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Artificial Intelligence
and Searching
CPSC 315 – Programming Studio
Spring 2012
Project 2, Lecture 1
Adapted from slides of
Yoonsuck Choe
Artificial Intelligence
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Long-standing computational goal
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Field of AI very diverse
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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
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Robotics
Computer Vision
Topics in Artificial Intelligence
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Problem solving
Reasoning
Theorem Proving
Planning
Learning
Knowledge Representation
Perception
Agent Behavior
Understanding brain function and development
Optimizing
etc.
Game Playing and Search
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Game playing a long-studied topic in AI
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Seen as a proxy for how more complex reasoning can be
developed
Search
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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
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By applying known theorems, can expand the state, until reaching
the goal theorem
Should be stored concisely
Really Basic
State Search Example
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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
(a=c, b=d), a=d
Operators
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Transition from one state to another
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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
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Can only fly certain routes
Only certain theorems can be applied
Only valid moves in game
Meetings can have capacity, requirements for/against
grouping people, etc.
Search
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Examine possible states, transitions to find
goal state
Interesting problems are those too large to
explore exhaustively
Uninformed search
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Systematic strategy to explore options
Informed search
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Use domain knowledge to limit search
Game Playing
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Abstract AI problem
Nice and challenging properties
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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
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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
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In upcoming lectures, we will discuss some of
the basic methods for performing search
Project will likely focus on a deterministic
game with perfect information