Transcript Artificial Intelligence and Expert Systems

CHAPTER 6
Knowledge-Based Decision Support
Opening Vignette: Case Study
A Knowledge-based DSS in A Chinese Chemical
Plant.
Dalian Dyestuff plant is one of the largest chemical plants in
China. It produces about 100 different kinds of dyes and
other chemical products. With the economic reform in
China, manufacturing decisions were decentralized. The
plant managers were suddenly faced with the problem of
determining their own production plans. Because of the size
of the plant and the number of products, it became very
difficult to make and appropriately change production
plans, which depend on market demand. The plant also had
to make purchasing decisions and decisions regarding of
the disposal of environmentally damaging materials.
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6.1 Concepts and Definitions
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Managerial Decision Makers are Knowledge
Workers
Use Knowledge in Decision Making
Accessibility to Knowledge Issue
Knowledge-Based Decision Support: Applied
Artificial Intelligence
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6.1 Concepts and Definitions
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Artificial Intelligence (AI) is a term that (in this
textbook)
Encompasses many definitions
• AI involves studying human thought processes (to
understand what intelligence is)
• Representing thought processes on machines (such as
computer and robots)
One well-published definition of AI is : Behavior by a
machine that, if performed by a human being, would be
considered intelligent
“…study of how to make computers do things at which,
at the moment, people are better” (Rich and Knight
[1991])
Theory of how the human mind works (Mark Fox)
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6.1 Concepts and Definitions
Winston and Prendergast [1984] list three Objectives of
AI
– Make machines smarter (primary goal)
– Understand what intelligence is (Nobel Laureate
purpose)
– Make machines more useful (entrepreneurial purpose)
(Winston and Prendergast [1984], The AI Business. Cambridge, MA:
MIT Press)
The meaning of the term Intelligent behavior as the follows:
Or on other words, signs of Intelligence:
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6.1 Concepts and Definitions
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Learn or understand from experience
Make sense out of ambiguous or contradictory messages
Respond quickly and successfully to new situations
Use reasoning to in solving problems and directing
conduct effectively
Deal with perplexing situations
Understand and infer in ordinary, rational ways
Apply knowledge to manipulate the environment
Think and reason
Recognize the relative importance of different elements in
a situation
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6.1 Concepts and Definitions
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However, Although AI’s ultimate goal is to build
machines that mimic human intelligence, the capabilities
of current commercial AI products are far from exhibiting
any significant success in the abilities just listed.
Nevertheless, AI programs are continually improving, and
they increase productivity and quality by automating
several tasks that requires some human intelligence.
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6.1 Concepts and Definitions
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Testing for Intelligence
An interesting test designed to determine whether a
computer exhibits intelligent behavior was designed by
Alan Turing and is called the Turing test.
A computer can be considered to be smart only when a
human interviewer, “conversing” with both an unseen
human being and an unseen computer, could not
determine which is which
The following definitions and characteristics of AI focus
on decision-making and problem solving.
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6.1 Concepts and Definitions
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Symbolic Processing
When human experts solve problems, particularly the types
that are considered appropriate for AI, they do not do it by
solving sets of equations or performing other laborious
mathematical computations. Instead, they choose symbols to
represent the problem concepts and apply various strategies
and rules to manipulate these concept. The AI approach
represents knowledge as sets of symbols that stands for
problem concepts. In Summary,
– Use Symbols to Represent Problem Concepts
– Apply Various Strategies and Rules to Manipulate these
Concepts
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6.1 Concepts and Definitions
A symbol is a string of characters that stands for some
real-world concept
Examples
Product
– Defendant
– 0.8
– Chocolate
These symbols can be combined to express meaningful
relationships, which calls for symbol structure.
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6.1 Concepts and Definitions
Symbol Structures (Relationships)
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(DEFECTIVE product)
(LEASED-BY product defendant)
(EQUAL (LIABILITY defendant) 0.8)
tastes_good (chocolate).
Interpreted to “the product is defective”, “product is leased by
the defendant”, “the liability of the defendant is 0.8”, and
“chocolate taste good”. However, they may be interpreted
differently. This is one of the problems we encounter in
building AI systems.
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To solve a problem, an AI Programs Manipulate
Symbols to Solve Problems
Symbols and Symbol Structures Form Knowledge
Representation
Symbolic processing (definition) is an essential
characteristics of AI. As reflected by a definition of AI
in a branch of computer science: Artificial Intelligence
Dealings Primarily with Symbolic, Non-algorithmic
Problem- Solving Methods
These definition focuses on two characteristics:
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6.1 Concepts and Definitions
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Numeric versus Symbolic
Computers were originally designed specifically to
process numbers (numeric processing). However,
people tend to think symbolically; our intelligence
seems to be based, in part, on our mental ability to
manipulate symbols rather than just numbers. Although
symbol processing is a core in AI, this does not mean
that AI does not involve math; rather, the emphasis in
AI is on the manipulation of symbols.
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6.1 Concepts and Definitions
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Algorithmic versus Non-algorithmic
An algorithm is a step-by-step procedure that has welldefined starting and ending points and is guaranteed to
reach a solution to a specific problem. Most computer
architectures readily lend themselves to this step-bystep approach. Many human reasoning processes tend
to be non-algorithmic; in other words, our mental
activities consist of more than just following logical,
step-by-step procedures.
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6.1 Concepts and Definitions
Heuristics
Heuristics are included as a key element of AI in the following
definition: “AI is the branch of computer science that deals
with ways of representing knowledge using symbols rather
than numbers and with rules-of-thumb, or heuristics, methods
for processing information.(Encyclopedia Britannica)
– Inferencing
AI involves an attempt by machines to exhibit reasoning
capabilities. The reasoning consists of inferencing from
facts and rules using heuristics or other search approaches.
AI is unique in that it makes inferencing by using a pattern
Matching approach.
– Pattern Matching
Attempt to describe objects, events, or processes in terms
of their qualitative features and logical and computational
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6.2 Artificial Intelligence versus
Natural Intelligence
The potential value of AI can be better understood by
contrasting it with natural, or human intelligence.
 More permanent. Natural intelligence is perishable from a
commercial standpoint in that workers can change their
place of employment or forget information. However, AI is
permanent as long as the computer systems and programs
remain unchanged.
 Ease of duplication and dissemination. Transferring a
body of knowledge from one person to another usually
requires a lengthy process of apprenticeship; even so,
expertise can never be duplicated completely. However,
when knowledge is embodies in a computer system, it can
be copies from what computer and easily moved to another
computer, sometimes across the globe.
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6.2 Artificial Intelligence versus
Natural Intelligence
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Less expensive than the natural intelligence. There are
many circumstances in which buying computer service
costs less than having corresponding human power carry
out the same tasks.
AI, being a computer technology, is consistent and
thorough: Natural intelligence is erratic because people
are erratic; they do not always perform consistently.
Can be documented. Decisions made by a computer can
be easily documented by tracing the activities of the
system. Natural intelligence is difficult to reproduce. For
example, a person may may reach a conclusion but at some
later date may be unable to recreate the reasoning process
that led to that conclusion or to even recall the assumptions
that were a part of the decision.
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6.2 Artificial Intelligence versus
Natural Intelligence
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Can execute certain tasks much faster than a human
 Can perform certain tasks better than many or even
most people
Natural language does have several advantages over AI.
Some are:
 Natural intelligence is creative, whereas AI is rather
uninspired. The ability to acquire knowledge is inherent
in human beings, but with AI, tailored knowledge must
be built into a carefully constructed system.
 Natural intelligence enables people use sensory
experience directly, whereas most AI system must work
with symbolic input and representations.
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6.2 Artificial Intelligence versus
Natural Intelligence
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Perhaps most importantly, human beings reasoning is
able to use at all times a wide context of experience
bring that to bear on individual problems. In contrast,
AI systems typically gain their power by having a Very
Narrow Focus
Information Processing
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Computers can collect and process information
efficiently (such as a large amount of information)
People instinctively:
• Recognize relationships between things
• Sense qualities
• Spot patterns indicating relationships
BUT, AI technologies can provide significant
improvement in productivity and quality!
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6.3 Knowledge in Artificial Intelligence
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What is knowledge? (based on Sowa[1985])
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Knowledge encompasses the implicit and explicit
restrictions placed upon objects (entities), operations
and relationships along with general and specific
heuristics and inference procedures involved in the
situation being modeled.
Major characteristics that distinguishes AI from other
CBIS is that AI’s major emphasis is knowledge
processing (rather than data or information processing).
Knowledge is now recognized as a major organization
resource.
• Data, information and knowledge can be classified
by their degree of abstraction and by their quantity.
Knowledge is the most abstraction and exists in the
smallest quantity.
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6.3 Knowledge in Artificial Intelligence
High
Degree of
Abstraction
Knowledge
Information
Low
Data
Quantity
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Processed INFORMATION Relevant and
actionable
DATA
KNOWLEDGE
6.3 Knowledge in Artificial
Intelligence
Relevant and
actionable data
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6.3 Knowledge in Artificial Intelligence
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Uses of knowledge
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Although the computers can not have a diversity of
experiences, or study and learn as the human mind can,
it can use knowledge given to it by human experts.
Such knowledge consists of facts, concepts, theories,
heuristics methods, procedures and relationships.
Knowledge is also information that has been organized
and analyzed to make it understandable and applicable
to problem solving or decision making.
The collection of knowledge related to a problem (or an
opportunity) used in an AI system is organized together
and it is called a knowledge base. Most knowledge
bases are limited in that they typically focus on some
specific, usually narrow subject area or domain.
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6.3 Knowledge in Artificial Intelligence
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In fact, the narrow domain of knowledge and the fact
that an AI system must involve some qualitative aspects of
decision making are viewed as critical for AI application
success.
– Once a Knowledge base is built, AI techniques are used
to give the computer inference capabilities based on the
facts and relationships contained in the knowledge
base.
Knowledge Bases
– With a knowledge base and the ability to draw
inferences from it, the computer can be put to practical
use as a problem solver and decision maker. Figure
shows a application of KB.
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6.3 Knowledge in Artificial Intelligence
Computer
Inputs
(questions,
problem,
etc.
Knowledge
Inferencing
Base
capability
Outputs
(answers,
alternative,
solution,
etc.
By searching the knowledge base for relevant facts and
relationships, the computer can reach one or more
alternative solutions to the given problems.
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6.3 Knowledge in Artificial Intelligence
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Knowledge Engineering (definition)
(Feigenbaum and McCorduck [1983])
– The art of bringing the principles and tools of AI research
to bear on difficult applications problems requiring
expert’s knowledge for their solutions.
– The technical issues of acquiring this knowledge,
representing it and using it appropriately to construct and
explain lines of reasoning are important problems in the
design of knowledge-based systems.
– The art of constructing intelligent agents is both part of
and an extension of the programming art. It is the art of
building complex computer programs that represent and
reason with knowledge of the world.
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6.3 Knowledge in Artificial Intelligence
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Knowledge engineering process: (Narrow scope)
– Knowledge acquisition: acquisition of knowledge from
human experts, books, documents, sensors, or computer
files. Knowledge may be specific to the problem domain or
to the problem-solving procedures, or it may be general
knowledge, or it may be metaknowledge (knowledge about
knowledge --- information about how experts use their
knowledge to solve problems and problem-solving
procudures)
– Knowledge representation: Acquired knowledge is
organized in an activity called knowledge representation.
For example, preparation of knowledge map and encoding
the knowledge in the knowledge base.
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6.3 Knowledge in Artificial Intelligence
– Knowledge validation: knowledge is validated and
verified until its quality is acceptable. Test case results are
usually shown to the experts to verify the accuracy of the
ES.
– Inferencing: Design of software to enable the computer to
make inference based on the knowledge and specifics of a
problem. Then the system can provide advice to a
nonexpert user.
– Explanation and justification: Design and programming
of an explanation capability; e.g., programming the ability
to answer question like why a specific piece of information
is needed by the computer or how a certain conclusion was
derived by a computer.
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6.3 Knowledge in Artificial Intelligence
Knowledge engineering process: A overview
Knowledge
validation
(test cases)
Sources of knowledge
(experts, others)
Knowledge
Acquisition
Knowledge
base
Encoding
Knowledge
Representation
Explanation
justification
Inferencing
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6.3 Knowledge in Artificial Intelligence
– Wide scope
• Entire process of developing and maintaining AI
 Knowledge Types
 Advantaged knowledge
 Base knowledge
 Trivial knowledge (琐细的\一般性的)
 Explicit knowledge
 Objective, rational, technical
 Easily documented
 Easily transferred / taught / learned
 Tacit knowledge
 Subjective, cognitive, experiential learning
 Hard to document
 Hard to transfer / teach / learn
 Involves
a lot of human interpretation
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6.3 Knowledge in Artificial Intelligence
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Knowledge Management
 A process of elicitation, transformation, and diffusion
of knowledge throughout an enterprise so that it can be
shared and thus reused
 Helps organizations find, select, organize, disseminate,
and transfer important information and expertise
 Transforms data / information into actionable
knowledge to be used effectively anywhere in the
organization by anyone
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6.3 Knowledge in Artificial Intelligence
Process of explication
may ge ner ate ne w tacit
knowle dge
Explicit Knowledge
Tacit
Tacit
Knowledge
Knowledge
Conver t tacit knowledge into
artic ulated and measurable
explicit knowledge
Policies, patents,
decisions,
stra tegies, IS, white
papers, etc.
Expertise, know-how, ideas,
orga nization culture, values, etc.
Core Competencies of the
Organization
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6.3 Knowledge in Artificial Intelligence
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KM Objectives
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Create knowledge repositories
Improve knowledge access
Enhance the knowledge environment
Manage knowledge as an asset
Chief Knowledge Officer (CKO)
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Maximize firm’s knowledge assets
Design and implement KM strategies
Effectively exchange knowledge assets
Promote system use
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6.4 Knowledge Acquisition
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Knowledge Acquisition Difficulties
– Problems in Transferring Knowledge
• Expressing Knowledge
• Transfer to a Machine
• Number of Participants
• Structuring Knowledge
– Experts may lack time or not cooperate
– Testing and refining knowledge is complicated
– Poorly defined methods for knowledge elicitation
– System builders may collect knowledge from one
source, but the relevant knowledge may be scattered
across several sources
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6.4 Knowledge Acquisition
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Knowledge Acquisition Difficulties
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May collect documented knowledge rather than use
experts
The knowledge collected may be incomplete
Difficult to recognize specific knowledge when mixed
with irrelevant data
Experts may change their behavior when observed
and/or interviewed
Problematic interpersonal communication between the
knowledge engineer and the expert
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6.4 Knowledge Acquisition
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Knowledge Acquisition Methods
– Manual
– Semiautomatic
– Automatic (Computer Aided)
Manual Methods - Structured Around Interviews
– Process (See Figure)
– Interviewing
– Tracking the Reasoning Process
– Observing
– Manual methods: slow, expensive and sometimes
inaccurate
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6.4 Knowledge Acquisition
Experts
Knowledge
engineer
Coding
Knowledge
base
Documented
knowledge
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Semiautomatic Methods
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Support Experts Directly (see Figure)
Help Knowledge Engineers
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6.4 Knowledge Acquisition
Expert
Computer-aided
(interactive)
interviewing
Coding
Knowledge
base
Knowledge
engineer
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Automatic (Computer Aided)
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Expert’s and/or the knowledge engineer’s roles are
minimized (or eliminated)
Induction Method (see Figure )
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6.4 Knowledge Acquisition
Case
histories
and examples
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Induction
system
Knowledge
base
Knowledge Modeling
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The knowledge model views knowledge acquisition as
the construction of a model of problem-solving
behavior-- a model in terms of knowledge instead of
representations
Can reuse models across applications
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6.4 Knowledge Acquisition -----Manual
methods
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Interviews
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Most Common Knowledge Acquisition: Face-to-face
interviews
Interview Types
• Unstructured (informal)
• Semi-structured
• Structured
Tracking Methods
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Techniques that attempt to track the reasoning process of
an expert
From cognitive psychology
Most common formal method: Protocol Analysis
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6.4 Knowledge Acquisition
– Protocol Analysis:
• Protocol: a record or documentation of the expert's
step-by-step information processing and decisionmaking behavior
• The expert performs a real task and verbalizes his/her
thought process (think aloud)
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Observations and Other Manual Methods
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Case analysis
Critical incident analysis
Discussions with the users
Commentaries
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6.4 Knowledge Acquisition
• Conceptual graphs and models
• Brainstorming
• Prototyping
• Multidimensional scaling
• Johnson's hierarchical clustering
• Performance review
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Expert-driven Methods
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Knowledge Engineers Typically
• Lack Knowledge About the Domain
• Are Expensive
• May Have Problems Communicating With Experts
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6.4 Knowledge Acquisition
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Knowledge Acquisition May be Slow, Expensive and
Unreliable
Can Experts Be Their Own Knowledge Engineers?
Expert-driven Methods May Use
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Manual---Expert's Self-reports
Computer-Aided (Semiautomatic)
– REFINER+ - case-based system
– Visual modeling techniques
– New machine learning methods to induce
decision trees and rules
– Tools based on repertory grid analysis
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6.4 Knowledge Acquisition
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Automated Knowledge Acquisition (Machine
Learning)
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Rule Induction
• Induction: Process of Reasoning from Specific to
General
• In ES: Rules Generated by a Computer Program from
Cases
• Interactive Induction
Case-based Reasoning
• For Building ES by Accessing Problem-solving
Experiences for Inferring Solutions for Solving Future
Problems
• Cases and Resolutions Constitute a Knowledge Base
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6.4 Knowledge Acquisition
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Automated Knowledge Acquisition (Machine
Learning)
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Neural Computing
• Fairly Narrow Domains with Pattern Recognition
• Requires a Large Volume of Historical Cases
Intelligent Agents
• KQML (Knowledge Query and Manipulation
Language) for Knowledge Sharing
• KIF, Knowledge Interchange Format (Among
Disparate Programs)
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6.5 Knowledge Representation
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Knowledge representation
Once acquired, knowledge must be organized for use.
– A good knowledge representation naturally represents the
problem domain
– An unintelligible (难解的) knowledge representation is
wrong
– Most artificial intelligence systems consist of:
• Knowledge Base
– Forms the system's intelligence source
– Inference mechanism uses to reason and draw
conclusions
• Inference Mechanism (Engine) Examines the knowledge
base to answer questions, solve problems or make
decisions
domain
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6.5 Knowledge Representation
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Knowledge representation
– Many knowledge representation schemes
• Can be programmed and stored in memory
• Are designed for use in reasoning
– Major knowledge representation schemas:
• Production rules
• Frames
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Representation in Logic and other Schemas
– General form of any logical process
– Inputs (Premises)
– Premises used by the logical process to create the output,
consisting of conclusions (inferences)
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6.5 Knowledge Representation
– Facts known true can be used to derive new facts that
are true
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Symbolic logic: System of rules and procedures
that permits the drawing of inferences from
various premises
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Basic Forms of Computational Logic
– Propositional logic (or propositional calculus)
– Predicate logic (or predicate calculus)
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6.5 Knowledge Representation
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Propositional Logic
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A proposition is a statement that is either true or false
Once known, it becomes a premise that can be used to
derive new propositions or inferences
Rules are used to determine the truth (T) or falsity (F)
of the new proposition
Symbols represent propositions, premises or
conclusions
Statement: A = The mail carrier comes Monday through
Friday.
Statement: B = Today is Sunday.
Conclusion: C = The mail carrier will not come today.
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Propositional logic: limited in representing real-world
knowledge
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6.5 Knowledge Representation
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Predicate Calculus
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Predicate logic breaks a statement down into component
parts, an object, object characteristic or some object
assertion
Predicate calculus uses variables and functions of
variables in a symbolic logic statement
Predicate calculus is the basis for Prolog (PROgramming
in LOGic)
Prolog Statement Examples
• comes_on(mail_carrier, monday).
• likes(jay, chocolate).
– (Note - the period “.” is part of the
statement)
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6.5 Knowledge Representation
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Scripts
Knowledge Representation Scheme
Describing a Sequence of Events
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Elements include
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Entry Conditions
Props
Roles
Tracks
Scenes
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6.5 Knowledge Representation
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Lists: Written Series of Related Items
– Normally used to represent hierarchical knowledge
where objects are grouped, categorized or graded
according to
• Rank or
• Relationship
Decision Tables (Induction Table): Knowledge
Organized in a Spreadsheet Format
– Attribute List
– Conclusion List
– Different attribute configurations are matched against the
conclusion
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6.5 Knowledge Representation
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Decision Trees
– Related to tables
– Similar to decision trees in decision theory
– Can simplify the knowledge acquisition process
– Knowledge diagramming - very natural
O-A-V Triplet
– Objects, Attributes and Values
– O-A-V Triplet
– Objects may be physical or conceptual
– Attributes are the characteristics of the objects
– Values are specific measures of the attributes
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6.5 Knowledge Representation
Representative O-A-V Items
Object
Attributes
Values
House
Bedrooms
2, 3, 4, etc.
House
Color
Green, white, brown,
etc.
Admission to a
university
Grade-point average
3.0, 3.5, 3.7, etc.
Inventory control
Level of inventory
14, 20, 30, etc.
Bedroom
Size
9 X 10, 10 X 12, etc.
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6.5 Knowledge Representation
• Default Logic
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Knowledge Maps
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
Deals with uncertainties
Incomplete information
Visual representation
Cognitive maps
Semantic Networks




Fall, 2007
Graphic Depiction of Knowledge
Nodes and Links Showing Hierarchical Relationships
Between Objects
Nodes: Objects
Arcs: Relationships
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6.5 Knowledge Representation
–
• is-a
• has-a
Semantic networks can show inheritance
Semantic Nets - visual representation of relationships
Can be combined with other representation methods
–
Semantic Network Example
–
–
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6.5 Knowledge Representation
Human
Being
Boy
Needs
Goes to
School
Woman
Joe
Food
Has
a child
Kay
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6.5 Knowledge Representation

Production Rules
–
Condition-Action Pairs
• IF this condition (or premise or antecedent) occurs,
• THEN some action (or result, or conclusion, or
consequence) will (or should) occur
• IF the stop light is red AND you have stopped, THEN a
right turn is OK
–
–
Fall, 2007
Each production rule in a knowledge base represents an
autonomous chunk of expertise
When combined and fed to the inference engine, the set
of rules behaves synergistically
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6.5 Knowledge Representation
Rules can be viewed as a simulation of the cognitive
behavior of human experts
– Rules represent a model of actual human behavior
Forms of Rules
– IF premise, THEN conclusion
• IF your income is high, THEN your chance of being
audited by the IRS (美国国税局)is high
– Conclusion, IF premise
• Your chance of being audited is high, IF your income is
high
Inclusion of ELSE
– IF your income is high, OR your deductions are unusual,
THEN your chance of being audited by the IRS is high,
OR ELSE your chance of being audited is low
–


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6.5 Knowledge Representation


More Complex Rules
– IF credit rating is high AND salary is more than $30,000,
OR assets are more than $75,000, AND pay history is not
"poor," THEN approve a loan up to $10,000, and list the
loan in category "B.”
– Action part may have more information: THEN "approve
the loan" and "refer to an agent"
Knowledge and Inference Rules
– Common Types of Rules:
• Knowledge rules, or declarative rules, state all the facts
and relationships about a problem
• Inference rules, or procedural rules, advise on how to
solve a problem, given that certain facts are known
• Inference rules contain rules about rules (metarules)
• Knowledge rules are stored in the knowledge base
• Inference rules become part of the inference engine
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6.5 Knowledge Representation

Advantages of Rules
–
–
–
–
–

Easy to understand (natural form of knowledge)
Easy to derive inference and explanations
Easy to modify and maintain
Easy to combine with uncertainty
Rules are frequently independent
Limitations of Rules
–
–
–
Fall, 2007
Complex knowledge requires many rules
Builders like rules
Search limitations in systems with many rules
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Characteristics of Rule Representation
First Part
Second Part
Names
Premise 
Antecedent 
Situation 
IF 
Conclusion
Consequence
Action
THEN
Nature
Conditions, similar to declarative
knowledge
Resolutions, similar
to procedural
knowledge
Size
Can have many IFs
Usually one
conclusion
AND statements
All conditions must
be true for a
conclusion to be true
OR statements
If any of the OR
statement is true, the
conclusion is true
Statements
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6.5 Knowledge Representation

Frames: Definitions and Overview
– Frame: Data structure that includes all the
knowledge about a particular object
– Knowledge organized in a hierarchy for diagnosis of
knowledge independence
– Form of object-oriented programming for AI and
ES.
– Each Frame Describes One Object
– Special Terminology
Default
Instantiation
Demon
Master frame
Facet
Object
Hierarchy of frames
Range
If added
Slot
If needed
Value (entry)
Instance of
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6.5 Knowledge Representation







Concise, natural, structural representation of
knowledge
Encompasses complex objects, entire situations or
a management problem as a single entity
Frame knowledge is partitioned into slots
Slot can describe declarative knowledge or
procedural knowledge
Major Capabilities of Frames (next slide)
Typical frame describing an automobile
Hierarchy of Frames: Inheritance
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6.5 Knowledge Representation
Frame Capabilities
Ability to clearly document information about a domain model; for example, a
plant's machines and their associated attributes
Related ability to constrain the allowable values that an attribute can take on
Modularity of information, permitting ease of system expansion and maintenance
More readable and consistent syntax for referencing domain objects in the rules
Platform for building a graphic interface with object graphics
Mechanism that will allow us to restrict the scope of facts considered during
forward or backward chaining
Access to a mechanism that supports the inheritance of information down a
class hierarchy
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6.5 Knowledge Representation


Multiple Knowledge representations
– Rules + Frames
– Others
Knowledge Representation Must Support
–
–
–
Fall, 2007
Acquiring knowledge
Retrieving knowledge
Reasoning
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6.5 Knowledge Representation

Considerations for Evaluating a Knowledge
Representation
–
Naturalness, uniformity and understandability
–
Degree to which knowledge is explicit (declarative) or
embedded in procedural code
–
Modularity and flexibility of the knowledge base
–
Efficiency of knowledge retrieval and the heuristic
power of the inference procedure
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6.5 Knowledge Representation

No single knowledge representation method is ideally
suited by itself for all tasks

Multiple knowledge representations: each tailored to a
different subtask

Production Rules and Frames works well in practice

Object-Oriented Knowledge Representations
– Hypermedia
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6.5 Knowledge Representation

Experimental Knowledge Representations
–
–
–

Cyc
NKRL
Spec-Charts Language
The Cyc System
–
–
–
–
–
Fall, 2007
Attempt to represent a substantial amount of common
sense knowledge
Bold assumptions: intelligence needs a large amount of
knowledge
Need a large knowledge base
Cyc over time is developing as a repository of a
consensus reality - the background knowledge possessed
by a typical U.S. resident
There are some commercial applications based on
portions of Cyc
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6.5 Knowledge Representation

NKRL
– Narrative Knowledge Representational Language
(NKRL)
– Standard, language-independent description of the
content of narrative textual documents
– Can translate natural language expressions directly into
a meaningful set of templates that represent the
knowledge
–
Fall, 2007
Knowledge Interchange Format (KIF): To Share
Knowledge and Interact
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6.5 Knowledge Representation

The Spec-Charts Language
–
–
–

Based on Conceptual Graphs: to Define Objects and
Relationships
Restricted Form of Semantic Networks
Evolved into the Commercial Product – STATEMATE
Knowledge Representation and the Internet
–
–
–
Fall, 2007
Hypermedia documents to encode knowledge directly
Hyperlinks Represent Relationships
MIKE (Model-based and Incremental Knowledge
Engineering
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6.5 Knowledge Representation




Formal model of expertise: KARL Specification Language
Semantic networks: Ideally suited for hypermedia
representation
Web-based Distributed Expert System (Ex-W-Pert System)
for sharing knowledge-based systems and groupware
development
Representing Uncertainty: An Overview
– Dealing with Degrees of Truth, Degrees of Falseness in
ES
– Uncertainty
• When a user cannot provide a definite answer
• Imprecise knowledge
• Incomplete information
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6.5 Knowledge Representation



Uncertainty: Several Approaches Related to
Mathematical and Statistical Theories
– Bayesian Statistics
– Dempster and Shafer's Belief Functions(规则的有效性
和限制 )
– Fuzzy Sets
Uncertainty in AI: Approximate Reasoning, Inexact
Reasoning
Relevant Information is Deficient in One or More
– Information is partial
– Information is not fully reliable
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6.5 Knowledge Representation
–
Representation language is inherently imprecise
–
Information comes from multiple sources and it is
conflicting
–
Information is approximate
–
Non-absolute cause-effect relationships exist
–
Can include probability in the rules
–
IF the interest rate is increasing, THEN the price of
stocks will decline (80% probability)
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6.6 How AI differs from conventional
computing

Conventional Computing.
Conventional computer programs are based on an
algorithm, which is a clearly defined, step-by-step
procedure for solving a problem. It may be a
mathematical formula or a sequential procedure that
will let to a solution. The algorithm is converted into a
computer program that tells the computer exactly what
operations to perform. The algorithm then uses data,
symbol, or words to solve the problem.
– Conventional computer process data: Calculate,
Perform Logic, Store, Retrieve, Sort, Edit, Make
structured decision, Monitor, Control, Networking
 AI computing: AI software is based on symbolic
representation and manipulation. A symbol is a letter,
word, or number that is used to represent objects, process,
–
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6.6 How AI differs from conventional
computing
–
Fall, 2007
and their relationships. Objects can be people, things,
ideas, concepts, events, or statements of fact. By using
symbol , it is possible to create a knowledge base that
states facts, concepts, and the relationships among
them. Then, various processes are used to manipulate
the symbols to generate advice or a recommendation
for solving problems.
Once Knowledge Base has been built, it can be used to
solve the problem. How does the AI software reason or
infer with KB? Basic technique is search and pattern
matching. Given some initial information, the AI
software searches the knowledge base, looking for
specific conditions or patterns. It looks for match ups
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6.6 How AI differs from conventional
computing
–
–
–
that satisfy the criteria set up to solve the problem. The
computer literally hunts around until it finds the best
answer, based on the knowledge it has.
Even though AI problem solving does not take place
directly by algorithmic processes, algorithms are used to
perform the search process.
AI is not magic. An AI system is a computer-based
information system, although it has some distinct
characteristics.
AI vs. conventional programming
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6.6 How AI differs from conventional
computing
AI vs. Conventional programming
Processing
Nature of input
Search
Explanation
Major interest
structure
Nature of output
Maintenance and update
Hardware
Reasoning capability
Fall, 2007
Primarily symbolic
Can be incomplete
Heuristics(mostly)
Provided
Knowledge
Separation of control from
knowledge
Can be incomplete
Easy because of modularity
Mainly workstations and personal
computers
Limited, but improving
Primarily algorithm
Must be complete
Algorithm
Usually not provided
data, information
Control integrated with information
(data)
Must be complete
usually difficult
All types
None
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6.7 Does A Computer really Thinking?




Knowledge base and search technique certainly make
computers more useful, but can they really make computer
more intelligent? AI specialists, computer scientists, and
others regularly debate this question.
The fact that most AI programs are implemented by search
and pattern-matching techniques leads to the conclusion
that computers are not really intelligent.
Although AI is making computers act smarter and more
powerful, the dream of building a machine that can fully
duplicate the human brain will not be realized in our
lifetime.
AI methods are valuable.
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6.8 Artificial Intelligence Field

Expert Systems are computerized advisory programs that
attempt to imitate the reasoning processes and knowledge
of experts in solving specific problems. They are used
more than any other applied AI technology. Expert systems
are of great interest to organization because of their ability
to enhance productivity and to augment workforces in
many specialty areas where human experts are becoming
increasing difficult to find and retain.
– Software:
• EXSYS (EXSYS Inc. http://www.exsysinfo.com)
• K-version (Ginesys Corp. http://www.ginesys.com)
• KnowledgePro (Knowledge Garden Inc.
http://www.kgarden.com)
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6.8 Artificial Intelligence Field

Natural Language Technology
Natural language technology gives computer users the
ability to communicate with the computer in their native
language. It allows for a conversational type of interface,
in contrast to using a programming language of computer
jargon, syntax, and commands. However, limited success
in this area is typified by current systems.
– Natural Language Processing (NLP) consists of two
sub-fields:
• Natural Language Understanding investigates
methods of enabling the computer to comprehend
instructions given in ordinary English so that
computer can understand people more easily.
• Natural Language Generation strives to have
computers produce ordinary English language so
that people can understand computer are easily.
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6.8 Artificial Intelligence Field



Speech (voice) Understanding
is the recognition and understanding by a computer of
spoken language. (Next chapter will be talked in detail)
Robotics and Sensory Systems
– Sensory Systems, such as vision systems, tactile
systems, and signal processing systems, when combined
with AI, define a broad category of systems generally
called Robotics.
– A robot is an electromechanical device that can be
programmed to perform manual tasks.
– Not all of robotics is considered to be part of AI.
Computer Vision and Scene Recognition
– Visual recognition has been defined as the addition of
some of form of computer intelligence and decision
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6.8 Artificial Intelligence Field

making to digitized visual information received from a
machine sensor such as camera. The combined
information is then used to perform, or control, such
operations as robotic movement, convey speeds, and
producing line quality.
– The basic objective of CV is to interpret scenarios
rather than generate pictures.
Intelligent Computer-Aided Instruction (ICAI)
– Refers to machines that can tutor humans. To a certain
extent, such a machine can be viewed as an expert
system. However, the major objective of an ES is to
render advice, whereas the purpose of ICAI is to teach.
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6.8 Artificial Intelligence Field


Neural Computing
– A neural network is a mathematical model of the way a
brain functions. Details are not provided in this course.
Other Applications
– Automatic Programming-CASE
– Summarizing News
– Language Translation
– Fuzzy Logic
– Genetic Algorithm
– Intelligent Agents
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6.8 Artificial Intelligence Field

Big Picture
( see figure 6.3)
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6.9 Types of Knowledge-based DSS

The activities supported by the expert systems in this case
were different from the activities supported by the data and
model components of the DSS. Thus, the knowledge
component enabled a wide range of decisions: it extends
the capabilities of computer well beyond data-based and
model-based DSS. Other possible supports are as follows:
– Support the decisions un-addressed by mathematics.
– Support the building, storing, and managing of models
in a multiple-model DSS. Enhance the MBMS, making
it intelligence.
– Support for the uncertainty, where expertise in applying
tools ranging from fuzzy logic to neural computing.
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6.9 Types of Knowledge-based DSS
–
–
Fall, 2007
Support for user interfaces. The user interface plays a
major role in DSS implementation. For example,
natural language processing and voice techniques can
make the interfaces very easy and natural.
Others such as Intelligent Agents.
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6.10 Intelligent Decision Support Systems
Representative IDSS:
 Active (symbiotic) DSS: Regular DSS play a passive role in
human-machine interaction. The DSS executes computations,
presents data, and responds to standard commands. But it can
not play the role of an intelligent assistant to the decision
makers. This restricts the use of some DSS to well-defined and
unambiguous tasks.
– However, certain tasks in problem solving are ambiguous
and complex. Then, we need an active DSS.
– According to Mili [1990], active DSS is applicable to the
following tasks:
• Understanding the domain (terminology, parameters,
interactions) , for example, provide a explanation.
• Formulating problems. Help in determining
assumptions, abstracting reality, deciding what is
relevant, and so on.
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6.11 Intelligence DSS
Relating a problem to a solver. The active DSS can assist
with proper problem-solver interaction, advise what
procedures to use and what solution techniques to follow,
and so on.
– Interpreting results.
– Explaining results and decisions.
Self-evolving DSS.
A self-evolving DSS is an approach to DSS design, whose basic
premise is that a DSS should be “aware” of how it is being
used, and then it should automatically adapt to the evolution of
its users. This capability is achieved by adding an extra
component: an intelligent self-evolving mechanism.
– A dynamic menu provides different hierarchies to fulfill
different user requirements
– A dynamic user interface that provides different output
representation for different users
–

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6.11 Intelligence DSS
–

An intelligent model base management system can
select appropriate models to satisfy different
preferences.
Structure of Self-Evolving DSS
Major components are:
– Data management, model management, and a user
interface, which are basic components of any DSS.
– A usage record that contains system usage data
pertinent to the evolution of the system and its
management (EMS, or evolution management systems)
– User interface elements that are needed for creating a
very user-friendly interface.
– Central control mechanism that coordinates all the
operations of the DSS. This is an intelligent control that
contains a knowledge base.
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6.11 Intelligence DSS
User
Data
User
Model
Management
Interface
management
Control
Mechanism
Knowledge base
Usage
User
Record EMS
Interface
elements
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6.11 Intelligence DSS
–
Fall, 2007
The control mechanism collects the user’s usage data
and stores them in the usage record base. Then the
control mechanism analyzes these data and a new
version of the DSS is created. This process is repetitive
and continues as long as necessary. The control can
work on each component of the DSS independently. In
other words, it provides an intelligent interfaces,
intelligent model management, and intelligent data
management. Finally, the system is an online, real-time
system.
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6.11 Intelligence DSS

Problem Management
–
–
–
–
–
Fall, 2007
Most DSS revolve around the design and choice phases
of decision making. The intelligence phase, which
includes problem finding, problem representation, and
information surveillance, is neglected by most DSS.
Moreover, several activities in the design and choice
phases, such as model management, are executed
manually
To make DSS more effective, it is necessary to
automate as many tasks as possible.
Weber and Konsynski [1987] suggested dividing the
decision-making process into five steps and proposed
architecture support to the functional requirements of
these steps. They called their approach problem
management.
Table 6.3 shows it most contents.
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6.12 Problem Management
table 6.3 Problem Management: functional requirements, and architechutural support
Problem
management stage Functional Requirements
Architectural Support
Flexible knowledge
Perceptual filters, knowledge
management, intelligent
Problem finding
management
filters
Flexible dialogue and
knowledge management,
reason maintenance
Problem
Model and pattern management,
system, pattern search
representation
suspension of judgement
strategies
Demons, intelligent
Information
lenses, scanners,
serveillance
Knowledge and model management evaluators interpreters
Idea and solution model
Knowledge management, idea
management, heuristic
Solution generation generation
and analytical drives
Flexible knowledge
Meta-level dialog and knowledge
management, analytic
Solution evalution
management
and symbolic processors
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Assignments (individual)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Fall, 2007
Distinguish among data, information and
knowledge.
Define a Knowledge base, as one of component of
DSS, what can it support in DSS.
Define an AI.
What is symbolic processing.
Define an Expert System.
What is a natural language processing?
What is an intelligent DSS (IDSS)?
What is Active DSS and Self-Evolving DSS?
Explain what is problem management.
The knowledge acquisition/representation has
several methods, please list these ways.
What is the Production Rule in knowledge
representation?
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