- Email: [email protected]
A survey on intelligence approaches that may be useful for information and communication technology research
THE JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOMMUNICATIONS Volume 15, Issue 1, March 2008
ZHONG Yi-xin
A survey on intelligence approaches that may be useful for information and communication technology research CLC number TN919
Document A
Article ID
1005-8885 (2008) 01-0001-05
Abstract The concepts on information and communication technology (ICT) and “intelligence” are defined firstly and the analyses on the environment and requirements for ICT are then followed. Based on the definitions and the analyses, a survey on intelligence approaches that may be useful for ICT is thus made. The conclusion drawn from the survey is a recommendation by saying that intelligence approaches have been one of the keys for further development of the entirety of ICT and therefore should receive much more attentions from ICT researchers in the years to come.
types of uncertainty can no longer be ignored. Moreover, the world has been confronted with more and more complicated systems and complicated problems. Hence, new methodologies and paradigms in ICT research are unavoidably needed which must be able to intelligently cope with such kinds of issues as uncertainty, complexity, convergence, unification and the like. This convinces why a survey on intelligence approaches to the research for ICT is necessary.
Keywords uncertainty, complexity, intelligence approaches, ICT
As is well known presently, ICT stands for information and communication technology in which communication technology has a unique definition as the one that performs the function of information transferring whereas the terminology of information technology (IT) has ever been quite confused. As a matter of fact, IT in literature may either be referred as to computer technology alone, or the mixture of computer and communication technology, or to the collection of computer, communication, and control technology, and so forth. In other words, although the term of ICT has been very widely used, its definition has never been made clear enough. To make it clear, the definitions of ICT and intelligence will be given in what follows and some notes and discussions related to the definitions will be made thereafter. Definition 1 A general conception of ICT is defined as a set of technologies that can successfully be used for dealing with all kinds of information processes. Note 1 The term of “all kinds of information processes” in the definition should not only include such kinds of processes as information acquisition (i.e., sensing), information transferring (communication), and information manipulation (computation), but should also include the processes of information cognition (knowledge producing), infonnation regeneration (strategy creation) and information execution (control) since knowledge is the products of information cognition and strategy is the products of knowledge activation, a kind of higher level information. It is clear that the definition of ICT given above is rather the same as the one of IT. The adoption of the term of ICT, compared
1 lntroductlon Due to the old tradition of divide and conquer methodology in science and technology, the studies on communication and information technology, the latter of which was frequently referred as to the computer technology, have long been separated from each other. This has brought to the ICT research rather serious problems. One of such problems is the different paradigms being adopted in the centralized telecommunication network and distributed Internet. Moreover, due to the old tradition of deterministic point of view in modem science and technology, the studies on information technology, particularly in the studies of computer technology, have paid little attention to the uncertain phenomena than that it should have. This ignorance of uncertain factors has also brought to the practice many serious problems because of the fact that the uncertain phenomena exist pervasively. Technology today is facing a new situation where the centralized telecommunication network and distributed Internet technology have gradually to converge and the fact of various Received date: 2008-01-05
W O N G Yi-xin (m)
School of Information Engineering, Beijing University of Posts and Telecommunications. Beijing 100876,China E-mail:[email protected]
2 DdntWons on ICT and Intdllgence [I]
2
The Journal of CHUFT
with IT, is just to emphasize the role that communication technology plays. Definition 2 When a problem, a group of constraints related to the problem, and the prescribed goal for the problem handling are given, the intelligence is defined as a set of abilities for solving the problem, meeting the constraints and reaching the goal. Note 2 The term of “a set of abilities” in the definition should include the ones of (1) acquiring all the information concerning the given problem, the given constraints, and the given goal; (2) transferring the acquired information to an assigned position; (3) manipulating the information to the one easy to use; (4) refining the information into knowledge (cognition) and associating the newly refined knowledge with the related ones already existed in knowledge base; (5) creating the strategy for solving the problem based on the knowledge on one hand and guided by the goal on the other hand; and (6) transferring the strategy created to another point; (7) conversing the created strategy into the corresponding action through which the strategy is executed. These abilities and their relations can clearly be shown in Fig. 1
lnformation (3) nianipulation
Information cognition
lnformation (2) transferring
Knowledge
lnformation
lnformation
Fig. 1 Model of basic intelligence process
It is worth of mentioning that both the process of defining the problem associated with the constraints to be handled and that of defining the prescribed goal for the problem solving are termed the implicit intelligence whereas the processes (1)-(7) in Fig. 1 are termed the explicit intelligence. Till the present time, the Implicit Intelligence is still full with mysteriousness and is difficult to cope and may remain the same at least for the near future. The explicit intelligence, on the other hand, is much more tangible, attractive, and challengeable though. In fact, there has already been a discipline in scientific research named artificial intelligence (AI) that has been devoted to the understanding and implementation of the explicit intelligence. It is interesting to note from Fig. 1 that the model of intelligence process defined in definition 2 is very much the similar to the model of ICT defined in definition 1 as listed below: Unit (1) in Fig. 1 corresponds to sensing technology in ICT;
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Unit (2) and (6) are similar to communication technology; Unit (3) has the same function as computation technology; Unit (4) and (5) are equivalent to artificial intelligence; Unit (7) has a counterpart with control technology. The similarity between the model of ICT and that of intelligence process indicates that ICT should be able to perform its functions in such a way that an intelligent system can do. In other words, in addition to artificial intelligence in ICT, it is possible and also reasonable to make the other components in ICT to be intelligent units: intelligent sensing, intelligent communication, intelligent computation, and intelligent control technology, and after all, an entirety of intelligent ICT. This analysis paves a feasible way for ICT to adopt the intelligence approaches.
As time going on, both the environment and requirements
for ICT today and in the future are undergoing historical transition. The great changes mainly caused by the transition to a new era can briefly be summarized at least as the following aspects. First, people nowadays are facing more and more complicated challenges. Economic globalization, globally scaled information networks, worldwide cooperated manufacturing, internationally connected transportation, climate change, and large-scale natural disasters, etc., are some of the typical examples. The dramatic changes in ICT environment may include the following aspects: the states of the environment may be from fully observable to partially observable, the properties of the object may be from deterministic to stochastic, the period for working may be from episodic to sequential, the nature of the system may be from static to dynamic, the feature of the variable may be from binary to many valued, the characteristic of the system may be from linear to nonlinear, and the number of the units and the span of the systems may be from small scale to large scale, and even more. Second, for successfully dealing with these grand Challenges, it is demanded that all the components of ICT must work together harmoniously as an entirety so that the problems can be solved better and quicker. “All the components of ICT work as an entirety” is rather different from what it was in the past: sensing, communication, computation and control as well as intelligence technology, all the components of ICT, have long been working separately. Yet the “entirety of ICT” should exactly be the same thing as a large scaled intelligent system as mentioned previously. Third, for successfully dealing with these grand challenges,
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ZHONG Yi-xin, et al.: A survey on intelligence approaches that may be useful for.. .
it is also demanded that the performances of “the entirety of ICT” must be as smartly as that an intelligent system can provide. In other words, the entirety of ICT must work smartly regardless of any kinds of complexity that the given problems may have, any kinds of uncertainty that the environment and constraints may possess, and any kinds of changes in constraints and prescribed goal that may happen during the working period. It may also be necessary to mention that the typical characteristics of the solutions for most of the complex problems would be kinds of multi-object and multi-criteria optimization under uncertain and dynamic environment. Summarily, in response to the new environmental conditions and new requirements for ICT described above, a reasonable advice is to take necessary steps for researchers in ICT fields to initiate the studies of the intelligence theory and technology and employ the results in their own fields.
4 Intelllgenm approaches useful for ICT There have been plenty of results achieved in intelligence research as it has at least over six decades history of its development. From very sophisticated analysis for large scaled and nonlinear dynamic and distributed complex systems to very straightforward search for finding optimal solution in certain cases, there are numerous choices: learning, understanding, logic reasoning, problem solving, game-playing, schedule planning, heuristic search, adaptation, diagnosis, selforganizing, and self-repairing, etc. It would, nonetheless, be impossible to list all the results and methods in such a limited space. An acceptable way for quickly doing is to give a brief introduction 4.1 Neural networks approach [3-91
The first approach to intelligence research that can be applied to ICT is the neural network that is an approach to the simulation of the structure of biological neural networks of human brain and is therefore termed structuralism approach to intelligence research, sometimes. As is well known from the brain science, neuroscience, and cognitive science disciplines, biological neural network (BNN) is a kind of very large scaled, and densely connected, massively parallel processing structure with both non-linear and dynamic properties where there are approximately 10” or 10” neurons working simultaneously each of which is a quite simple, but a kind of nonlinear, processing element and each of which is connected to as many as 103-4other neurons. It has been proved in brain science, neurological science and cognitive science that most of the high level cognitive performances are supported basically by BNN lied over the
3
cerebral cortex in human brain. Artificial neural network (ANN) is a sort of simulated version of BNN with much less number of neurons and much loosely connection matrix and simpler learning algorithm and therefore much lower complexity than that in ANN due to the practical limitation from the present industrial manufacturing capabilities. This leads to the limitation of the performances of ANN compared with BNN in human brain. However, ANN in today’s practice still possesses rather extraordinary power in signal processing applications that cannot well match with that of BNN of human brain though. A variety of ANN models can be found in literature among which multi-layer perceptron (MLP) with BP learning algorithm and Hopfield network with gradient descent learning algorithm are most popular. The former is a feed-forward ANN model and the latter is a feedback model. Both of the models have found many successful applications in pattern recognition, fault diagnosis, associative memory, optimization problem solver, and other kinds of signal processing. Since 1990s, ANN research has been stepped into a new stage, the computational intelligence stage, by integrating itself with fuzzy logic and genetic algorithm. It is widely believed that as the progresses that will be made in theoretical research of ANN and in industrial implementation in the future, both the applicability and the performance of ANN will all be improved greatly. 4.2
Loglc inference approach [1&15]
The second approach to intelligence research that can be applied to ICT is the logic inference that is an approach to the simulation of the function of logic thinking in human brain and is sometimes named functionalism approach to the field of intelligence research. Logic thinking carried on in human brain is commonly regarded as one of the most powerful capabilities for human beings. The successful simulation of logic thinking in human brain is of course attractive and meaningful. The most typical feature of logic thinking can be expressed as a series of logic operations on knowledge such as knowledge acquisition, knowledge representation and storage, knowledge inference and goal match, etc. It is assumed in this approach that if the knowledge concerning the problem to be handled (the start state), constraints to be observed (the rules applicable) during the problem solving and the goal to be reached (the end state) are available that can be represented with logic forms, then the possible solutions to the problem-and-goal (at least a path from the start state to the end state) ought to be found through the continuously use of logic inference. It is true that many problems that are complicated enough may not be described
4
and solved by using the standard mathematics. In these cases, logic expression and inference may serve as a good means to the problem solving. Currently, the logic inference approach, often in the form of expert systems, has been widely applied in many complicated problem solving. To effectively use this approach, a proper representation of the problem becomes crucial issue. Logic, graph, state space, semantic network, and frame system, etc., are good choices for this purpose. In addition to the problem representations, search strategies are another key. Breadth first search, depth first search, random search, and various kinds of heuristic search with different evaluation functions in algorithm A and A*, etc., are good examples. In most cases, the more amounts of the experience and information on the problem and constraints can be utilized, the higher efficiency the search strategy may have. It is highly expected that as the progress made in logic and related theory in the future, the logic inference approach will surely be advanced with more power. 4.3
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The Journal of CHUPT
Black box approach [10,17]
The third approach to intelligence research that can be applied to ICP is the black box, or sensor-motor systems, that i s an approach to the simulation of the behavior of intelligent systems and is thus often named the behaviorism approach to intelligence research. The behavior of a system can be defined through the use of its interrelationship between the stimuli (inputs) and responses (outputs) of the system. It is reasonably thought that the simulation of an intelligent system can well be achieved if the interrelationship between the inputs and outputs of the system in consideration has been simulated. One of the major advantages of this approach is that one may be able to simulate an intelligent system without the necessity to consider the internal structure of the system by which the structural difficulty can be avoided. This is why the approach has been named as black box method. In practice, the sensor-motor system may be regarded as a good form for implementing the black box approach. More specifically, assuming that the interrelationship between the inputs and outputs has been available, then as long as the category of the input of the system to be simulated is correctly recognized, the corresponding output can automatically be activated. Therefore, only pattern recognition technology toward the input categories of a system is needed, the simulation of such kind of intelligent systems can then be completed provided that the interrelationship between inputs and outputs had already been known previously. In the cases where uncertainty is existed, methods like
adaptation, learning, evolution and self-organizing, etc., can be regarded as modified versions of black box approach. When the environment of living beings changes, for example, they will make efforts to adapt themselves to the new environment by adjusting their own states without the need to understand the detailed structure of the new environment. What they are concerned with is the result of the adaptation. If the result of the adjusting is good to their survivals under the environment, the adaptation adopted is regarded as a success, otherwise failure. The principle is almost the same when learning, self-organizing and evolution, etc., are considered in technology. 4.4
Mechanism approach [IS, 191
The fourth approach to intelligence research that can be applied to ICT is the mechanism of intelligence formation that is an approach to the simulation of the working mechanism of intelligent systems and is thus named mechanism approach to intelligence. It was discovered recently by the author of the paper that the core mechanism of intelligence formation, for a given problem to be handled, the given constraints to be observed during the problem solving, and the given goal to be reached for the problem solving, is a series of transforms that converse the information concerning the given problem-constraints-goal to the knowledge needed for problem solving and further converse the knowledge to intelligent strategy for the problem solving. This becomes clearer if looking back to the model shown in Fig. 1. As is clearly indicated in the figure, units (1)-(3) are dealing with information acquisition, transferring and processing, unit (4) with the transformation that converses the information into knowledge, unit (5) with the transformation conversing the knowledge into intelligence (embodied in intelligent strategy), unit (6) with strategy transferring, and unit (7) with the transformation that converses the intelligent strategy into intelligent action through which the problem can be solved intelligently. Evidently, units (4) and ( 5 ) perform the core functions of intelligence formation, that is the process of knowledge refining and intelligence creation. It is worth of noticing that the methods shown in units (1)-(3) and ( 6 ) and (7) are well known already for researchers in the fields of sensing, communication, computing, and control while those in units (4) and (5) would be somewhat new to certain extent but may be more useful in dealing with complicated problems in ICT. In practice, the transformations associated with units (4) and ( 5 ) may be implemented through the use of mathematical tools if the problems faced can well be described by normal
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ZHONG Yi-xin, et al.: A survey on intelligence approaches that may be useful for.. .
mathematics. In much more cases, however, the problems may be so complicated that they cannot be represented as well as solved by employing the mathematics existed currently. For example, many kinds of uncertainty cannot be treated by classic mathematics, many kinds of complex cognitive processes carried on in human brain can hardly be described by normal mathematics, and so on. In these cases, mathematical logic, computational algorithms and even manual skills may be useful. It is clear that as more and more new progress made in new categories of mathematics, new type of logic, and new kinds of algorithm, the mechanism approach will be able to gain much more momentum in applications. This is because of the fact that most of the problems related to any complex systems can rarely be solved through only investigating its structure, function, or its behavior without deep understanding on its working mechanism. 4.5
Multl-agent approach [2,20-221
The combination of approach 4.1 with other approaches (4.2-4.4) provides a new approach that can also be well applied to ICT. The approach 4.1 recommends a framework of large scaled network with neuron as its node whereas the approaches 4.2-4.3 suggest various agents as the node of the network. The new approach is called multi-agent approach, or distributed A1 approach. The agent in the system may have different functions, from as simple as a neuron to as complex as a robot system. The example for the former category is the standard artificial neural network and the example for the latter category can be the robot soccer team within which each agent (team member) performs such functions as finding the position of the ball, understanding the configuration of the game and the rules for the game playing and making correct decision: when and how to launch an attack or take a defending? As the configuration of the game changed, the agent should be able to adjust its strategy for the purpose of wining the game. Furthermore, each of the team members should be able to coordinate itself with other team members in the game. Another new and interesting example of multi-agent system can be given as an information content security control system for Internet. Due to the huge amount of information flowed back and forth within Internet, for security control over the information, a large scaled multi-agent system must be employed. Each of the agents must be able to understand if the content of the information passed is secure or not in assigned area. In this application, the agent should have the ability to intelligently understand the natural language from syntactic to semantic and pragmatic levels. Even further, the agent in the system may be requested to extract the emotional factors from
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the natural language both in text and voice forms. To certain extent, these sorts of multi-agent systems may form an intelligent agent society within which each agent can not only have intelligence and emotion but can also work collectively. This may be of great significance for the study of large-scale of complicated systems as well as human society development in the future.
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Concludlng remarks
As one of the fundamental branches of modem technology as well as the information infrastructure of the modem society, ICT has entered in a new era of its development. One of the remarkable features of the development in the new era is that ICT should possess more and more intelligent capabilities so that it can meet the demand of better and higher requirements from the society under the more complicated and severer environmental constraints. The traditional concepts, ideas and approaches to ICT research, having made great contributions to the development though, may not be sufficient to support the new requirements from the society. It would be a good idea to update the traditional methodologies with something new. This is the motivation of the paper to provide a brief survey on intelligence approaches for ICT research.
References 1 . Zhong Yi-xin. Principles of Information Science. Third Edition. Beijing: BUPT Press, 2002 (in Chinese) 2. Russell S J, Norvig P. Artificial Intelligence: A Modern Approach. Second Edition. Beijing: Pearson Education Asia Limited and Tsinghua University Press, 2006 3. Cowan J D, Sharp D H. Neural Nets. Quarterly Reviews of Biophysics, 1988,21: 365-427 4. Cowan J D, Sharp D H. Neural nets and artificial intelligence. Daedalus, 1988, 117: 85-121 5. McCulloch W S, Pitts W H. A logical calculus of the ideas imminent in nervous activity. Bull Math Biophy. 1943, 5: 115-135 6. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psych Rev, 1958, 65: 386408 7. Hinton G E, Anderson J A. Parallel Models of Associative Memory. Lawrence Erlbaum Associates, Potomac, Maryland. 1981 8. Hopfield J J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of Nat Acad of Sci, 1982.79: 2554-2558 9. Rumelhart D E, McClelland J L. Parallel Distributed Processing. MIT Press, Cambridge, Massachusetts. 1986 (Eds.)
To p. 17
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ZENG Xian, et al.: Genetic algorithm for autonomic joint radio resource management in...
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Biographies: ZENG Xian, master Candidate of Beijing University of Posts and Telecommunications, interested in the research on heterogeneous radio resource management and reconfigurability.
LIN Yue-wei, doctor Candidate of Beijing University of Posts and Telecommunications, interested in the research on heterogeneous radio resource management and reconfigurability .
MA Tao, master Candidate of Beijing University of Posts and Telecommunications, interested in the research on heterogeneous radio resource management and reconfigurability.
FENG Zhi-yong, Ph. D., associate professor of Beijing University of Posts and Telecommunications, interested in the research on mobile communication.
~~
From p. 5
Theory of Information-Knowledge-IntelligenceTransform. Beijing:
10. Newell A, Simon H A. Human problem solving. Englewood
Science Press. 2007 (in Chinese) 19. Zhong Yi-xin. Cognitive approach to artificial and natural intel-
Cliffs, NJ: Prentice-Hall, 1972 11. Simon H A. The Sciences of Artificial. Cambridge, MA: The
ligence. International Journal on Cognitive Informatics and Natural Intelligence. 2007, 2( 1): 1-16
MIT Press, 1969 12. Webber B L, Nilsson N J. Readings in Artificial Intelligence.
20. Nilsson N. Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publisher, 1998
Morgan Kaufmann, San Mateo, California. 1981 (Eds.) 13. Cohen P R. Empirical Methods for Artificial Intelligence. MIT
21. Jennings N R. Agent-based computing promise and perils. UCAI-99, 1999, 1429-1436
Press, Cambridge, Massachusetts. 1995 14. Nilsson N J. Principles of Artificial Intelligence. Springer-Verlag,
22. Chaib-draa B, et al. Trends in distributed artificial intelligence. Artificial Intelligence Review, 1992,6: 35-66
Berlin, 1982 15. Feigenbaum E A, Feldman J. Computers and thought.
Biography: ZHONG Yi-xin, professor and chair
McGraw-Hill, 1963 (Eds.) 16. Brooks R. Intelligence without representation.
Artificial
Intelligence, 1991,47: 139-159 17. Brooks R. Intelligence without Reasoning, in Proceedings of IJCAI’91 Sydney, 1991 18. Zhong Yi-xin. Principles of Cognetics in Machine: A Unified
of Academic Committee of Beijing University of Posts and Telecommunications, has long been devoted himself in the studies of information theory and intelligence science.
ZHONG Yi-xin
A survey on intelligence approaches that may be useful for information and communication technology research CLC number TN919
Document A
Article ID
1005-8885 (2008) 01-0001-05
Abstract The concepts on information and communication technology (ICT) and “intelligence” are defined firstly and the analyses on the environment and requirements for ICT are then followed. Based on the definitions and the analyses, a survey on intelligence approaches that may be useful for ICT is thus made. The conclusion drawn from the survey is a recommendation by saying that intelligence approaches have been one of the keys for further development of the entirety of ICT and therefore should receive much more attentions from ICT researchers in the years to come.
types of uncertainty can no longer be ignored. Moreover, the world has been confronted with more and more complicated systems and complicated problems. Hence, new methodologies and paradigms in ICT research are unavoidably needed which must be able to intelligently cope with such kinds of issues as uncertainty, complexity, convergence, unification and the like. This convinces why a survey on intelligence approaches to the research for ICT is necessary.
Keywords uncertainty, complexity, intelligence approaches, ICT
As is well known presently, ICT stands for information and communication technology in which communication technology has a unique definition as the one that performs the function of information transferring whereas the terminology of information technology (IT) has ever been quite confused. As a matter of fact, IT in literature may either be referred as to computer technology alone, or the mixture of computer and communication technology, or to the collection of computer, communication, and control technology, and so forth. In other words, although the term of ICT has been very widely used, its definition has never been made clear enough. To make it clear, the definitions of ICT and intelligence will be given in what follows and some notes and discussions related to the definitions will be made thereafter. Definition 1 A general conception of ICT is defined as a set of technologies that can successfully be used for dealing with all kinds of information processes. Note 1 The term of “all kinds of information processes” in the definition should not only include such kinds of processes as information acquisition (i.e., sensing), information transferring (communication), and information manipulation (computation), but should also include the processes of information cognition (knowledge producing), infonnation regeneration (strategy creation) and information execution (control) since knowledge is the products of information cognition and strategy is the products of knowledge activation, a kind of higher level information. It is clear that the definition of ICT given above is rather the same as the one of IT. The adoption of the term of ICT, compared
1 lntroductlon Due to the old tradition of divide and conquer methodology in science and technology, the studies on communication and information technology, the latter of which was frequently referred as to the computer technology, have long been separated from each other. This has brought to the ICT research rather serious problems. One of such problems is the different paradigms being adopted in the centralized telecommunication network and distributed Internet. Moreover, due to the old tradition of deterministic point of view in modem science and technology, the studies on information technology, particularly in the studies of computer technology, have paid little attention to the uncertain phenomena than that it should have. This ignorance of uncertain factors has also brought to the practice many serious problems because of the fact that the uncertain phenomena exist pervasively. Technology today is facing a new situation where the centralized telecommunication network and distributed Internet technology have gradually to converge and the fact of various Received date: 2008-01-05
W O N G Yi-xin (m)
School of Information Engineering, Beijing University of Posts and Telecommunications. Beijing 100876,China E-mail:[email protected]
2 DdntWons on ICT and Intdllgence [I]
2
The Journal of CHUFT
with IT, is just to emphasize the role that communication technology plays. Definition 2 When a problem, a group of constraints related to the problem, and the prescribed goal for the problem handling are given, the intelligence is defined as a set of abilities for solving the problem, meeting the constraints and reaching the goal. Note 2 The term of “a set of abilities” in the definition should include the ones of (1) acquiring all the information concerning the given problem, the given constraints, and the given goal; (2) transferring the acquired information to an assigned position; (3) manipulating the information to the one easy to use; (4) refining the information into knowledge (cognition) and associating the newly refined knowledge with the related ones already existed in knowledge base; (5) creating the strategy for solving the problem based on the knowledge on one hand and guided by the goal on the other hand; and (6) transferring the strategy created to another point; (7) conversing the created strategy into the corresponding action through which the strategy is executed. These abilities and their relations can clearly be shown in Fig. 1
lnformation (3) nianipulation
Information cognition
lnformation (2) transferring
Knowledge
lnformation
lnformation
Fig. 1 Model of basic intelligence process
It is worth of mentioning that both the process of defining the problem associated with the constraints to be handled and that of defining the prescribed goal for the problem solving are termed the implicit intelligence whereas the processes (1)-(7) in Fig. 1 are termed the explicit intelligence. Till the present time, the Implicit Intelligence is still full with mysteriousness and is difficult to cope and may remain the same at least for the near future. The explicit intelligence, on the other hand, is much more tangible, attractive, and challengeable though. In fact, there has already been a discipline in scientific research named artificial intelligence (AI) that has been devoted to the understanding and implementation of the explicit intelligence. It is interesting to note from Fig. 1 that the model of intelligence process defined in definition 2 is very much the similar to the model of ICT defined in definition 1 as listed below: Unit (1) in Fig. 1 corresponds to sensing technology in ICT;
2008
Unit (2) and (6) are similar to communication technology; Unit (3) has the same function as computation technology; Unit (4) and (5) are equivalent to artificial intelligence; Unit (7) has a counterpart with control technology. The similarity between the model of ICT and that of intelligence process indicates that ICT should be able to perform its functions in such a way that an intelligent system can do. In other words, in addition to artificial intelligence in ICT, it is possible and also reasonable to make the other components in ICT to be intelligent units: intelligent sensing, intelligent communication, intelligent computation, and intelligent control technology, and after all, an entirety of intelligent ICT. This analysis paves a feasible way for ICT to adopt the intelligence approaches.
As time going on, both the environment and requirements
for ICT today and in the future are undergoing historical transition. The great changes mainly caused by the transition to a new era can briefly be summarized at least as the following aspects. First, people nowadays are facing more and more complicated challenges. Economic globalization, globally scaled information networks, worldwide cooperated manufacturing, internationally connected transportation, climate change, and large-scale natural disasters, etc., are some of the typical examples. The dramatic changes in ICT environment may include the following aspects: the states of the environment may be from fully observable to partially observable, the properties of the object may be from deterministic to stochastic, the period for working may be from episodic to sequential, the nature of the system may be from static to dynamic, the feature of the variable may be from binary to many valued, the characteristic of the system may be from linear to nonlinear, and the number of the units and the span of the systems may be from small scale to large scale, and even more. Second, for successfully dealing with these grand Challenges, it is demanded that all the components of ICT must work together harmoniously as an entirety so that the problems can be solved better and quicker. “All the components of ICT work as an entirety” is rather different from what it was in the past: sensing, communication, computation and control as well as intelligence technology, all the components of ICT, have long been working separately. Yet the “entirety of ICT” should exactly be the same thing as a large scaled intelligent system as mentioned previously. Third, for successfully dealing with these grand challenges,
No. 1
ZHONG Yi-xin, et al.: A survey on intelligence approaches that may be useful for.. .
it is also demanded that the performances of “the entirety of ICT” must be as smartly as that an intelligent system can provide. In other words, the entirety of ICT must work smartly regardless of any kinds of complexity that the given problems may have, any kinds of uncertainty that the environment and constraints may possess, and any kinds of changes in constraints and prescribed goal that may happen during the working period. It may also be necessary to mention that the typical characteristics of the solutions for most of the complex problems would be kinds of multi-object and multi-criteria optimization under uncertain and dynamic environment. Summarily, in response to the new environmental conditions and new requirements for ICT described above, a reasonable advice is to take necessary steps for researchers in ICT fields to initiate the studies of the intelligence theory and technology and employ the results in their own fields.
4 Intelllgenm approaches useful for ICT There have been plenty of results achieved in intelligence research as it has at least over six decades history of its development. From very sophisticated analysis for large scaled and nonlinear dynamic and distributed complex systems to very straightforward search for finding optimal solution in certain cases, there are numerous choices: learning, understanding, logic reasoning, problem solving, game-playing, schedule planning, heuristic search, adaptation, diagnosis, selforganizing, and self-repairing, etc. It would, nonetheless, be impossible to list all the results and methods in such a limited space. An acceptable way for quickly doing is to give a brief introduction 4.1 Neural networks approach [3-91
The first approach to intelligence research that can be applied to ICT is the neural network that is an approach to the simulation of the structure of biological neural networks of human brain and is therefore termed structuralism approach to intelligence research, sometimes. As is well known from the brain science, neuroscience, and cognitive science disciplines, biological neural network (BNN) is a kind of very large scaled, and densely connected, massively parallel processing structure with both non-linear and dynamic properties where there are approximately 10” or 10” neurons working simultaneously each of which is a quite simple, but a kind of nonlinear, processing element and each of which is connected to as many as 103-4other neurons. It has been proved in brain science, neurological science and cognitive science that most of the high level cognitive performances are supported basically by BNN lied over the
3
cerebral cortex in human brain. Artificial neural network (ANN) is a sort of simulated version of BNN with much less number of neurons and much loosely connection matrix and simpler learning algorithm and therefore much lower complexity than that in ANN due to the practical limitation from the present industrial manufacturing capabilities. This leads to the limitation of the performances of ANN compared with BNN in human brain. However, ANN in today’s practice still possesses rather extraordinary power in signal processing applications that cannot well match with that of BNN of human brain though. A variety of ANN models can be found in literature among which multi-layer perceptron (MLP) with BP learning algorithm and Hopfield network with gradient descent learning algorithm are most popular. The former is a feed-forward ANN model and the latter is a feedback model. Both of the models have found many successful applications in pattern recognition, fault diagnosis, associative memory, optimization problem solver, and other kinds of signal processing. Since 1990s, ANN research has been stepped into a new stage, the computational intelligence stage, by integrating itself with fuzzy logic and genetic algorithm. It is widely believed that as the progresses that will be made in theoretical research of ANN and in industrial implementation in the future, both the applicability and the performance of ANN will all be improved greatly. 4.2
Loglc inference approach [1&15]
The second approach to intelligence research that can be applied to ICT is the logic inference that is an approach to the simulation of the function of logic thinking in human brain and is sometimes named functionalism approach to the field of intelligence research. Logic thinking carried on in human brain is commonly regarded as one of the most powerful capabilities for human beings. The successful simulation of logic thinking in human brain is of course attractive and meaningful. The most typical feature of logic thinking can be expressed as a series of logic operations on knowledge such as knowledge acquisition, knowledge representation and storage, knowledge inference and goal match, etc. It is assumed in this approach that if the knowledge concerning the problem to be handled (the start state), constraints to be observed (the rules applicable) during the problem solving and the goal to be reached (the end state) are available that can be represented with logic forms, then the possible solutions to the problem-and-goal (at least a path from the start state to the end state) ought to be found through the continuously use of logic inference. It is true that many problems that are complicated enough may not be described
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and solved by using the standard mathematics. In these cases, logic expression and inference may serve as a good means to the problem solving. Currently, the logic inference approach, often in the form of expert systems, has been widely applied in many complicated problem solving. To effectively use this approach, a proper representation of the problem becomes crucial issue. Logic, graph, state space, semantic network, and frame system, etc., are good choices for this purpose. In addition to the problem representations, search strategies are another key. Breadth first search, depth first search, random search, and various kinds of heuristic search with different evaluation functions in algorithm A and A*, etc., are good examples. In most cases, the more amounts of the experience and information on the problem and constraints can be utilized, the higher efficiency the search strategy may have. It is highly expected that as the progress made in logic and related theory in the future, the logic inference approach will surely be advanced with more power. 4.3
2008
The Journal of CHUPT
Black box approach [10,17]
The third approach to intelligence research that can be applied to ICP is the black box, or sensor-motor systems, that i s an approach to the simulation of the behavior of intelligent systems and is thus often named the behaviorism approach to intelligence research. The behavior of a system can be defined through the use of its interrelationship between the stimuli (inputs) and responses (outputs) of the system. It is reasonably thought that the simulation of an intelligent system can well be achieved if the interrelationship between the inputs and outputs of the system in consideration has been simulated. One of the major advantages of this approach is that one may be able to simulate an intelligent system without the necessity to consider the internal structure of the system by which the structural difficulty can be avoided. This is why the approach has been named as black box method. In practice, the sensor-motor system may be regarded as a good form for implementing the black box approach. More specifically, assuming that the interrelationship between the inputs and outputs has been available, then as long as the category of the input of the system to be simulated is correctly recognized, the corresponding output can automatically be activated. Therefore, only pattern recognition technology toward the input categories of a system is needed, the simulation of such kind of intelligent systems can then be completed provided that the interrelationship between inputs and outputs had already been known previously. In the cases where uncertainty is existed, methods like
adaptation, learning, evolution and self-organizing, etc., can be regarded as modified versions of black box approach. When the environment of living beings changes, for example, they will make efforts to adapt themselves to the new environment by adjusting their own states without the need to understand the detailed structure of the new environment. What they are concerned with is the result of the adaptation. If the result of the adjusting is good to their survivals under the environment, the adaptation adopted is regarded as a success, otherwise failure. The principle is almost the same when learning, self-organizing and evolution, etc., are considered in technology. 4.4
Mechanism approach [IS, 191
The fourth approach to intelligence research that can be applied to ICT is the mechanism of intelligence formation that is an approach to the simulation of the working mechanism of intelligent systems and is thus named mechanism approach to intelligence. It was discovered recently by the author of the paper that the core mechanism of intelligence formation, for a given problem to be handled, the given constraints to be observed during the problem solving, and the given goal to be reached for the problem solving, is a series of transforms that converse the information concerning the given problem-constraints-goal to the knowledge needed for problem solving and further converse the knowledge to intelligent strategy for the problem solving. This becomes clearer if looking back to the model shown in Fig. 1. As is clearly indicated in the figure, units (1)-(3) are dealing with information acquisition, transferring and processing, unit (4) with the transformation that converses the information into knowledge, unit (5) with the transformation conversing the knowledge into intelligence (embodied in intelligent strategy), unit (6) with strategy transferring, and unit (7) with the transformation that converses the intelligent strategy into intelligent action through which the problem can be solved intelligently. Evidently, units (4) and ( 5 ) perform the core functions of intelligence formation, that is the process of knowledge refining and intelligence creation. It is worth of noticing that the methods shown in units (1)-(3) and ( 6 ) and (7) are well known already for researchers in the fields of sensing, communication, computing, and control while those in units (4) and (5) would be somewhat new to certain extent but may be more useful in dealing with complicated problems in ICT. In practice, the transformations associated with units (4) and ( 5 ) may be implemented through the use of mathematical tools if the problems faced can well be described by normal
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ZHONG Yi-xin, et al.: A survey on intelligence approaches that may be useful for.. .
mathematics. In much more cases, however, the problems may be so complicated that they cannot be represented as well as solved by employing the mathematics existed currently. For example, many kinds of uncertainty cannot be treated by classic mathematics, many kinds of complex cognitive processes carried on in human brain can hardly be described by normal mathematics, and so on. In these cases, mathematical logic, computational algorithms and even manual skills may be useful. It is clear that as more and more new progress made in new categories of mathematics, new type of logic, and new kinds of algorithm, the mechanism approach will be able to gain much more momentum in applications. This is because of the fact that most of the problems related to any complex systems can rarely be solved through only investigating its structure, function, or its behavior without deep understanding on its working mechanism. 4.5
Multl-agent approach [2,20-221
The combination of approach 4.1 with other approaches (4.2-4.4) provides a new approach that can also be well applied to ICT. The approach 4.1 recommends a framework of large scaled network with neuron as its node whereas the approaches 4.2-4.3 suggest various agents as the node of the network. The new approach is called multi-agent approach, or distributed A1 approach. The agent in the system may have different functions, from as simple as a neuron to as complex as a robot system. The example for the former category is the standard artificial neural network and the example for the latter category can be the robot soccer team within which each agent (team member) performs such functions as finding the position of the ball, understanding the configuration of the game and the rules for the game playing and making correct decision: when and how to launch an attack or take a defending? As the configuration of the game changed, the agent should be able to adjust its strategy for the purpose of wining the game. Furthermore, each of the team members should be able to coordinate itself with other team members in the game. Another new and interesting example of multi-agent system can be given as an information content security control system for Internet. Due to the huge amount of information flowed back and forth within Internet, for security control over the information, a large scaled multi-agent system must be employed. Each of the agents must be able to understand if the content of the information passed is secure or not in assigned area. In this application, the agent should have the ability to intelligently understand the natural language from syntactic to semantic and pragmatic levels. Even further, the agent in the system may be requested to extract the emotional factors from
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the natural language both in text and voice forms. To certain extent, these sorts of multi-agent systems may form an intelligent agent society within which each agent can not only have intelligence and emotion but can also work collectively. This may be of great significance for the study of large-scale of complicated systems as well as human society development in the future.
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Concludlng remarks
As one of the fundamental branches of modem technology as well as the information infrastructure of the modem society, ICT has entered in a new era of its development. One of the remarkable features of the development in the new era is that ICT should possess more and more intelligent capabilities so that it can meet the demand of better and higher requirements from the society under the more complicated and severer environmental constraints. The traditional concepts, ideas and approaches to ICT research, having made great contributions to the development though, may not be sufficient to support the new requirements from the society. It would be a good idea to update the traditional methodologies with something new. This is the motivation of the paper to provide a brief survey on intelligence approaches for ICT research.
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ZENG Xian, et al.: Genetic algorithm for autonomic joint radio resource management in...
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Biographies: ZENG Xian, master Candidate of Beijing University of Posts and Telecommunications, interested in the research on heterogeneous radio resource management and reconfigurability.
LIN Yue-wei, doctor Candidate of Beijing University of Posts and Telecommunications, interested in the research on heterogeneous radio resource management and reconfigurability .
MA Tao, master Candidate of Beijing University of Posts and Telecommunications, interested in the research on heterogeneous radio resource management and reconfigurability.
FENG Zhi-yong, Ph. D., associate professor of Beijing University of Posts and Telecommunications, interested in the research on mobile communication.
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