An expert system is an interactive computer program that uses knowledge acquired from experts to solve complex problems in a specific domain. It consists of an inference engine that applies rules and logic to the facts contained within a knowledge base in order to provide recommendations or advice to users. The first expert system was called DENDRAL and was developed in the 1970s at Stanford University to identify unknown organic molecules. Expert systems are used in applications like diagnosis, financial planning, configuration, and more to perform tasks previously requiring human expertise. They have benefits like increased productivity and quality, reduced costs and errors, and the ability to capture scarce human knowledge. However, they also have limitations such as difficulty acquiring and representing human expertise and an inability to operate outside their
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
I. Hill climbing algorithm II. Steepest hill climbing algorithmvikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Application of Expert Systems inSystem Analysis & Designfaiza nahin
Design is a field in which a large part of the processes involved is knowledge-based rather than computation-based. Much of this knowledge is experiential and as such lends itself to be encapsulated in an expert system. An analogy is made between analysis and interpretation and between evaluation and comparison of interpretations. Three examples of expert systems carrying out design analysis and evaluation in different domains are described. It is argued that a graphical interface and a model of the elements within the domain are essential parts of any design system.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
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Minmax Algorithm In Artificial Intelligence slidesSamiaAziz4
Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
Introduction to Dynamic Programming, Principle of OptimalityBhavin Darji
Introduction
Dynamic Programming
How Dynamic Programming reduces computation
Steps in Dynamic Programming
Dynamic Programming Properties
Principle of Optimality
Problem solving using Dynamic Programming
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...The Integral Worm
This presentation is an introduction to Artificial Intelligence: The Nine Phases of the Expert System Development Lifecycle (ESDLC). Topics covered are the following: problem identification phase, feasibility study phase, project planning phase, knowledge acquisition phase, knowledge representation phase, knowledge implementation phase, verification and validation, installation/transition/ training, operation/evaluation/maintenance.
I. Hill climbing algorithm II. Steepest hill climbing algorithmvikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Application of Expert Systems inSystem Analysis & Designfaiza nahin
Design is a field in which a large part of the processes involved is knowledge-based rather than computation-based. Much of this knowledge is experiential and as such lends itself to be encapsulated in an expert system. An analogy is made between analysis and interpretation and between evaluation and comparison of interpretations. Three examples of expert systems carrying out design analysis and evaluation in different domains are described. It is argued that a graphical interface and a model of the elements within the domain are essential parts of any design system.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Minmax Algorithm In Artificial Intelligence slidesSamiaAziz4
Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
Introduction to Dynamic Programming, Principle of OptimalityBhavin Darji
Introduction
Dynamic Programming
How Dynamic Programming reduces computation
Steps in Dynamic Programming
Dynamic Programming Properties
Principle of Optimality
Problem solving using Dynamic Programming
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...The Integral Worm
This presentation is an introduction to Artificial Intelligence: The Nine Phases of the Expert System Development Lifecycle (ESDLC). Topics covered are the following: problem identification phase, feasibility study phase, project planning phase, knowledge acquisition phase, knowledge representation phase, knowledge implementation phase, verification and validation, installation/transition/ training, operation/evaluation/maintenance.
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
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2. What is Expert System ?
An expert system, is an interactive computer-based
decision tool that uses both facts and heuristics to
solve difficult decision making problems, based on
knowledge acquired from an expert.
Inference engine + Knowledge = Expert system
( Algorithm + Data structures = Program
in traditional computer )
First expert system, called DENDRAL, was developed
in the early 70's at Stanford University.
3. INTRODUCTION
Expert systems are computer applications which
embody some non-algorithmic expertise for solving
certain types of problems. For example :
Diagnostic applications
Play chess
Make financial planning decisions
Configure computers
Monitor real time systems
Underwrite insurance policies
Perform many services which previously required
human expertise.
4. Expert System Shells
Many expert system s are built with products called
expert system shells. A shell is a piece of software
which contains the user interface, a format for
declarative knowledge in the knowledge base, and an
inference engine. The knowledge and system
engineers uses these shells in making expert
systems.
5. Knowledge engineer : uses the shell to build a
system for a particular problem domain.
System engineer : builds the user interface, designs
the declarative format of the knowledge base, and
implements the inference engine.
Depending on the size of the system, the knowledge
engineer and the system engineer might be the
same person.
6. Human Expert Behaviors
Recognize and formulate the problem
Solve problems quickly and properly
Explain the solution
Learn from experience
Restructure knowledge
Break rules
Determine relevance
Degrade gracefully
8. The knowledge base contains the knowledge
necessary for understanding, formulating,
and solving problems.
Two Basic Knowledge Base Elements
Facts & Special heuristics, or rules that
direct the use of knowledge
The Inference Engine, brain of the ES.
The control structure (rule interpreter)
Provides methodology for reasoning
10. The Client Interface processes requests for service
from system-users and from application layer
components.
The Knowledge-base Editor is a simple editor that
enable a subject matter expert to compose and add
rules to the Knowledge-base.
Rule Translator converts rules from one form to
another i.e; their original form to a machine-readable
form.
The Rule Engine(inference engine) is responsible for
executing Knowledge-base rules.
The shell component, Rule Object Classes, is a
container for object classes supporting.
12. Components and Interfaces
User interface : The code that controls the dialog
between the user and the system.
Knowledge base : A declarative representation of
the expertise often in IF THEN rules .
Inference engine : The code at the core of the
system which derives recommendations from the
knowledge base and problem specific data in
working storage.
Working storage : The data which is specific to a
problem being solved.
13. Roles of Individuals who interact
with the system
Domain expert : The individuals who currently are
experts in solving the problems; here the system is
intended to solve.
Knowledge engineer : The individual who
encodes the expert's knowledge in a declarative
form that can be used by the expert system.
User : The individual who will be consulting with the
system to get advice which would have been
provided by the expert.
System engineer : builds the user interface, designs
the declarative format of the knowledge base, and
implements the inference engine.
14. Expert System Benefits
Increased Output and Productivity
Decreased Decision Making Time
Increased Process and Product
Quality
Reduced Downtime
Capture Scarce Expertise
Flexibility
Easier Equipment Operation
Elimination of Expensive
Equipment
15. Operation in Hazardous Environments
Accessibility to Knowledge and Help Desks
Integration of Several Experts' Opinions
Can Work with Incomplete or Uncertain
Information
Provide Training
Enhancement of Problem Solving and
Decision Making
Improved Decision Making Processes
Improved Decision Quality
Ability to Solve Complex Problems
Knowledge Transfer to Remote Locations
Enhancement of Other MIS
16. Expert System Limitations
Knowledge is not always readily available
Expertise can be hard to extract from
humans
Each expert’s approach may be different,
yet correct
Hard, even for a highly skilled expert, to
work under time pressure
Expert system users have natural
cognitive limits
ES work well only in a narrow domain of
knowledge
17. Most experts have no independent means
to validate their conclusions
Experts’ vocabulary often limited and
highly technical
Knowledge engineers are rare and
expensive
Lack of trust by end-users
Knowledge transfer subject to a host of
perceptual and judgmental biases
ES may not be able to arrive at valid
conclusions
ES sometimes produce incorrect
recommendations
18. References
"Artificial Intelligence", by Elaine Rich and Kevin
Knight, (2006), McGraw Hill
"Introduction To Artificial Intelligence & Expert
Systems", by Dan W Patterson.
"Expert Systems: Introduction To First And Second
Generation And Hybrid
Knowledge Based Systems", by Chris Nikolopoulos.
"Artificial intelligence and expert systems for
engineers", by C. S. Krishnamoorthy.
S. Rajeev, (1996), CRC Press INC, page 1-293.