ARTIFICIAL INTELLIGENCECSCI/PHIL-4550/6550

(IT’S FOR REAL)

DON POTTER

Institute for Artificial Intelligenceand

Computer Science DepartmentUGA

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

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.

INTELLIGENT BEHAVIOR(or stuff people are good at)

* - Problem Solving* - Learning* - Planning* - Perception* - Language Processing* - Collecting Stuff* - Independent Action

We’re scheduling a single elimination tennis tournament with 200 players.

How many matches will we have?

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.

TURING TEST

Interrogator

Guy Girl

Replace the guy with a machine. If the interrogator can’t tell,then the machine has exhibited intelligence.

Theoretical Computer Science

•- Automata Theory

•- Complexity Theory

•- Computability Theory

AUTOMATA THEORY

•Finite Automatons

•Pushdown Automatons

•Linear Bounded Automatons

•Unbounded Automatons (aka Turning Machines, a math model of a computer)

COMPLEXITY THEORY

•Solvable Problems

•Unsolvable Problems

COMPUTABILITY THEORY

•Decidable Problems

•Undecidable Problems

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.

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

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

PHYSICAL SYMBOLSYSTEM HYPOTHESIS

Using symbol manipulation, we can achieve intelligent behavior in

machines/devices.

Newell & Simon

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)

WHAT DO WE NEED?

•Start State

•Goal State

•Representation

•Operators (recall PSSH)

* Heuristics, the good stuff

Water Jug Problem

Problem Specs:infinite water supply,no markings on the jugscan fill, transfer, and empty

Start State: Both Jugs Empty (9,0) & (4,0)

9-Gallon Jug

4-Gallon Jug

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 jugfill 4-gallon jug, empty 4-gallon jugtransfer contents (no overflow)

from 9-gall to 4-gallfrom 4-gall to 9-gall

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)

AI RESEARCH (flight analogy)

•Feathers

AI RESEARCH (flight analogy)

•Feathers

•Flapping

AI RESEARCH (flight analogy)

•Feathers

•Flapping

•Feathers & Flapping

AI RESEARCH (flight analogy)

•Feathers

•Flapping

•Feathers & Flapping

•Beak

AI RESEARCH (flight analogy)

•Feathers

•Flapping

•Feathers & Flapping

•Beak

Facts: lift, air pressure, laws of physics, etc.

RECENT PROJECTS

Aerial Spray OptimizationPeanut Harvest OptimizationMedication Testing/AnalysisSnake Hunting (special math problem)

Intelligent ISs and DSSsWeather PredictionRobotics