Artificial Intelligence Overview

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Artificial Intelligence Overview

• John Paxton
• Montana State University
• February 22, 2005
• paxton_at_cs.montana.edu

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Montana State University
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A Brief Bio

• 1985 The Ohio State University, B.S.
• 1987 The University of Michigan, M.S.
• 1990 The University of Michigan, Ph.D.
• 1990 present MSU CS Professor

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Talk Outline

• What is AI?
• Foundations
• Areas
• Search
• Knowledge Representation
• Agents
• Questions

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What is AI?

• Scientific Approach
• Build systems that think like humans
• Build systems that act like humans
• Engineering Approach
• Build systems that think rationally
• Build systems that act rationally

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Acting Like a Human

• Turing Test (1950)

IBM
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Thinking Like a Human

• Cognitive Modeling Approach
• General Problem Solver (Newell and Simon, 1961)
• Towers of Hanoi Problem

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Thinking Rationally

• The laws-of-thought approach
• Syllogisms (Aristotle) deductive reasoning in
  which a conclusion is derived from premises
• It is difficult to code the knowledge and to
  reason with it efficiently.

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Sample Logic Puzzle

• Robinson found himself on an island where some of
  the people were liars, and others always told the
  truth. When he met with one of the inhabitant of
  the island, he asked him "Are you a liar or
  not?""I'm not a liar", answered the person."All
  right, if it is so, you'll be my companion",
  Robinson said.After a while they saw another
  man.Robinson pointed to the man and asked his
  new friend,"Could you, please, ask him, if he is
  a liar or not?"The new friend asked the question
  to the man, came back and said,"He said he was
  not a liar"."All right, now I'm convinced that
  you are not a liar!" smiled Robinson. What
  convinced Robinson?

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Acting Rationally

• Rational Agent Approach. The agent acts to
  achieve the best (or near best) expected outcome.

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Water Jug Problem
4
10
8
5 2
15 5
10 4
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Foundations

• Philosophy (e.g. Where does knowledge come from?)
• Mathematics (e.g. What are the formal rules to
  draw valid conclusions?)
• Economics (e.g. How should we make decisions to
  maximize payoff?)
• Neuroscience (e.g. How do brains process
  information?)
• Psychology (e.g. How do humans and animals think
  and act?)
• Computer Engineering (e.g. How can we build an
  efficient computer?)
• Control Theory (e.g. How can artifacts operate
  under their own control?)
• Linguistics (e.g. How does language relate to
  thought?)

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Areas

• Agents
• Artificial Life
• Machine Discovery and Data Mining
• Expert Systems
• Fuzzy Logic
• Game Playing
• Genetic Algorithms

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Areas

• Knowledge Representation
• Learning
• Neural Networks
• Natural Language Processing
• Planning
• Reasoning
• Robotics

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Areas

• Search
• Speech Recognition and Synthesis
• Virtual Reality
• Computer Vision

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Search

• Missionaries and Cannibals Problem

MMM CCC
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Search

• Missionaries and Cannibals Solution

M C
MM CC
MMM CCC
CCC
C
MMM CC
MMM
M C
MMM C
MM CC
MM CC
MMM C
M C
CC
CC
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Types of Search

• Uninformed Search
• Breadth-First Search
• Depth-First Search
• Informed Search
• Best-First Search
• A Search

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Breadth-First Search
MMM CCC
MMM C
MM CC
M C
MMM CC
CC
C
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Knowledge Representation

• Semantic Nets
• Fuzzy Logic
• First Order Predicate Calculus

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Supply the Missing Words!

• 60 M in an H
• 26 L in the A
• 12 S of the Z
• 88 P K
• 200 D for P G in M

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Semantic Nets
can-fly
yes
bird
is-a
is-a
is-a
no
robin magpie
ostrich
can-fly
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Fuzzy Logic

• Shaquille ONeal is tall

1.0 0.0
tall
50 60 70
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First Order Predicate Calculus

• Every Saturday is a weekend. ?x Saturday(x) ?
  weekend(x)
• Some day is a week day. ?x day(x) ? weekday(x)

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Agents
sensors
actuators
AGENT
ENVIRONMENT
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Rationality Factors

• Performance Measure
• Prior Knowledge
• Performable Actions
• Agents Prior Percepts

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Rational Agent

• For each possible sensor sequence, a rational
  agent should select an action that is expected to
  maximize its performance measure, given the
  evidence provided by the sensor sequence and
  whatever built-in knowledge the agent has.

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Thank you!

• Questions??