Download Expert Systems and Artificial Intelligence

Chapter 7: Expert Systems
and Artificial Intelligence
Decision Support Systems in the
21st Century, 2nd Edition
by George M. Marakas
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 1
7-1: The Concept of Expertise
Expertise: extensive knowledge in a narrow
field
Expert systems: a computer application that
employs a set of rules based on human
knowledge to solve problems that require
human expertise
Artificial Intelligence: practical mechanisms
that enable computers to simulate the
reasoning process
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 2
7-2: The Intelligence of Artificial Intelligence
How do people reason?
 Categorization
 Specific Rules
 Heuristics
 Past Experience
 Expectations
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 3
How Do Computers Reason?
Rule-based reasoning: IF-THEN statements
represent knowledge encoded as rules
Frames: representations of stereotyped
situations that are typical of some category
Case-based reasoning: adapting previous
solutions to a current problem
Pattern recognition: detecting sounds, shapes
or long sequences
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 4
Other Forms of AI
Machine learning – neural networks and
genetic algorithms
Automatic programming – mechanisms that
generate a program to do a specific task
(allows non-programmers to “program”)
Artificial life – attempts to recreate biological
phenomena within computer-based systems
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 5
7-3: The Concept and Structure of Expert Systems
Basic structure of an ES follows the generic
structure of a DSS
The knowledge base is specific to a particular
problem domain associated with the ES
The main difference between an ES and DSS
is that the ES contains knowledge acquired
from experts in the application domain
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 6
Common Expert System Architecture
Knowledge
Engineer
User
Organization
Systems
Interface
User
Interface
KE
Interface
Inference
Engine
KE
Tool Kit
Knowledge
Base
User Environment
Development Environment
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 7
The User Interface in an ES
Design of the UI focuses on human concerns
such as ease of use, reliability and reduction
of fatigue
Design should allow for a variety of methods
of interaction (input, control and query)
Mechanisms include touch screen, keypad,
light pens, voice command, hot keys
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 8
The Knowledge Base
Contains the domain-specific knowledge
acquired from the domain experts
Can consist of object descriptions, problemsolving behaviors, constraints, heuristics and
uncertainties
The success of an ES relies on the
completeness and accuracy of its knowledge
base
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 9
The Inference Engine
Here, the knowledge is put to use to produce
solutions
The engine is capable of performing
deduction or inference based on rules or facts
Also capable of using inexact or fuzzy
reasoning based on probability or pattern
matching
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 10
The Inference Control Cycle
Three steps characterize a cycle:
1. Match rules with given facts
2. Select the rule that is to be executed
3. Execute the rule by adding the deduced
fact to the working memory
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 11
Chaining
Simple methods used by most inference
engines to produce a line of reasoning
Forward chaining: the engine begins with the
initial content of the workspace and proceeds
toward a final conclusion
Backward chaining: the engine starts with a
goal and finds knowledge to support that goal
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 12
Forward Chaining Example
Suppose we have three rules:
R1: If A and B then D
R2: If B then C
R3: If C and D then E
If facts A and B are present, we infer D from R1
and infer C from R2. With D and C inferred,
we now infer E from R3.
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 13
Backward Chaining Example
The same three rules:
R1: If A and B then D
R2: If B then C
R3: If C and D then E
If E is known, then R3 implies C and D are true.
R2 thus implies B is true (from C) and R1
implies A and B are true (from D).
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 14
7-4: Designing and Building Expert Systems
Expert System Shells: generic systems that
contain reasoning mechanisms but not the
problem-specific knowledge
Early shells were cumbersome but still
allowed the user to avoid having to
completely program the system from scratch
Modern shells contain two primary modules:
a rule set builder and an inference engine
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 15
Building an Expert System
An early step is to identify the type of tasks
(interpretation, prediction, monitoring, etc.)
the system will perform
Another important step is choosing the
experts who will contribute knowledge: It is
common for one or more of these experts to
be part of the development team
Unlike more general information systems
design projects, the software tools and
hardware platform are selected very early
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 16
7-5: Evaluating the Benefits of Expert Systems
Some major benefits:
1. Increased timeliness in decision making
2. Increased productivity of experts
3. Improved consistency in decisions
4. Improved understanding
5. Improved management of uncertainty
6. Formalization of knowledge
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 17
Limitations Associated With ES
One important limitation is that expertise is
difficult to extract and encode.
Another is that human experts adapt naturally
but an ES must be recoded.
Further, human experts better recognize
when a problem is outside the knowledge
domain, but an ES may just keep working
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall
Chapter 7 - 18