ICT619 Intelligent Systems
Topic 1: Introduction to Intelligent Systems: background and overview
What is an intelligent system?
There is considerable ongoing debate among scientists about the meaning of the word intelligence,
although most of us would agree on some common traits regarded as manifestations of intelligence.
The most important attributes of human-like intelligence capabilities are:
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Logical Inferencing and Deduction
Contextual Domain Knowledge
Pattern Recognition
Learning and Adaptivity
In this study, we will deal with man-made systems that are regarded as intelligent. The intelligence
possessed by these systems is referred to as machine intelligence. The question asked is whether the
display of what one would regard as intelligent behaviour makes a machine intelligent. Some argue that
a truly intelligent system should be capable of adapting itself to solve problems in a range of
environments. A case in point is IBM’s Deep Blue, which was deemed (mainly by the media) to be
highly “intelligent” as it defeated a world chess champion, but unlike humans, it exhibited no capacity
for learning by itself.
Most of today’s intelligent information systems would fail to meet this criterion of automatic learning
or self-adaptation. For our purpose, we’ll adopt a somewhat looser definition of an intelligent system as
one that solves problems by following processes that are similar in certain ways to problem solving by
humans.
Intelligent systems in the context of business application are smart information systems that utilise one
or more intelligence tools mentioned below to aid decision making (known as decision support systems
or DSS). These systems can analyse problems to extract new information for providing insights,
recognising trends, and making predictions.
Rapid progress in computer hardware and software, and information technology infrastructure over the
last two decades in particular means we are now able to access and process information at speeds
which enable many of the intelligence tools to be useful and practical. Intelligent techniques and
systems are now poised to become more and more commonplace in all aspects of life including the
business environment.
Significance of intelligent systems in business
Intelligent systems are proving vital in providing knowledge – the so-called “business intelligence” crucial for business success. Examples of such intelligence are information about customer behaviour,
market trends, efficiency bottlenecks etc, which can make it possible to
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Increase productivity through more efficient allocation of resources
Obtain competitive advantage by making appropriate strategic decisions.
Some examples of successful intelligent systems applications in business are:
 customer service
 scheduling
 data mining
 financial market prediction
 quality control
 consumer product marketing
Intelligent Systems are assuming greater significance at all levels of management from
the CEO to the operational manager. This trend is likely to continue as more traditional methods of
problem solving and data analysis are superseded by newly emerging intelligent methodologies,
particularly for unstructured problems like optimisation, for which algorithmic solutions are difficult if
not impossible. Knowledge of available intelligent systems products is becoming an important part of
the know-how for people with managerial and decision making responsibilities. According to one
estimate, investment in AI for customer service systems could increase from $100 million in 2001 to $1
billion 2005.
Characteristics of intelligent systems
Intelligent systems possess one or more of these characteristics
 Capability to extract and store knowledge
 Human like reasoning process
 Learning from experience (or training)
 Dealing with imprecise expressions of facts
 Finding solutions through processes similar to natural evolution
We’ll find how these characteristics are embodied in intelligent systems through the use of a number of
methodologies outlined below.
There is also a growing trend for intelligent systems to exhibit human-like communication abilities
through natural language understanding, speech recognition and synthesis, and image analysis.
Most intelligent systems in use today are based either on the rule based methodology from the field of
Artificial Intelligence(AI) known popularly as expert systems, or on one or more of the methodologies
belonging to the more recently emerging field known as Soft Computing (SC).
The field of Artificial Intelligence (AI)
Carnegie Mellon professor Tom Mitchell described AI as "inventing machines that will help people in
a variety of ways by giving machines some of the sophisticated capabilities that humans have, such as
the ability to understand spoken words or interpret images, or to learn from experience."
There appears to be no single universally accepted definition of artificial intelligence (Fogel 2000). It is
nevertheless safe to say that the field of AI is primarily concerned with the goal of developing software
aimed at enabling machines to display intelligence by solving problems using reasoning similar to
humans.
AI attempts to achieve the goal of mimicking human intelligence by building a model of the way
knowledge is represented and processed by the human mind. Naturally, it is concerned with the study
of the way we reason to solve problems, and at a different level, the way our brain functions. AI has
drawn researchers not just from computer science and information systems but also from fields such as
psychology and neurology, who endeavour to understand the structure and functions of the brain.
AI has been in existence as a discipline since the sixties, but it has failed to live up to the excitement
and expectation it generated initially. Its progress has been limited by a lack of our understanding of
the problem solving process taking place in the human mind, and the complex nature of the problems it
has tried to solve. One of the more successful products of AI research is a knowledge-based system for
problem solving, known as an expert system. Knowledge in expert systems is usually encoded in the
form of rules.
The Soft Computing paradigm
Also known as Computational Intelligence, soft computing differs from conventional computing in
that, techniques belonging to it can be tolerant of imprecise, incomplete or corrupt input data. Some of
them can solve problems without requiring the solution steps or reasoning process to be explicitly
stated. Some soft computing systems develop the capability to solve problems through repeated
observation and adaptation. Some arrive at a solution through a process similar to evolution in nature.
In more than one ways, the human mind is the role model for soft computing techniques - for example,
the ability to solve problems expressed in vague terms, or solving problems without making use of
explicit solution steps. Arriving at a solution through an evolutionary process is commonplace in
nature.
The predominant SC methodologies found in current intelligent systems are:
 Artificial Neural Networks (ANN)
 Fuzzy Systems
 Genetic Algorithms (GA)
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An Overview of Intelligent System Methodologies
Expert Systems
Expert systems are designed to solve problems in a specific and often quite narrow domain, eg, a
system to diagnose a particular type of disease, or to assist foreign currency traders in deciding when to
buy or sell.
Expert systems are built by interrogating human experts about their knowledge in the relevant problem
domain. Their expertise is stored in a form suitable for solving problems in that domain by following a
reasoning strategy. The most common form of the expert system knowledge base is a collection of IF
… THEN … rules.
Expert systems take a top-down approach in the sense that they attempt to mimic the problem solving
strategy followed by human experts and work their way to a solution using data specific to a problem
instance.
Some of the areas expert systems have found applications in are:
 banking and finance
 manufacturing
 retail
 personnel management
 emergency services
 law
 biotechnology
 media
 music
Artificial Neural Networks (ANN)
Unlike expert systems, ANNs follow a bottom-up approach. They try to mimic the way the basic
processing elements in the human brain, known as neurons, are thought to operate. The highly
interconnected neurons function as a massively parallel system of relatively simple processing
elements.
ANNs learn from experience gained usually through being presented with data representing examples,
with or without human intervention. The problem solving strategy remains implicit and unknown to the
user. This makes them particularly suitable for problems for which solution strategies or algorithms are
hard to come by. As we’ll find later, decision support systems usually face problems of this nature.
Use of ANNs in industry and business have been growing since the late eighties with a major
breakthrough in training algorithm. There are a number of different models of ANNs depending on the
architecture, learning method and other operational characteristics.
ANNs are particularly good at pattern recognition and classification problems. One major strength of
ANNs are their ability to capture the essential characteristics of data used as examples during training,
and utilise this to handle previously unseen, incomplete or corrupted data.
Some of the diverse applications of ANNs are:
 explosive detection at airports
 character and signature recognition
 financial risk assessment
 optimisation
 scheduling
Genetic Algorithms (GA)
Genetic algorithms, which are part of a broader field known as evolutionary computation, attempt to
come up with optimal solutions through a process similar to evolution. It follows the principles of
survival of the fittest, crossbreeding and mutation to generate better solutions.
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A population of candidate solutions is initialised usually in the form of binary strings (the
chromosomes). A new generation of solutions is produced from the current population using specific
genetic operations of crossover (splicing and joining two chromosomes) and bit mutation. The fitness
of each solution generated is evaluated using a fitness function. The steps of solution generation and
evaluation continue until an acceptable solution is found.
GAs have been used in
 portfolio optimisation
 bankruptcy prediction
 financial forecasting
 fraud detection
 scheduling
Fuzzy Systems
Traditional logic is bivalent or two-valued – any proposition is either true or false, and unlike real-life
situations, there is no room in it for partially true or partially false propositions. Fuzzy logic, introduced
in the 60s, attempts to deal with reality by assigning degrees of truth to facts.
Fuzzy systems are characterized by their ability to handle imprecise information to generate solutions.
They allow us to express knowledge in vague linguistic terms relieving us of the burden of finding
exact numerical values – a task, which if not impossible, can result in inaccuracies.
Initially treated with scepticism, the flexibility and power of fuzzy systems is now well recognised.
One major application of fuzzy systems has been in controlling manufacturing processes and various
appliances such as air conditioners and video cameras. Increasingly fuzzy logic is being combined with
other intelligent system methodologies to develop hybrid fuzzy-expert, neuro-fuzzy, or fuzzy-GA
systems.
Some business applications of fuzzy systems:
 Company acquisition and credit analysis
 Credit authorization
 Criminal identification system
 Project management
 Integrated MRP (materials-requirement planning system) and production scheduler
 Loan evaluation advisor
Case-based reasoning (CBR)
Intelligent systems, which use CBR, attempt to solve problems by making use of knowledge about
similar problems encountered by the system in the past. The knowledge used is built up as a case-base.
Unlike rule-based expert systems, case-based systems can improve over time by learning from
mistakes made with past problems.
To solve a problem, A CBR system searches its case base for cases with attributes similar to those of
the given problem. It then creates a solution by putting together similar cases and adjusting the solution
by taking into account the differences between the given problem and these cases.
Application examples:
 Utilisation of shop floor expertise in aircraft repairs
 Legal reasoning
 Dispute mediation
 Data mining
 Fault diagnosis
 Scheduling
For a comprehensive list of CBR applications see: http://www.ai-cbr.org/biblio/Applied%20CBR.html
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Data mining
The term data mining stands for the process of, rather than a product for, exploring and analysing data
for discovering new and useful information. The last four decades has seen an abundance of data
generated or captured electronically – eg, data generated by point-of-sale (POS) devices such as bar
code scanners, customer call-detail databases, web-log files in e-commerce etc.
Organizations are ending up with huge amounts of mostly day-to-day transaction data, which are being
stored away. Data is being collected mostly for improving efficiency of underlying operations, but not
for analysis and prediction. It has become obvious that businesses can gain competitive advantage if it
is possible to extract useful information on market and customer behaviour by “mine”-ing the data.
Such information may indicate important underlying trends, associations or patterns in market
behaviour, which can help obtain answers to questions like – “Based on past buying behaviour, which
customers should be targeted for direct marketing?”
The advent of intelligent techniques such as artificial neural networks and decision trees has made it
possible to perform data mining involving large volumes of data more effectively and efficiently.
Current interest in data mining, in particular for gaining competitive advantage in business, is growing.
Its application in areas such direct target marketing campaigns, fraud detection, and development of
models to aid in financial predictions is likely to intensify in the coming years.
Intelligent Software Agents (ISA)
These are computer programs that provide active assistance to information system users to help them
cope with information overload. They act in many ways like a personal assistant to the user by
attempting to adapt to the specific needs of the user. They are capable of learning from the user as well
as other intelligent software agents.
Some reported application examples of ISAs are in:
 Data Collection and Filtering
 Pattern Recognition
 Event Notification
 Data Presentation
 Planning and Optimization
 Rapid Response Implementation
Language Technology (LT)
Communication between people and computers is an important aspect of any intelligent information
system. The ultimate goal is for the user to be able to use a language like English to explain the task to
be carried out, and for the computer to perform the task, and if required, respond back in that language.
Explanation of the task to be performed may be in the form of text (typed, printed or handwritten) or
spoken words. For a computer to send back information or queries to a human, instead of words
appearing on a screen, synthesised speech with a human-like voice may be desirable in certain
situations. An automated telephone helpline, such as a flight information system is such an example.
Natural Language Processing (NLP) or natural language understanding grew initially as a sub-domain
of AI concerned with the task of developing software capable of “understanding” a natural language in
order to achieve specific goals (Thayse 1991). Understanding natural languages, although trivial for
humans, is a challenging task for computers. This is due to the inherent ambiguities as well as frequent
use of context in verbal and written communication. An NLP system can be the front-end of
information systems based on other intelligence tools to relieve the user of the task of understanding
the complexities of a computer language.
Recently a new broadly-based area called language technology has been defined. It encompasses all
aspects of human-computer communication involving written and spoken text. It has been defined as
the - “application of knowledge about human language in computer-based solutions” (Dale 2004).
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Applications of LT include:
 Natural Language Processing (NLP)
 Speech recognition
 Optical character recognition (OCR)
 Handwriting recognition
 Machine translation
 Text summarisation
 Speech synthesis
A LT-based system can be the front-end of information systems themselves based on other intelligence
tools.
RESOURCES
Books
 Barr, A., & Feigenbaum, E.A., The Handbook of Artificial Intellignce, Vol.4, Reading MA,
Addison- Wesley, 1989 – Expert systems.
 Dell, R. Language Technology: Applications and Techniques Tutorial 2004, 8th Pacific Rim
International Conference on AI, Auckland, 9-13 August 2004.
 Dhar, V., & Stein, R., Seven Methods for Transforming Corporate Data into Business
Intelligence., Prentice Hall 1997
 Fogel, D.B., Evolutionary Computation, IEEE Press 2000.
 Holland, J., “Genetic algorithms”, Scientific American, July 1992, pp.66-72.
 Mitchell, T.M., Machine Learning,McGraw-Hill 1997
 Sangalli, A., The Importance of Being Fuzzy, Princeton University Press, 1998.
 Thayse, A. (Editor), From Natural Language Processing to Logic for Expert Systems, John
Wiley & Sons, 1991.
 Waterman, D.A.,A Guide to Expert Systems, Reading MA, Addison-Wesley, 1986 – Expert
systems
 Winston, P. "Learning by Building Identification Trees", in P. Winston, Artificial Intelligence,
Addison-Wesley, 1992.
Journals
 IEEE expert : intelligent systems and their applications, New York, N.Y : IEEE Computer
Society, 1990-1997
 IEEE intelligent systems & their applications, IEEE Computer Society, Los Alamitos, CA,
1998 International Journal of Intelligent Systems Online, Access through Murdoch University
Library
 AI Expert
Useful web sites (URL - topic)
 http://www.newsfactor.com/perl/story/16430.html - News story on some AI applications.
 http://library.gsfc.nasa.gov/SubjectGuides/SoftComp.htm - Soft computing
 http://www.helsinki.fi/~niskanen/sc2000.html - Course on soft computing
 http://www.soft-computing.de/def.html - Soft computing
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http://www.alumni.caltech.edu/~croft/research/agent/definition/ - Intelligent software agents
http://lcs.www.media.mit.edu/groups/agents/resources/ - Intelligent software agents
http://pattie.www.media.mit.edu/people/pattie/CACM-94/CACM-94.p7.html - Intelligent
software agents
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http://www.ai-cbr.org/biblio/Applied%20CBR.html - Case-based reasoning
http://www.ai-cbr.org/library.html- Case-based reasoning
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http://www.emsl.pnl.gov:2080/proj/neuron/neural/systems/shareware.html
- Lists shareware and freeware software tools for intelligent systems.
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http://www.alta.asn.au/ - Australian Language Technology Association Web site.
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