Logic and Artificial Intelligence ITC 461 Week 1

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Logic and Artificial IntelligenceITC 461Week 1

• Overview of AI

Introduction of AI AI methodology Turing
Test History of AI Agent Approach Type of AI
Applications
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Introduction of AI

• Intelligent?
• Ability to perform reasoning
• Ability to acquire knowledge
• Ability to apply knowledge (sense)
• Ability to perceive and manipulate things
  (understand)
• Artificial?
• Not genuine
• Something made by human not occurring by nature

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Introduction of AI

• AI?
• System thinks like human
• System thinks rationally (human mind)
• System acts like human
• System acts rationally (agent)

• Branch of computer science

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Introduction of AI

• AI defines as the collection of problems and
  methodologies studied by artificial intelligence
  researchers.
• AI is the science and engineering of making
  intelligent machines, especially intelligent
  computer program

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History of AI

• The gestation of AI (1943-1955)
• Hebbian learning
• Alan Turing
• First articulated a complete version of AI in
  1950
• Article computing machinery and intelligence
• Turing test
• The birth of AI (1956)
• McCarthy gives name called AI
• Why AI?
• The idea of duplicating human faculties like
  creativity, language used, None of the field
  addressing these issues.
• Field that attempt to build machines that will
  function autonomously in complex

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History of AI

• Early enthusiasm, great expectation(1952-1969)
• Newell and Simon
• General problem solver is a program to embody the
  thinking humanly approach
• Formulate physical symbol system hypothesis for
  general intelligent action (hypothetical
  program).
• A dose of reality (1966-1973)
• Machine can think, learn and create
• Apply with human mind
• Machine evolution (now called GA)

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History of AI

• Knowledge based system (1969-1979)
• DENDRAL program (early example of weak method
  approach)
• DENDRAL-powerful because all the relevant
  theoretical knowledge to solve these problems has
  been mapped.
• Expert system eg MYCIN use cf (certainty
  factor)
• Logic (prolog)
• Frames adapted a more structured approach,
  assembling facts about particular object and
  event types.

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History of AI

• AI becomes an industry (1980-present)
• Chip design
• Human interface research
• AI winter failed in many companies to deliver
  AI in 1998
• The return of neural network (1986-present)
• Parallel distributed processing
• Connectionist model of AI
• AI becomes a science (1987-present)
• NN, data mining technology
• Revolution in robotics, knowledge representation,
  computer vision
• The emergence of intelligent agents(1995-present)
• Sensory system (vision, sonar, speech recognition)

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Problem AI can solve

• Concerns with qualitative instead of quantitative
  problem solving
• Concerns with reasoning rather than calculation
• Concerns with organizing large and varied amount
  of knowledge rather than implementing a single,
  well defined algorithm.

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AI Methodology

• Based on application to develop
• Search method
• Rule based
• Reasoning logic, uncertainty
• Frame based
• Machine learning

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Turing Test

• Paper on machine intelligence- related to modern
  digital computer
• Was written in 1950 by British mathematician
• Alan Turing mathematic view
• Published in Mind (Computing machinery and
  intelligence)
• Can machine thinks?
• How it works?
• Turing called the test as imitation game.
• The machine and a human, interrogator
• 3 important features of Turing Test
• To determine intelligence represent the
  beginning of AI
• Eliminates bias
• Prevent from being sidetracked
• Basic of AI schemes
• http//crl.ucsd.edu/saygin/papers/MMTT.pdf

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Agent- approach

• Agents are autonomous or semi-autonomous
• Agents are situated
• Agents are interactional
• The society of agent is structured
• Finally, it emergent
• Most intelligent solutions require a variety of
  agents.
• Coordination, interaction between agents
• So the main requirements for designing and
  building the society of agents
• Structures for the representation of information
• Strategies for the search through alternative
  solution
• The creation of architectures that can support
  the interaction of agents.

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Intelligent Agent

• Rational action
• IA takes the best possible action in a situation

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AI application

• Autonomous planning and scheduling
• Game playing
• chess
• Autonomous control
• ALVINN computer vision system was trained to
  steer a car to keep it following a lane
• Diagnosis
• Logistic planning
• Robotics
• Language understanding and problem solving

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AI Application Areas

• Lisp, prolog
• Game playing checkers, chess, puzzles
• 2. Automated Reasoning and Theorem Proving
  developing formal representation of languages
  such as predicate calculus- logic programming
  Prolog
• 3. Expert System
• domain specific knowledge
• Expert knowledge (theoretical understanding of
  the problem collection of heuristic problem
  solving rules)
• So expert system are constructed by obtaining
  this knowledge from a human experts and coding
  it into a form that a computer may apply to
  similar problem.
• DENDRAL, MYCIN

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AI Application Areas

• 4. Natural Language Understanding and Semantic
  Modeling
• Real understanding is depends on extensive
  background knowledge about the domain of
  discourse and the idioms used in the domain as
  well as an ability to apply general contextual
  knowledge to resolve the omissions and
  ambiguities that are normal part of human speech.
• 5. Modeling Human Performance
• 6. Planning and Robotics
• 7. Machine Learning
• 8. Neural Net and Genetic Algorithms