Intro to AI, Search, and Game Playing
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Transcript Intro to AI, Search, and Game Playing
Artificial Intelligence
and Searching
CPSC 315 – Programming Studio
Spring 2008
Project 2, Lecture 1
Adapted from slides of
Yoonsuck Choe
Artificial Intelligence
Long-standing computational goal
Field of AI very diverse
Turing test
“Strong” AI – trying to simulate thought itself
“Weak” AI – trying to make things that behave intelligently
Several different approaches used, topics studied
Sometimes grouped with other fields
Robotics
Computer Vision
Topics in Artificial Intelligence
Problem solving
Reasoning
Theorem Proving
Planning
Learning
Knowledge Representation
Perception
Agent Behavior
Understanding brain function and development
Optimizing
etc.
Game Playing and Search
Game playing a long-studied topic in AI
Seen as a proxy for how more complex reasoning can be
developed
Search
Understanding the set of possible states, and finding the
“best” state or the best path to a goal state, or some path
to the goal state, etc.
“State” is the condition of the environment
e.g. in theorem proving, can be the state of things known
By applying known theorems, can expand the state, until reaching
the goal theorem
Should be stored concisely
Really Basic
State Search Example
Given a=b,b=c,c=d, prove a=d.
a=b, b=c, c=d
a=b, b=c, c=d
a=c
a=b, b=c, c=d
b=d
a=b, b=c, c=d
b=d, a=d
Operators
Transition from one state to another
Fly from one city to another
Apply a theorem
Move a piece in a game
Add person to a meeting schedule
Operators and states are both usually limited
by various rules
Can only fly certain routes
Only valid moves in game
Search
Examine possible states, transitions to find
goal state
Interesting problems are those too large to
explore exhaustively
Uninformed search
Systematic strategy to explore options
Informed search
Use domain knowledge to limit search
Game Playing
Abstract AI problem
Nice and challenging properties
Usually state can be clearly, concisely represented
Limited number of operations (but can still be large)
Unknown factor – account for opponent
Search space can be huge
Limit response based on time – forces making good
“decisions”
e.g. Chess averages about 35 possible moves per turn,
about 50 moves per player per game, or 35100 possible
games. But, “only” 1040 possible board states.
Types of games
Deterministic vs. random factor
Known state vs. hidden information
Examples
Deterministic
Perfect Info
Chess, Checkers, Monopoly,
Othello, Go
Backgammon
Imperfect Info Stratego,
Bridge?
Chance
Poker, Scrabble
Bridge?
Game Playing
In upcoming lectures, we will discuss some of
the basic methods for performing search
Project will focus on a deterministic game
with perfect information