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Logic and Artificial Intelligence ITC 461 Week 1

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Ability to perform reasoning. Ability to acquire knowledge ... Prevent from being sidetracked. Basic of AI schemes. http://crl.ucsd.edu/~saygin/papers/MMTT.pdf ... – PowerPoint PPT presentation

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Title: Logic and Artificial Intelligence ITC 461 Week 1


1
Logic and Artificial Intelligence ITC 461 Week 1
  • Overview of AI

Introduction of AI AI methodology Turing
Test History of AI Agent Approach Type of AI
Applications
2
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

3
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

4
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

5
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

6
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)

7
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.

8
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)

9
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.

10
AI Methodology
  • Based on application to develop
  • Search method
  • Rule based
  • Reasoning logic, uncertainty
  • Frame based
  • Machine learning

11
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

12
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.

13
Intelligent Agent
  • Rational action
  • IA takes the best possible action in a situation

14
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

15
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

16
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
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