1.
UNIT V
Chapter 12
Artificial Intelligence and Expert Systems

2.
Concepts and Definitions of Artificial
Intelligence
• The term has many different definitions most
experts agree that AI is concerned with two
basic ideas
• The study of human thought processes
• The representation and duplication of thought
processes in machines

3.
Concepts and Definitions of Artificial
Intelligence
• To understand AI we need to examine those abilities that are
considered to be signs of intelligence:
• Learning or understanding from experience
• Making sense out of ambiguous messages
• Responding quickly and successfully to a new situation
• Using reasoning in solving problems
• Understanding and inferring in a rational way
• Applying knowledge to manipulate environment
• Thinking and reasoning
• Recognizing and judging relative importance of different elements
in a situation
•

4.
Characteristics of AI:
• AI techniques usually have features describe below:
• 1. Symbolic processing:
• AI is a branch of science that deals with symbolic, non- algorithmic methods of
problem solving.
• This definition focuses on two characteristics
• Numeric versus symbolic
• Symbolic processing being core f AI still that doesn’t mean AI cannot use math
• Algorithmic versus heuristic
• An algorithm is a step by step process and is intended to find same solution for a
specific problem.
• Human processes are usually non-algorithmic rather human thinking relies more
on rules, opinions and gut feelings, learned from previous experiences
• 2. Heuristics
• Heuristics are intuitive knowledge learned from previous experience
• By using heuristics in AI we don’t have to rethink completely what we have if we
encounter a similar problem
• Many AI methods uses heuristics to reduce complexity of problem solving

5.
Characteristics of AI:
3) Inferencing
• AI also includes reasoning capabilities that can build higher level
knowledge using existing knowledge represented as heuristics in
the form of rules.
• Inference is the process of deriving logical outcome using set of
facts and rules
4. Machine learning
• Learning is an important capability of human being it separates
human from other creatures.
• AI have simplest learning capabilities called machine learning
• Machine learning allow computer systems to monitor and sense
environmental factors and adjust their behavior to react to changes

6.
AI versus Natural intelligence

7.
Basic Concepts of Expert Systems
Expert system are computer based systems that uses expert
knowledge to take decisions.
The basic concept of ES is to determine who experts are ,the
definition of expertise, how expertise can be extracted and
transferred from person to computer and how the expert system
should mimic the reasoning process of human experts

8.
Basic Concepts of Expert Systems
• Experts:
• An expert is a person who has special knowledge or experience in an area
and skill to put his knowledge into action to provide advice and solve
complex problem in that area.
• Its expert job to provide knowledge about how he or she performs task
that KBS will perform
• Typically human experts are capable of doing following:
• Recognizing and formulating problem
• Solving a problem quickly and correctly
• Explaining a solution
• Learning from experience
• Restructuring knowledge
• Breaking rules
• Determining relevance and associations
• Declining gracefully

9.
Basic Concepts of Expert Systems
• Expertise:
• Expertise is task specific knowledge that expert
possesses
• The level of expertise determines the performance of
decision
• Expertise is acquired through training, reading and
experience in practice
• Following is the list of possible knowledge types that
can be possessed by expert
•

10.
• ES must have following features:
• 1. Expertise
• ES must possess expertise to make expert level decision
• 2. Symbolic reasoning
• Knowledge must be represented symbolically
• 3.Deep knowledge
• Knowledge base must contain complex knowledge which
cannot be found among non-experts
• 4. Self-knowledge
• ES must be able evaluate its own reasoning and must be
able to provide proper explanation as to why a particular
conclusion was reached

11.
APPLICATION OF EXPERT SYSTEMS
• ES have been applied to may technological areas to support decision making.
• Early ES application such as DENDRAL for molecular identification and MYCIN for medical was
primarily in the science domain.
• XCON for configuration of VAX computer system
•
• DENDRAL
• It uses set of knowledge or rule based reasoning commands to deduce structure of organic
compounds from known chemical analyses
•
• MYCIN
• MYCIN is rule based ES that diagnoses bacterial infections of the blood.
• MYCIN can recognize 100 causes of bacterial infections which allow system to recommend effective
drug prescriptions.
• In a controlled test its performance was rated to be equal that of human specialist
•
• XCON
• It uses rules to determine optimal system configuration that fits customer requirements.
• The system was able to handle request in 1 minute that typically took sales team 20-30 minutes
•

12.
Newer Application of ES
• CREDIT ANALYSIS SYSTEM
• ES can help lender analyze customers credit record and determine proper credit line
• Rules in knowledge base can help assess risk and risk management policies
•
PENSION FUND ADVISORS
• This system maintains an up to date knowledge base to give participants advice concerning impact of regulation
changes and conformance with new standard
•
AUTOMATED HELP DESKS
• remedy.com is a rule based help desk solution for small business to deal with customers request efficiently .
• Incoming mails are passed on to business rule engine and messages are sent too proper technicians who resolves
problem and track issues more effectively
•
HOMELAND SECURITY
• Such systems are designed to asses terrorist threats and provide
• An assessment of vulnerability to terrorist attack
• Indicators of terrorist surveillance activities
• Guidance for managing interaction with potential terrorist
•

13.
Newer Application of ES
• MARKET SURVEILLANCE SYSTEM
• This are systems that uses rule based inference and data mining to
monitor stocks and futures markets for suspicious pattern.
•
• BUSINESS PROCESS REENGINEERING SYSTEMS
• Reengineering requires exploitation of information technology to
improve business process
• KBS are used to analysing the workflow of businesses process
reengineering
•
•

14.
STRUCTURE OF EXPERT SYSTEMS
• ES can be viewed as two environments
• Development environment- Populate knowledgebase with expert
knowledge
• Consultation Environment-To obtain advice and solve problems using
expert knowledge
• The three major components that appear in every ES are
• Knowledgebase
• Inference engine
• User interface
• In general ES that interacts with user has following additional components
• Knowledge acquisition subsystem
• Blackboard(workplace)
• Explanation subsystem
• Knowledge refining system

15.
STRUCTURE OF EXPERT SYSTEMS
• KNOWLEDGE ACQUISITION SUBSYSTEM
• Knowledge question is accumulation, transfer and transformation of expertise from experts or documented
knowledge sources into computer system.
• Acquiring knowledge from experts is complex task and is typically done but knowledge engineer who interacts
with one or more experts and builds knowledge base.
•
• KNOWLEDGEBASE
• It is the foundation of ES and contains relevant knowledge
• A typical Knowledge base include
• Facts that scribe a specific problem
• Special heuristics or rules that represent deep expert knowledge to solve the problem
•
INFERENCE ENGINE
• It is also called brain of ES.
• It is rule interpreter
• It is basically a copter program that provides methodology or reasoning about information in knowledgebase and
formulates appropriate decisions.
•
USER INTERFACE
• I provides communication user and computer
• Existing system uses graphical or textual question and answer approach to interact with user
•

16.
• BLACKBOARD(WORKPLACE)
• It’s an area of working memory set aside for
description of current problem
• Three types of decision can be recorded on
workplace
• Plan (how to attack problem)
• An agenda(potential action awaiting execution)
• Solution(courses of action system has generated
so far)

17.
• KNOWLEDGE REFINING SYSTEM
• Human experts have knowledge refining system that is
they can analyze their own knowledge and its effectiveness
and learn from it and improve it on for future consultations.
• Similarly such evaluation is necessary in expert systems too.
• The critical component of knowledge refinement system is
self-learning mechanism that allow it to adjust its
knowledgebase and its processing of knowledge based on
evaluation of its past performances.