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HFE 451/651 Artificial Intelligence and Expert Systems -Presented By Damodar Kavya Sogra Contents Introduction Definitions of AI Approaches of AI History of AI Designing an AI system Applications of AI Expert Systems Conclusion References Questions???? Introduction

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Definitions of AI

Approaches of AI

History of AI

Designing an AI system

Applications of AI

Expert Systems






Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent.

AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think".

introduction contd


AI researchers are active in a variety of domains.

Formal Tasks (mathematics, games),

Mundane tasks (perception, robotics, natural language, common sense reasoning)

Expert tasks (financial analysis, medical diagnostics, engineering, scientific analysis, and other areas)

approaches to ai acting humanly the turing test approach
Approaches to AIActing humanly: The Turing Test approach
  • Alan Turing(1950)
  • Designed to provide a satisfactory operational definition of intelligence
  • Intelligent behavior- The ability to achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator.

The computer would need to possess

  • Natural language processing
  • Knowledge representation
  • Automated reasoning
  • Machine learning
thinking humanly the cognitive modelling approach
Thinking humanly: The Cognitive modelling approach

Determine how humans think

  • Introspection
  • Psychological experiment

Come up with precise theory of the mind and express as a computer program

  • GPS - Newall and Simon, 1961
  • Wang
thinking rationally the laws of thought approach
Thinking rationally: The laws of thought approach
  • Aristotle – “Right thinking”
  • Laws of thought govern the operation of mind – initiated the field of logic
  • Programs based on laws of thought to create intelligent systems

Main obstacles

  • Informal knowledge in terms of formal terms

- Difference between theoretical and practical approach

acting rationally the rational agent approach
Acting rationally: The rational agent approach
  • Acting so as to achieve one’s goals given one’s beliefs
  • Agent – perceives and acts
  • AI is the study and construction of agents
  • Situational awareness unlike the laws of thought approach(makes inferences)
  • Knowledge and reason to reach good decisions in a wide variety of situations


  • More general than laws of thought approach

- More open to scientific development than approaches based on human behavior or thought –clearly defined rationality

why artificial intelligence
Why Artificial Intelligence??
  • Attempts to understand intelligent entities-learn more about ourselves
  • Strives to build intelligent entities as well as understand them
  • Computers with human-level intelligence(or better) would have a huge impact on our daily life
  • Allows less or no human involvement
history of ai

History of AI

The beginnings of AI reach back before electronics, to philosophers and mathematicians such as Boole and others theorizing on principles that were used as the foundation of AI Logic.

AI really began to intrigue researchers with the invention of the computer in 1943

The technology was finally available, or so it seemed, to simulate intelligent behavior

history of ai12
History of AI
  • Warren McCulloch and Walter Pitts (1943) developed a model of artificial neurons.
  • Claude Shannon (1950), and Alan Turing (1953) developed chess programs
  • John McCarthy, Marvin Minsky, Shannon and Nathaniel Rochester - neural networks and the study of intelligence
history of ai13
History of AI
  • A big contribution to AI, again came from McCarthy in 1958 when he wrote a high level programming language called 'LISP'.
  • Allen Newell and Herbert Simon developed 'General Problem Solver‘
  • Weizenbaum's ELIZA program (1965)
  • MYCIN was developed to diagnose blood infections.
  • Many other algorithms
history of ai contd
History of AI(contd.)

AI has grown from a dozen researchers, to thousands of engineers and specialists; and from programs capable of playing checkers, to systems designed to diagnose disease.

Advanced-level computer languages, as well as computer interfaces and word-processors owe their existence to the research into artificial intelligence.

designing an ai system

Designing an AI System

Top Down Approach

2. Bottom Up Approach

Bottom Up Approach is most widely used

some facts about the human brain
Some Facts about the Human Brain
  • Human Brain is made up of Billions of cells called neurons
  • Neurons work when grouped together
  • Decisions are made by passing electrical signals
  • Neurons are devices for processing Binary digits
how binary processing works
How Binary processing works
  • Binary numbers are represented as 0 and 1or T and F
  • A decision is made from a given input in terms of 0 and 1
    • Apples are red-- is True
    • Apples are red AND oranges are purple-- is False
    • Apples are red OR oranges are purple-- is True
    • Apples are red AND oranges are NOT purple-- is also True
relevance to the human mind
Relevance to the Human Mind
  • The Human Mind works on the principle of Binary processing
  • Information is transmitted via impulses
    • Presence of impulse – True
    • Absence of impulse –False

*Logical Operation is based on two or more such signals

applications of ai
Applications Of AI
  • Banking System

- Micro Bankers High Tech Banking System

- Internet Banking

  • Medicine



  • Eliza

- The Psychotherapist

eliza computer therapist

ELIZA- computer therapist

expert systems

Expert Systems

Expert systems are computerized advisory programs that attempt to imitate the reasoning process and knowledge of experts in solving specific types of problems.





Renaissance Age

what can expert systems do

What can Expert Systems do?












knowledge engineering the discipline of building expert systems

Knowledge Engineering-the discipline of building expert systems

Knowledge Acquisition

Knowledge Elicitation

Knowledge Representation

how does it work

How does it work?

Knowledge Base

Inference Engine

A generalized Interface

when expert systems are applicable to the nature of the task

When Expert Systems are applicable to the Nature of the task?

Expert systems can do much better

Task involves reasoning and knowledge and not intuition or reflexes

Task can be done in minutes or hours

Task is concrete enough to codify

The task is commonly taught to novice in the area.

when expert systems are applicable nature of the knowledge

When expert systems are applicable Nature of the knowledge

Recognized expert exist

There is general agreement among experts

Experts are able and willing to articulate the way they approach problems.

how the system works

How the system works?

Use AI techniques

Knowledge component

Separate knowledge and control

Use inference procedures - heuristics - uncertainty

Model human expert

comparison of conventional and expert systems

Comparison of conventional and expert systems

Conventional System Expert System

Information and processing are Knowledge base is separated from processing combined in one program mechanism

May make mistakes Does not make mistakes

Changes are tedious Changes are easy

System operates only when completed System can operate even with few rules

Data processing is a repetitive process Knowledge engineering is inferential process

Algorithmic Heuristic

Representation and use of data Representation and use of knowledge

how do people reason

How do people reason?

They create categories

They use specific rules, a priori rules

They Use Heuristics --- "rules of thumb"

They use past experience --- "cases"

They use "Expectations"

how do computers reason

How do Computers Reason?

Computer models are based on models of human reasoning

They use rules A--->B--->C

They use cases

They use pattern recognition/expectations

features of expert systems

Features of Expert Systems

Deal with complex subject which normally require a considerable amount of human expertise.

Exhibit performance and high reliability

Capable of explaining and justifying solutions and recommendations.

features of expert systems contd
Features of Expert Systems(contd.)
  • Incorporate some form of Inferential reasoning.
  • Be flexible, capable of accomodating significant changes without necessary programming
  • Be user friendly
examples of expert systems
Examples of Expert Systems
  • Dendral-Identify organic compounds.
  • Mycin-diagnosing medical problems.
  • Prospector-identifying mineral deposits
  • XCON-customized hardware configuration.
  • Expert Tax- accrual and tax planning
advantages of expert systems

Advantages of Expert Systems









disadvantages of rule based expert systems

Disadvantages of Rule-Based Expert Systems



Sensory Experience


Common sense

  • Artificial Intelligence – A Modern Approach-Stuart J. Russell and Peter Norvig
  • What is Artificial Intelligence? by John McCarthy, Computer Science Department, Stanford University
  • What is Artificial Intelligence? by Aaron Sloman, Computer Science Department, University of Birmingham, UK
  • Expert Systems: A Quick Tutorial - by Schmuller, Dr. Joseph, Journal of Information Systems Education 9/92, Volume 4, Number 3
  • Artificial Intelligence a Modern Approach --- Chapter 1 Introduction by Stuart Russell and Peter Norvig.
  • AI Tutorial by Eyal Reingold, University of Toronto
  • AI Education Repository - links to classes, tutorials etc.