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ARTIFICIAL INTELLIGENCE CSCI/PHIL-4550/6550 (IT’S FOR REAL) DON POTTER Institute for Artificial Intelligence and Computer Science Department UGA. AI @ UGA * - Originated around 1985. * - First MS degree awarded: 1988.

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slide1
ARTIFICIAL INTELLIGENCE

CSCI/PHIL-4550/6550

(IT’S FOR REAL)

DON POTTER

Institute for Artificial Intelligence

and

Computer Science Department

UGA

slide2
AI @ UGA
      • * - Originated around 1985.
      • * - First MS degree awarded: 1988.
      • * - We follow an interdisciplinary approach based on logic programming.
      • Participants: Computer Science, Philosophy, Psychology, Linguistics, Engineering, Business, Forestry
slide3
What is Artificial Intelligence anyway?

“The science of making machines do things that would require intelligence if done by people” Marvin Minsky

I like: “the science of making machines exhibit intelligent behavior”

Neither is an attempt to make a human nor some superior being.

slide4
INTELLIGENT BEHAVIOR
  • (or stuff people are good at)
          • * - Problem Solving
          • * - Learning
          • * - Planning
          • * - Perception
          • * - Language Processing
          • * - Collecting Stuff
          • * - Independent Action
slide6
We’re scheduling a single elimination tennis tournament with 200 players.

How many matches will we have?

slide7
COOL DUDES

Charles Babbage considered intelligent devices long ago. Lady Lovelace?

Alan Turing brought the notion up to date with some math foundations and a test (called the TURING TEST).

John McCarthy coined the name Artificial Intelligence.

slide8
TURING TEST

Interrogator

Guy

Girl

Replace the guy with a machine. If the interrogator can’t tell,

then the machine has exhibited intelligence.

slide9
Theoretical Computer Science
          • - Automata Theory
          • - Complexity Theory
          • - Computability Theory
slide10
AUTOMATA THEORY
  • Finite Automatons
  • Pushdown Automatons
  • Linear Bounded Automatons
  • Unbounded Automatons (aka Turning Machines, a math model of a computer)
slide11
COMPLEXITY THEORY
  • Solvable Problems
  • Unsolvable Problems
  • COMPUTABILITY THEORY
  • Decidable Problems
  • Undecidable Problems
slide12
Can a problem be solved (or can I prove that it is unsolvable)?

If it can be solved, is it easy to solve or hard to solve?

If it is easy, then develop the algorithm and solve it.

If it is hard to solve then try using artificial intelligence techniques.

slide13
HARD PROBLEMS

Search Space too big to be searched in a reasonable time by a typical (good) algorithm.

In AI, we use heuristics (rules of thumb learned via experience).

E.g., Medical Diagnosis

slide14
From PHILOSOPHY

* Logic

* Knowledge

* lots more neat stuff

From PSYCHOLOGY

* Learning

* Comprehension

* sure, more neat stuff

From LINGUISTICS

* Language

* Language Processing

* yea, more neat stuff

slide15
PHYSICAL SYMBOL

SYSTEM HYPOTHESIS

Using symbol manipulation, we can achieve intelligent behavior in machines/devices.

Newell & Simon

slide16
15-Puzzle

Water Jug Puzzle (9 & 4 want 6)

Farmer, Fox, Goat, Grain

Pick up sticks (two player, go 2nd)

Lily Pond problem

Counterfeit Coins (81, 12)

Fast Falcon (45mph)

slide17
WHAT DO WE NEED?
        • Start State
        • Goal State
        • Representation
        • Operators (recall PSSH)
        • * Heuristics, the good stuff
slide18
Water Jug Problem
  • Problem Specs:
  • infinite water supply,
  • no markings on the jugs
  • can fill, transfer, and empty
  • Start State: Both Jugs Empty (9,0) & (4,0)

9-Gallon Jug

4-Gallon Jug

slide19
Water Jug Problem
  • Start State: Both Jugs Empty (9,0) & (4,0)
  • Goal: Six Gallons in 9-Gallon Jug (9,6) (4,_)
  • Representation: (Jug ID , Gallons)
  • Operators:
  • fill 9-gallon jug, empty 9-gallon jug
  • fill 4-gallon jug, empty 4-gallon jug
  • transfer contents (no overflow)
  • from 9-gall to 4-gall
  • from 4-gall to 9-gall
slide20
Step 0: (9,0) (4,0)

Step 1: (9,9) (4,0)

Step 2: (9,5) (4,4)

Step 3: (9,5) (4,0)

Step 4: (9,1) (4,4)

Step 5: (9,1) (4,0)

Step 6: (9,0) (4,1)

Step 7: (9,9) (4,1)

Step 8: (9,6) (4,4)

slide23
AI RESEARCH (flight analogy)
  • Feathers
  • Flapping
  • Feathers & Flapping
slide24
AI RESEARCH (flight analogy)
  • Feathers
  • Flapping
  • Feathers & Flapping
  • Beak
slide25
AI RESEARCH (flight analogy)
  • Feathers
  • Flapping
  • Feathers & Flapping
  • Beak
  • Facts: lift, air pressure, laws of physics, etc.
slide26
RECENT PROJECTS
      • Aerial Spray Optimization
      • Peanut Harvest Optimization
      • Medication Testing/Analysis
      • Snake Hunting (special math problem)
      • Intelligent ISs and DSSs
      • Weather Prediction
      • Robotics
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