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. • * - 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 SYMBOL SYSTEM 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 jugs • can 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 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
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 Optimization • Peanut Harvest Optimization • Medication Testing/Analysis • Snake Hunting (special math problem) • Intelligent ISs and DSSs • Weather Prediction • Robotics