket and making the trip are major endeavors for travelers. As a national response to these gaps between capacities and needs, the government is investing heavily in the rail transportation system. This rapid expansion is bringing significant opportunities as well as challenges to both academia and industry. The next-generation Chinese rail transportation system will require major advances in related technologies. Intelligent rail transportation systems represent a critical enabling framework
Article
Full-text available
The study seeks to investigate the awareness and adoption of modern technologies which are collectively called (IRTS) Intelligent Railway Transport Systems by the NRZ (National railways of Zimbabwe) of Zimbabwe. Adoption of these technologies are on an increasing trend in developed and developing countries, installation and implementation of a railway system called RailTracker in Tanzania has improved railway services in that country, in Uganda and Kenya the Rift Valley Railway (RVR) has introduced GPS technology to track trains. In India a system is used to detect defects in rolling stock while they are on the run. Where these systems have been implemented, they have significantly improved the efficiency, safety and quality of service of railway operations. In Zimbabwe the rail network is an important transport infrastructure enabling movement of goods and passengers. Primary research was carried out using questionnaires and semi structured interviews, data was collected from 67 participants comprising Engineers, Technicians, Train Drivers and Station Managers. 98% of the technical participants indicated that they were aware of IRTS however the adoption of the systems by the NRZ is at 0%. 100% of the Managers indicated that they were aware of IRTS and the company is willing to adopt them but currently no system has been installed Secondary research was conducted to identify and study similar projects elsewhere, their success as well as the difficulties encountered during their implementation. Secondary data was collected from books and the Internet. Article visualizations: </p
Article
As the intelligent railway is the trend of current railway development, the paper aims to show an overview of the Railway Intelligent Transportation System (RTI'S) in China. Firstly, the current transportation situation in China is given and then some basic concepts of RTI'S are talked about as well. Secondly, the necessity of RTI'S is analyzed to show its strategic position. Then, the overall architecture of RTI'S is introduced here to make a clear picture of RTI'S and how it works. Finally, apart from key technologies in RTI'S, its sustainable strategies are mentioned to show the future development of RTI'S.
Conference Paper
In China, the railway has always been a key factor in the economic development. The intelligent railway is the trend of current railway development, since it plays an important role in the sustainable transportation system that minimizes environmental impacts and contributes greatly to social and economic prosperity. And in this paper, firstly the current transportation situation in China is given and then some basic concepts of the Railway Intelligent Transportation System (RITS) are talked about as well. Secondly, based on the railway building programs in China, the necessity of RITS is analyzed to show its strategic position. Then, according to the previous research work, the overall architecture of RITS is introduced here so as to make a clear picture of what is RITS and how it works. Finally, apart from key technologies in RITS, its sustainable strategies are mentioned to show the future development of RITS.
Conference Paper
Full-text available
Knowledge and Intelligence have a much closed relationship. Knowledge is both the crystallization and source of intelligence. Knowledge embodies intelligence, and intelligence emerges from knowledge. Every ICAX system (Intelligent Computer Aided X, where X may mean any domain, such as education, design or manufacturing, etc.), such as ICAI (I = Instruction), ICAD (D = Design), ICAM (M = Manufacturing), etc., has its intelligence based on a content rich knowledge base. In this sense, we may have the formula: ICAX = CAX + X knowledge base. Using this formula, we have developed a methodology of generating knowledge based system automatically. The core idea is to develop a domain-oriented pseudo-natural language (PNL for short), where PNL means a normalized subset of some natural language, which can be easily parsed by computer. Each domain expert may use this language to write down his knowledge and experience. A PNL compiler then compiles ’program’s written in this PNL to form a domain knowledge base. Combined with a preexisting system shell, a prototype of the knowledge based system is automatically generated. We have applied this idea to automatic generation of ICAI and ICASE (SE = Software Engineering) systems. The following problem is how to generalize this idea. Can the development of knowledge base and system shell be done by different people or groups? Can the knowledge base be easily renewed or even become an independent commodity? Finally, we have got an answer to this problem. The commodity form of such knowledge base is knowware. In general, knowware is a commodity form of knowledge. More precisely, knowware is a commercialized knowledge module with documentation and intellectual property, which is computer operable, but free of any built-in control mechanism, meeting some industrial standards and embeddable in software/hardware. The process of development, application and management of knowware is called knowware engineering. Three different knowware life cycle models are discussed: the furnace model, the crystallization model and the spiral model. Software/knowware co-engineering is a mixed process involving both software engineering and knowware engineering issues. It involves three parallel lines of developing system components of different types. The key issues of this process are how to guarantee the correctness and appropriateness of system composition and decomposition. The ladder principle, which is a modification of the waterfall model, and the tower principle, which is a modification of the fountain model, are proposed.
Article
Full-text available
This book proposes to separate knowledge from software and to make it a commodity that is called knowware. The architecture, representation and function of Knowware are discussed. The principles of knowware engineering and its three life cycle models: furnace model, crystallization model and spiral model are proposed and analyzed. Techniques of software/knowware co-engineering are introduced. A software component whose knowledge is replaced by knowware is called mixware. An object and component oriented development schema of mixware is introduced. In particular, the tower model and ladder model for mixware development are proposed and discussed. Finally, knowledge service and knowware based Web service are introduced and compared with Web service. In summary, knowware, software and hardware should be considered as three equally important underpinnings of IT industry. Ruqian Lu is a professor of computer science of the Institute of Mathematics, Academy of Mathematics and System Sciences. He is a fellow of Chinese Academy of Sciences. His research interests include artificial intelligence, knowledge engineering and knowledge based software engineering. He has published more than 100 papers and 10 books. He has won two first class awards from the Academia Sinica and a National second class prize from the Ministry of Science and Technology. He has also won the sixth Hua Loo-keng Mathematics Prize.
Article
Full-text available
This paper presents the recent process in a long-term research project, called National Knowledge Infrastructure (or NKI). Initiated in the early 2000, the project aims to develop a multi-domain shareable knowledge base for knowledge-intensive applications. To develop NKI, we have used domain-specific ontologies as a solid basis, and have built more than 600 ontologies. Using these ontologies and our knowledge acquisition methods, we have extracted about 1.1 millions of domain assertions. For users to access our NKI knowledge, we have developed a uniform multi-modal human-knowledge interface. We have also implemented a knowledge application programming interface for various applications to share the NKI knowledge.
Article
Full-text available
Knowware is a natural development in IT after hardware and software. It is a knowledge module that is independent, commercialized, suitable for computer manipulation, and directly usable by a class of software. It is directly related to intelligence: if software is the condensation and crystallization of knowledge, then knowware is the condensation and crystallization of intelligence. Pseudo natural languages (PNLs) such as ZHIWEN make it possible to develop knowware in a fast and massive fashion. They also offer a way to separate software developers from knowware developers, so that software engineers need only develop software tools, leaving the task of knowware development to the domain-specific experts.
Article
The challenges and grand challenges for researchers doing computational intelligence, in the artificial intelligence area of computer science, are discussed. There is a need to facilitate the building of general and specific ontologies. It is suggested to give the multitude of web page creators a markup language in which each can do an extensive semantic markup of textual submission. To implement the Grand Vision, knowledge engineers must build a system of 'semantic scrapers' that will access the semantic markups, integrate them appropriately into the growing knowledge base.
Article
Transmitting multimedia data over a CDMA channel presents a new set of challenges. Sometimes, data demands will exceed the system capacity, in which case the system must make the most efficient use of its limited resources. The resources we consider are: fixed bandwidth available for each user and the transmit power budget for each cell. We present our approach for unifying power control, variable forward error correction (VFEC), and scheduling for allocating the system resources. Our objective is to maximize the overall system satisfaction, which we call “system utility”. This objective is achieved by applying a distributed algorithm which divides the overall optimization problem into a hierarchy of three levels (system, cell and user). At each level, the system performs independent and parallel optimizations; the critical information is then passed to the higher level for further optimization. Finally, an iterative and distributed algorithm is applied at the system level to achieve the overall system optimization