Chapter 13
ADVANCED
INTELLIGENT
SYSTEMS
Learning Objectives
• Understand machine-learning concepts
• Learn the concepts and applications of
case-based systems
• Understand the concepts and applications
of genetic algorithms
• Understand fuzzy set theories and their
applications in designing intelligent systems
Learning Objectives
• Understand the concepts and applications
of natural language processing (NLP)
• Learn the concepts, advantages, and
limitations of voice technologies
• Learn about integrated intelligent support
systems
Machine-Learning Techniques
• Machine-learning concepts and
definitions
– Machine learning
The process by which a computer learns
from experience (e.g., using programs that
can learn from historical cases)
Machine-Learning Techniques
– Human learning is a combination of many
complicated cognitive processes including:
• Induction (learning by example)
• Deduction (specific inferences based on
generalities)
• Analogy (transference)
• Other special procedures related to observing or
analyzing examples
Machine-Learning Techniques
– How learning relates to intelligent systems
• Learning systems demonstrate interesting learning
behaviors
• AI is not able to learn as well as humans or in the
same way that humans
• Machine learning cannot be applied in a creative
way, although such systems can handle cases to
which they have never been exposed
• It is not clear why learning systems succeed or fail
• A common thread running through most AI
approaches to learning is the manipulation of
symbols rather than numeric information
Machine-Learning Techniques
• Machine-learning methods
– Supervised learning
A method of training artificial neural networks
in which sample cases are shown to the
network as input and the weights are
adjusted to minimize the error in its outputs
– Unsupervised learning
A method of training artificial neural networks
in which only input stimuli are shown to the
network, which is self-organizing
Machine-Learning Techniques
Machine-Learning Techniques
Machine-learning methods and algorithms
• Inductive learning
• Case-based
reasoning
• Neural computing
• Genetic algorithms
• Natural language
processing (NLP)
• Cluster analysis
• Statistical methods
• Explanation-based
learning
A machine learning
approach that
assumes that there is
enough existing
theory to rationalize
why one instance is
or is not a
prototypical member
of a class
Case-Based Reasoning (CBR)
• Case-based reasoning (CBR)
A methodology in which knowledge
and/or inferences are derived from
historical cases
Case-Based Reasoning (CBR)
– Analogical reasoning
Determining the outcome of a problem with the use
of analogies. A procedure for drawing conclusions
about a problem by using past experience
Suppose, for example, that I am thinking about buying a new car. I'm
very likely to speak with other people who have recently bought new
cars, noting their experiences with various makes, models, and
dealers. If I discover that three of my friends have recently bought
Brand X from ABC Dealership and that all three have been delighted
with their purchases, then I will conclude by analogy that if I buy Brand
X from ABC Dealership, I will be delighted, too.
– Evaluating Analogical Reasoning
• Number of instances, instance variety, # of similarities,
relevance, # of dissimilarities, modesty of conclusion
Case-Based Reasoning (CBR)
– Inductive learning
A machine learning approach in which rules
are inferred from facts or data
Case-Based Reasoning (CBR)
•
The basic idea and process of CBR
– Four-step process
1.
2.
3.
4.
Retrieve
Reuse
Revise
Retain
Case-Based Reasoning (CBR)
• Definition and concepts of cases in CBR
– Ossified cases
Cases that have been analyzed and have no
further value
– Paradigmatic cases
A case that is unique that can be maintained
to derive new knowledge for the future
Case-Based Reasoning (CBR)
• Definition and concepts of cases in CBR
– Stories
Cases with rich information and episodes.
Lessons may be derived from this kind of
cases in a case base
Case-Based Reasoning (CBR)
Case-Based Reasoning (CBR)
• Benefits and usability of CBR
– CBR makes learning much easier and the
recommendation more sensible
Case-Based Reasoning (CBR)
• Advantages of using CBR
–
–
–
–
–
–
–
–
Knowledge acquisition is improved.
System development time is faster
Existing data and knowledge are leveraged
Complete formalized domain knowledge is not
required
Experts feel better discussing concrete cases
Explanation becomes easier
Acquisition of new cases is easy
Learning can occur from both successes and failures
Case-Based Reasoning (CBR)
• Uses, issues, and applications of CBR
– Applications
•
•
•
•
•
•
•
CBR in electronic commerce
WWW and information search
Planning and control
Design
Reuse
Diagnosis
Reasoning
Case-Based Reasoning (CBR)
• Uses, issues, and applications of CBR
– Implementation issues for designers
• What makes up a case? How can we represent
case memory?
• Automatic case-adaptation rules can be very
complex
• How is memory organized? What are the indexing
rules?
• The quality of the results is heavily dependent on
the indexes used
Case-Based Reasoning (CBR)
– Implementation issues for designers
• How does memory function in relevant information
retrieval?
• How can we perform efficient searching (i.e.,
knowledge navigation) of the cases?
• How can we organize the cases?
• How can we design the distributed storage of cases?
• How can we adapt old solutions to new problems?
Can we simply adapt the memory for efficient
querying, depending on context? What are the
similarity metrics and the modification rules?
Case-Based Reasoning (CBR)
– Implementation issues for designers
• How can we factor errors out of the original cases?
• How can we learn from mistakes? That is, how can
we repair and update the case base?
• The case base may need to be expanded as the
domain model evolves, yet much analysis of the
domain may be postponed.
• How can we integrate CBR with other knowledge
representations and inferencing mechanisms?
• Are there better pattern-matching methods than the
ones we currently use?
• Are there alternative retrieval systems that match
the CBR schema?
Case-Based Reasoning (CBR)
• Success factors for CBR systems
1.
2.
3.
4.
Determine specific business objectives
Understand your end users and customers
Design the system appropriately
Plan an ongoing knowledge-management
process
5. Establish achievable returns on investment
(ROI) and measurable metrics
6. Plan and execute a customer-access strategy
7. Expand knowledge generation and access
across the enterprise
Genetic Algorithm Fundamentals
• Genetic algorithms (GAs)
Software programs that learn in an
evolutionary manner similar to the way
biological systems evolve
Genetic Algorithm Fundamentals
• Genetic algorithm process and terminology
– Chromosome
A candidate solution for a genetic algorithm
– Reproduction
The creation of new generations of improved
solutions with the use of a genetic algorithm
Genetic Algorithm Fundamentals
• Genetic algorithm process and terminology
– Crossover
The combining of parts of two superior
solutions by a genetic algorithm in an attempt
to produce an even better solution
– Mutation
A genetic operator that causes a random
change in a potential solution
Genetic Algorithm Fundamentals
Genetic Algorithm Fundamentals
– A few parameters must be set for the genetic
algorithm
• Number of initial solutions to generate
• Number of offspring to generate
• Number of parents and offspring to keep for the
next generation
• Mutation probability (very low)
Genetic Algorithm Fundamentals
– Limitations of genetic algorithms
• Not all problems can be framed in the mathematical
manner that genetic algorithms demand
• Development of a genetic algorithm and
interpretation of the results requires an expert who
has both the programming and
statistical/mathematical skills demanded by the
genetic algorithm technology in use
• In some situations, the “genes” from a few
comparatively highly fit (but not optimal) individuals
may come to dominate the population, causing it to
converge on a local maximum
Genetic Algorithm Fundamentals
– Limitations of genetic algorithms
• Most genetic algorithms rely on random number
generators that produce different results each time
the model runs
• Locating good variables that work for a particular
problem is difficult
• Selecting methods by which to evolve the system
requires thought and evaluation
Developing Genetic
Algorithm Applications
• GAs are a type of machine learning for
representing and solving complex
problems
Developing Genetic
Algorithm Applications
Applications of GAs include:
• Dynamic process control
• Induction of optimization
of rules
• Discovery of new
connectivity topologies
(e.g., neural computing
connections, i.e., neural
network design)
• Simulation of biological
models of behavior and
evolution
• Complex design of
engineering structures
• Pattern recognition
• Scheduling
• Transportation and
routing
• Layout and circuit design
• Telecommunication
• Graph-based problems
Fuzzy Logic Fundamentals
• Fuzzy logic
Logically consistent ways of reasoning
that can cope with uncertain or partial
information; characteristic of human
thinking and many expert systems.
• Fuzzy sets
A set theory approach in which set
membership is less precise than having
objects strictly in or out of the set
Fuzzy Logic Fundamentals
Fuzzy Logic Fundamentals
• Fuzzy logic applications in manufacturing and
management
– Selection of stocks to purchase (e.g., the Japanese
Nikkei stock exchange)
– Retrieval of data (because fuzzy logic can find data
quickly)
– Inspection of beverage cans for printing defects
– Matching of golf clubs to customers’ swings
– Risk assessment
– Control of the amount of oxygen in cement kilns
– Accuracy and speed increases in industrial qualitycontrol applications
– Sorting problems in multidimensional spaces
Fuzzy Logic Fundamentals
• Fuzzy logic applications in manufacturing and
management
– Enhancement of models involving queuing (i.e.,
waiting lines)
– Managerial decision support applications
– Project selection
– Environmental control building
– Control of the motion of trains
– Paper mill automation
– Space shuttle vehicle orbiting
– Regulation of water temperature in shower heads
Natural Language
Processing (NLP)
• Natural language processing (NLP)
Using a natural language processor to
interface with a computer-based system
• Two types of NLP
– Natural language understanding
– Natural language generation
Natural Language
Processing (NLP)
– Some problems that make NLP difficult
•
•
•
•
•
Word boundary detection
Word sense disambiguation
Syntactic ambiguity
Imperfect or irregular input
Speech acts and plans
Natural Language
Processing (NLP)
– The current NLP technology
• Search and information retrieval
• A person enters a certain phrase, word, or
sentence on which to search the Internet or some
database, and NLP is then used to construct the
best query possible
Natural Language
Processing (NLP)
– Applications of NLP
• Human–computer interfaces
– Abstracting and summarizing text
– Analyzing grammar
– Understanding speech
Natural Language
Processing (NLP)
– Applications of NLP
• Front ends for other software packages—
querying a database that allows the user to
operate the applications programs with everyday
language
– Text mining
– FAQs and query answering
Natural Language
Processing (NLP)
•
Machine translation
– Translation of content to other languages
– Criteria used to assess machine translation
1. Intelligibility
2. Accuracy
3. Speed
Voice Technologies
• Voice technologies fall into three broad
categories:
– Voice (or speech) recognition
– Voice (or speech) understanding
– Text-to-voice (or voice synthesis)
Voice Technologies
– Voice (speech) recognition
Translation of the human voice into individual
words and sentences understandable by a
computer
– Speech understanding
An area of AI research that attempts to allow
computers to recognize words or phrases of
human speech
Voice Technologies
– Advantages of voice technologies
1.
2.
3.
4.
5.
6.
7.
8.
9.
Ease of access
Speed
Manual freedom
Remote access
Accuracy
Communicating while driving
Quick selection
Security
Cost benefit
Voice Technologies
– Limitations of speech recognition and
understanding
• Inability to recognize long sentences, or the
excessive length of time needed to accomplish
that understanding
• High cost
• Speech may need to be combined with keyboard
entry, which slows communication
Voice Technologies
• Voice synthesis
The technology by which computers
convert text-to-voice (speak)
– A text-to-speech system is composed of two
parts:
• Front end takes input in the form of text and
outputs a symbolic linguistic representation
• Back end takes the symbolic linguistic
representation as input and outputs the
synthesized speech waveform
Voice Technologies
• Voice technology applications
– Call center
– Contact of customer care center
– Computer/telephone integration (CTI)
– Interactive voice response (IVR)
– Voice portal
– Voice over IP (VoIP)
Voice Technologies
– Voice portals
Web sites, usually portals, with audio
interfaces
Developing
Integrated Advanced Systems
• Fuzzy neural networks
– Fuzzification
A process that converts an accurate number
into a fuzzy description, such as converting
from an exact age into young or old
– Defuzzification
Creating a crisp solution from a fuzzy logic
solution
Developing
Integrated Advanced Systems
Developing
Integrated Advanced Systems
Developing
Integrated Advanced Systems
• Genetic algorithms and neural networks
– The genetic learning method can perform rule
discovery in large databases, with the rules fed into
a conventional ES or some other intelligent system
– To integrate genetic algorithms with neural network
models use a genetic algorithm to search for
potential weights associated with network
connections
– A good genetic learning method can significantly
reduce the time and effort needed to find the optimal
neural network model