Artificial Intelligence and Expert Systems

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Artificial Intelligence and Expert Systems
HFE 451/651

• -Presented By
• Damodar
• Kavya
• Sogra

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Contents

• Introduction
• Definitions of AI
• Approaches of AI
• History of AI
• Designing an AI system
• Applications of AI
• Expert Systems
• Conclusion
• References
• Questions????

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Introduction

• 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".

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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)

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Some definitions of AI
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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

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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

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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

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Acting rationally The rational agent approach

• Acting so as to achieve ones goals given ones
  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
• Advantages
• More general than laws of thought approach
• - More open to scientific development than
  approaches based on human behavior or thought
  clearly defined rationality

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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

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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

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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

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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

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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.

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Designing an AI System

• Top Down Approach
• 2. Bottom Up Approach
• Bottom Up Approach is most widely used

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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

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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

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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

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Network of Neurons

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Decision Making Process

• Identify a Bird

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Applications Of AI

• Banking System
• - Micro Bankers High Tech Banking System
• - Internet Banking
• Medicine
• - MYCIN
• - INTERNEST
• Eliza
• - The Psychotherapist

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ELIZA- computer therapist

• http//www.manifestation.com/neurotoys/eliza.php3

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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.

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History

• 1960s
• 1970s
• Renaissance Age

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What can Expert Systems do?

• Diagnosis
• Instruction
• Monitoring
• Analyzing
• Interpretation
• Debugging
• Repair
• Control
• Consulting
• Planning
• Design
•  

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Knowledge Engineering-the discipline of building
expert systems

• Knowledge Acquisition
• Knowledge Elicitation
• Knowledge Representation

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How does it work?

• Knowledge Base
• Inference Engine
• A generalized Interface

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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.

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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.

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How the system works?

• Use AI techniques
• Knowledge component
• Separate knowledge and control
• Use inference procedures - heuristics -
  uncertainty
• Model human expert

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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

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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"

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How do Computers Reason?

• Computer models are based on models of human
  reasoning
• They use rules A---gtB---gtC
• They use cases
• They use pattern recognition/expectations

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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.

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Features of Expert Systems(contd.)

• Incorporate some form of Inferential reasoning.
• Be flexible, capable of accomodating significant
  changes without necessary programming
• Be user friendly

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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

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Advantages of Expert Systems

• Permanence
• Reproducibility
• Efficiency
• Consistency
• Documentation
• Completeness
• Timeliness
• Differentiation

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Disadvantages of Rule-Based Expert Systems

• Creativity
• Learning
• Sensory Experience
• Degradation
• Common sense

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Conclusion Computers Think--and Often Think Like
People    
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References

• Artificial Intelligence A Modern
  Approach-Stuart J. Russell and Peter Norvig
• http//library.thinkquest.org
• http//www.ai.mit.edu/people/minsky/minsky.html
• 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.