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Overview of Expert Systems (What is an expert system? (A particular kind…
Overview of Expert Systems
Intro to AI
What is AI?
AI is concerned with exploring such aspects of human (and other animal) mental activity as Understanding/Creativity, Perception/Problem-solving, Consciousness/Using language and Intelligence by simulating them using computers.
Expert Systems Background
AI's scientifc goal is to understand intelligence
Building computer programs that exhibit intelligent behaviour
Designed to imitate the human capabilities of thinking and sensing
Concerned with the concepts and methods of symbolic inference or reasoning by a computer and how the knowledge used to make those inferences will be represented inside the machine
ES Definition
AI programs that achieve expert-level competence in solving problems in task areas by bringing a body of knowledge about specific tasks are called knowledge-based or expert systems
The term expert systems is reserved for programs whose knowledge base contains the knowledge used by human experts.
What is knowledge?
Knowledge is the sort of information that people use to solve problems
Knowledge includes: facts, concepts, procedures, models, heuristics, exampels
Knowledge may be: specific or general, exact or fuzzy, procedural or declarative
What is an expert system?
A particular kind of knowledge-based system
One in which the knowledge, stored in the knowledge base, has been taken from an expert in some particular field
Therefore, an expert system can, to a certain extent, act as a substitute for the expert from whom the knowledge was taken
A computer system which emulates the decision-making ability of a human expert in a restricted domain (Giarratano & Riley, 1998)
"An intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solutions"
Sometimes, we also refer to knowledge-based system
Components of ES
Knowledge Base
Contains the knowledge necessary for understanding, formulating and solving problems
Contains essential information about the problem domain
Often represented as facts and rules
Knowledge is the primary raw material of ES
Inference Engine
The brain of the ES
The control structure of the rule interpreter
Mechanism to derive new knowledge from the knowledge base and the information provided by the user
Often based on the use of rules
User Interface
Interaction between the ES and users
Language processor for friendly, problem-oriented communication
NLP, or menus and graphics
Knowledge Acquisition
The accumulation, transfer and transformation of problem-solving expertise from experts and/or documented knowledge sources to a computer program for constructing or expanding the knowledge base
Requires a knowledge engineer
Knowledge Representation
Dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks
Incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalism that will make complex systems easier to design and build
e.g. KR formalisms: propositional logic, semantic nets, frames and rules
Knowledge Engineering
Used to mean the process of designing, building, installing.an expert system or other knowledge-based system
Some authors use the term to mean just the knowledge acquisition phase
Experts
An expert is an experienced practitioner in his/her particular field
More than that, he/she is a highly effective problem-solver and decision-taker in that field
Experts has three qualities:
They make good decisions
They make those decisions quickly
They are able to cope with a wide range of problems
Considering for Building an ES
Can the problem be solved effectively by conventional programming?
Is there a need and a desire for an expert system?
Is there at least one human expert who is willing to cooperate
Can the expert explain the knowledge to the knowledge engineer can understand it.
Is the problem-solving knowledge mainly heuristic and uncertain
Reasons Building an ES
To disseminate his/her knowledge so that it is available in more (possibly many more) places than the location of the expert
To ensure uniformity of advice/decisions
To archive an expert's knowledge, to insure against the day when he/she leaves or retires or dies
As a basis for training other specialists
Advantages of ES
Economical
Lower cost per user
Availability
Accessible anytime, almost anywhere
Response time
Often faster than human experts
Reliability
Can be greater than that of human experts
No distraction, fatigue, emotional involvement
Explanation
Reasoning steps that lead to a particular conclusion
Intellectual property
Can't walk out of the door
Disadvantages of ES
Developing an expert system usually costs a great deal of time & money
Historically, there has been a high failure rate in ES projects
The project may well fail during development - most likely during the "knowledge acquisition" phase
The development may succeed but the organisation may fail to accept and use the finished system
