R.S.P.KIRAN PAPER PRESENTATION
ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS
SRI KAVITHA ENGINEERING COLLEGE KAREPALLY
Artificial intelligence is a term that encompasses many
definitions and most experts agree that Ai is concerned with two basic ideas. First, it involves
studying the thought processes of humans, secondly it deals with representing this processes via
Ai definition also follows the behavior by machine that ,if
performed by a human being, would be called intelligence. A thought provoking definition is
provided by “Rich and knight” that is artificial intelligence is a study of how to make computers
do things at which, at the movement people are better.
The term intelligence behavior involves
1 Learning or understanding from experience.
2 Making sense out of ambiguous contradictory messages
3 Responding quickly and successfully to new situation.
4 Using reasoning in solving problems and directing conduct
5 Applying knowledge to manipulate the environment
Thinking and reasoning.
AI method involve some kind of search mechanism that focus on most
1 Inferencing: artificial intelligence involves an attempt by machines to exhibit reasoning
capability. This reasoning consists of inferencing from facts and rules using heuristics or
other search approaches. Artificial intelligence is unique in that it makes inferences by
using a pattern matching approaches.
Pattern matching: the following of AI on pattern matching techniques artificial intelligence
works with pattern matching methods such as, methods that attempt to describe objects events, or
processes in terms of their qualitative features and logical computational relationships. Expert
systems,robotics ,neural networks,Natural language processing, Speech understanding, Fuzzy
logic,Genetic algorithms ,Intelligent tutors.
Also involve fields such as game playing, computer vision,
automatic programming, machine learning. Expert system can be viewed as two environments
consultation environment and development. That development is used by an expert system
builders to build the components and put knowledge into the knowledge base. The consultation
environment is used by an non expert to take advise and expert knowledge.
The three major parts that
appear in expert systems are knowledgebase, inference engine and user interface. It also
contains additional parts such as
1 Knowledge acquisition subsystem
2 Black board (work space)
3 Explanation sub system
4 Knowledge refining system
5 Enter users
Artificial intelligence v/s Natural intelligence:
1 AI is more permanent: natural intelligence is perishable from a commercial stand point in
that workers can change their place of employment or forget information however, AI is
permanent as long as computer systems and programs remain unchanged.
2 AI offers involves ease of duplication and dissemination:
3 AI can be less expensive than natural intelligence:
4 AI can documented: Decisions made by computer can easily documented by tracing the
activities of the system. Natural intelligence is difficult to document.
5 AI, being a computer technology, is consistent and through. Natural intelligence is erratic
because people are erratic as they don’t perform consistently.
There are wide range of application of AI. They are
Expert systems,robotics ,neural networks,Natural language processing, Speech understanding,
Fuzzy logic,Genetic algorithms ,Intelligent tutors.Also involve fields such as game playing,
computer vision, automatic programming, machine learning.
Basic concept of expert system:
The basic concept of expert system
involve expertise, experts, transferring expertise, inferencing rule and explanation capability
this concepts play a major role in the aggregate development of expert systems.
1 Expertise: Expertise is the extensive, task specifying knowledge acquired from
training reading and experience it includes the following
Theories about the problem area.
Facts about the problem area
Rules and procedure regarding the general problem and its situation.
These enable experts to make better and faster decision than non experts in
solving complex problems. Expertise is usually associated with high degree of
intelligence but it is not always associated smartest person expert knowledge is
well stored, organized and quickly retrievable from an expert, expertise is usually
with vast domain of knowledge. Expertise makes people to learn from their
past success and failures.
Experts: It is difficult to define who an expert is because we can actually talk about degree or
levels of expertise. Nevertheless, it has been said that non experts out
number established experts in many fields by a ratio of 100:1. expert in the top tenth in any given
area are believed to perform three times as well as a average expert and thirty times as well as
those in lowest tenth.
Human expertise includes a consolation of a behavior that involves
Recognizing and formulating the problem
Solving the problem quickly and correctly
Learning from experience
Break rules if necessary
Transferring expertise: The objective of an expert system is to
transfer expertise from an expert to a computer system and then to other humans. This involves
four activities they are knowledge acquisition, knowledge representation, knowledge
inferencing, knowledge transferring to the users.
STRUCTURE OF EXPERT SYSTEM
Expert system can be viewed as two environments consultation
environment and development. That development is used by an expert system builders to build
the components and put knowledge into the knowledge base. The consultation environment is
used by an non expert to take advise and expert knowledge.
The three major parts that
appear in expert systems are knowledgebase, inference engine and
user interface. It also contains additional parts such as
6 Knowledge acquisition subsystem
7 Black board (work space)
8 Explanation sub system
9 Knowledge refining system
10 Enter users
Knowledge acquisition sub system: This is the accumulation, transfer and transformation of
problem solving expertise from experts or documented knowledge sources to a computer
programs for constructing and expanding the knowledge base. Acquiring knowledge from a
expert is a complex task that often creates a bottle neck in expert system construction. typically
the knowledge engineer helps the expert system structure the problem area by interpreting and
integrating human answers to questions, drawing analysis, posing counter examples
Inference engine: The brain of the expert system , and also known as the rule interpreter. This
is the component that provide a methodology for reasoning about the knowledge base and
formulating conclusions, developing the agenda that organizes and controls the steps taken to
solve problems when ever required.
User interface: Expert system contains language processor for friendly, problem oriented
communication between the user and computer. This communication can be best be carried out
in a natural language. Some times it is supplemented by menus, electronic forms and graphics.
Black Board: Is an area of working memory set a side as a data base for the description of the
current problem as specified by the input data three decision can be recorded on black board
they are a plan, a solution, an agenda.
Explanation sub system : The explanation sub system interactively answers questions such as
why was certain question asked by the expert How
was the conclusion reached.
why was a certain alternative rejected.
What is the plant reach solution
Knowledge refining system: This system analyze its own knowledge and its use, learn from it
and improve on it for future consultation. This could lead to improvement that result in more
accurate knowledge base and more effective reasoning.
The user: The user of an expert system id usually an non expert who needs adivce and training.
The user is considered as part of expert system while other people are involved in its
How an expert system work:
ES construction and use consist of three major
activities development, consultation, improvement .
1 Development : The development of an expert system
involves the construction of a problem specific knowledge base by acquiring knowledge
from knowledge base by acquiring knowledge from experts or documented sources the
knowledge is then separated into declarative and procedural then it also includes construction
of a interface engine, a blackboard, an explanation facility and any other required software,
such as interfaces.
ES shell is a tool of an used to expedite development.
Consultation: Once the system as been developed and validated it can be deployed to users the
ES conducts a bidirectional dialogue with a user asking for facts about a specific incident.
While answer is being received the Es attempts to reach the conclusion this effort is made by
inference engine , which chooses search techniques to be used to determine how the rules in
the knowledge base are to be applied to each other. The user can ask for explanations. The
quality of the interface capability is determined by the quality and completeness of the rules by
the knowledge representation used, by the power of the inference engine.
Improvement: The expert systems are improved several times through a process called rapid
prototyping. The computer keeps asking questions asked earlier and improve the knowledge
PROBLEM AREAS ADDRESSED BY EXPERT SYSTEM
Expert system address a wide range of problem areas such as
i. Interpretation systems infer situation descriptions from
observations. This category
Includes speech understanding image analysis, signal
interpretation and many kinds of intelligent analysis. An interpretation system explains
observed data by assigning them symbolic meanings describing the situation
ii. Prediction system includes weather forecasting
demographic predictions, economic forecasting, crop estimation, military marketing, and
iii. Designing system develop configuration of the objects that
satisfies the constrains of the design problems such problem include circuit layout, and plant
layout. This constructed descriptions of objects with various relationships with one another and
verify that these configurations conform to stated constraints.
iv. Debugging systems relay on planning, design, and
prediction capabilities for creating specification to correct a diagnosed problem
v. Control systems adaptively govern the over all behavior of
the system. To do this, the control system must repeatedly interpret the current situation,
predict the future, anticipate the cause of problem and formulate a remedial plan.
BENEFITS OF EXPERT SYSTEMS
Thousands of ES are in use today almost in every industry and
in every functional area. The major ES benefits are discussed
Increase output and productivity Flexibility
Easier equipment operation
Elimination of the need for expensive equipment
Knowledge transferred to remote location
Availability to solve complex problems
Ability to work with incomplete or uncertain information
Operation in hazardous environment
Improved decision quality
Enhancement of other information systems
Decrease decision making time
Increase process and product quality
Reduce down time
Capture of scare expertise
EXPERT SYSTEMS SUCCESS FACTOR
1 The level of knowledge must be sufficiently high.
2 The problem domain should be narrow
3 Knowledge base should be improved frequently
4 Management support must be cultivated
5 Es shell must be of high quality and naturally store and
manipulate the knowledge.
6 Expertise must be available from atleast one cooperative
7 The user interface must be friendly for novice user