The document discusses the history and development of expert systems. It describes some of the earliest expert systems like DENDRAL and MYCIN, which demonstrated that expert knowledge could be encoded in a computer system. The key aspects of expert systems are discussed, including what characterizes a human expert, how expert knowledge is represented in an expert system, and the typical components of an expert system like the knowledge base, inference engine, and user interface. The roles of domain experts, knowledge engineers, and system engineers in building expert systems are also outlined.

1. Wahab Khan
Sarhad University Peshawar
Department of CS/IT

2.  Expert Systems at the forefront of AI rebirth.
 Realization in the late 60 that the general framework of
problem solving was not enough to solve all kinds of
problem.
 Realization that specialized knowledge is a very
important component of practical systems.
 People observed that systems that were designed for
well-focused problems and domains, out performed
more ‘general’ systems.
 Importance of expert systems as the earliest AI systems
and the most used systems practically.

3.  One of the pioneering systems: DENDRAL (1960’s)
 developed at Stanford for NASA
 Chemical analysis of Martian soil for space mission
 Given mass spectral data, determine molecular structure.
 In the laboratory, generate and test method used: various
possible hypothesis (molecular structures) are generated
and tested (matched to data)
 Early realization that experts use certain heuristics to
rule out certain options. Encode that knowledge in the
system
 Moral: ‘Intelligent behavior is dependent, not so much
on the methods of reasoning, but on the knowledge
one has to reason with’ Durkin

4.  MYCIN( mid 70s)
 Developed at Stanford to aid physicians in diagnosing
and treating patients with a particular blood disease.
 Motivation for MYCIN
 Few experts, availability constraints
 Immediate expertise often needed, life-threatening condition
 Tested in 1982. diagnosis on ten selected cases, along
with a panel of human experts. Scored higher than
human experts
 Importance:
 Demonstrated that expert systems could be used for solving
practical problems

5.  R1/XCON (late 80s) one of the most cited ES.
 Developed by DEC( Digital Equipment
Corporation)
 Computer configuration assistant
 One of the most successful expert systems in
routine use.
 Estimated saving is $25million per year

6.  What characterizes an ‘Expert’
 Specialized knowledge in a certain area
 Experience in the given area
 Explanation of decisions
 A skill set that enables the expert to translate the
specilized knowledge gained through experience into
solutions.
 e.g. Skin specialist, heart specialist, car mechanic,
architect, software designer.

7.  “A computer program designed to model the
problem solving ability of a human expert”
Aspects of the human expert that we wish to
model
 Knowledge
 Reasoning

8.  Knowledge-based expert systems or simply expert systems
 An expert system is software that attempts to reproduce the
performance of one or more human experts, most commonly
in a specific problem domain (Wikipedia)
 Use human knowledge to solve problems that normally
would require human intelligence
 Embody some non-algorithmic expertise
 Represent the expertise knowledge as data or rules within the
computer
 Can be called upon when needed to solve problems

9.  Example: medical ES modelling a doctor, discuss
each of the following issues in that context.
 The ES outperforms the ‘average’ doctor and is
available in regions where people may not have
access to any medical care at all otherwise.

10. Issues Human Expert Expert System
Availability Limited Always
Geographic location Locally available Anywhere
Safety
considerations
Irreplaceable Can be replaced
Durability Depends on
individual
Non-perishable
Performance Variable High
Speed Variable High
Cost High Low
Learning Ability Variable/High Low
Explanation Variable Exact

11.  Knowledge base - a declarative
representation of the expertise, often in IF
THEN rules
 Working storage - the data which is
specific to a problem being solved
 Inference engine - the code at the core of
the system
 Derives recommendations from the
knowledge base and problem-specific
data in working storage
 User interface - the code that controls the
dialog between the user and the system

12.  Domain expert – currently experts solving
the problems the system is intended to
solve
 Knowledge engineer - encodes the expert's
knowledge in a declarative form that can be
used by the expert system
 User - will be consulting with the system to
get advice which would have been provided
by the expert
 Systems built with custom developed shells
for particular applications:
 System engineer - the individual who builds
the user interface, designs the declarative
format of the knowledge base, and
implements the inference engine

13.  Shell - a piece of software which contains:
 The user interface
 A format for declarative knowledge in the knowledge base
 An inference engine
 Major advantage of a customized shell: the format of the knowledge base can be
designed to facilitate the knowledge engineering process
 Knowledge engineer and the system engineer might be the same person
 Depending on the size of the project
 One of the major bottlenecks - knowledge engineering process:
 The coding of the expertise into the declarative rule format can be a difficult and
tedious task
 The semantic gap between the expert's representation of the knowledge and the
representation in the knowledge base should be minimized

14.  Example - This rules identifies birds:
IF
family is albatross and
color is white
THEN
bird is Laysan albatross
IF
family is albatross and
color is dark
THEN
bird is black footed albatross
 The following rule is one that satisfies the family sub-goal:
IF
order is tubenose and
size large and
wings long narrow
THEN
family is albatross