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ICS-171:Lecture 2: 1
Lecture 2: Problem Solving as Search;
Uniformed Search
ICS 171, Summer 2000
ICS-171:Lecture 2: 2
Outline
•
Representing problems as search
–
state space
–
operators
–
start state
–
goal states
•
A
Search Tree
is an efficient way to represent how a search algorithm
explores the state space
•
There are a variety of specific search techniques, including
–
Depth-First Search
–
Breadth-First Search

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ICS-171:Lecture 2: 3
What do these problems have in common?
•
Find the layout of chips on a circuit board which minimize the total
length of interconnecting wires
•
Schedule which airplanes and crew fly to which cities for American,
United, British Airways, etc
•
Write a program which can play chess against a human
•
Build a system which can find human faces in an arbitrary digital image
•
Program a tablet-driven portable computer to recognize your
handwriting
•
Decrypt data which has been encrypted but you do not have the key
•
Answer
–
they can all be formulated as
search problems
ICS-171:Lecture 2: 4
Problem-Solving Agents
•
Intelligent agents can solve problems by searching a state-space
•
State space
–
the agent’s model of the world
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usually a set of discrete states
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e.g., in driving, the states in the model could be towns/cities
•
Goal State(s)
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a goal is defined as a desirable state for an agent
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For now: all goal states have utility 1, and all non-goals have utility 0
–
there may be many states which satisfy the goal
• e.g., drive to a town with a ski-resort
–
or just one state which satisfies the goal
• e.g., drive to Mammoth Mountain
•
Operators
–
operators are legal actions which the agent can take to move from
one state to another

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ICS-171:Lecture 2: 5
State Spaces and Search
•
A State-Space Representation for Search Problems
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search = “journey” through a set of states
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start at initial state S
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want to get to a goal state G
(utility of these states = 1)
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nodes represent states
–
links represent state-transitions, may have associated costs
•
A search algorithm specifies precisely how to explore the state
space to:
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find any path to G
–
find all paths to G
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find the lowest cost path to G
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We will focus on
• finding any path to any goal state G
• ignore path costs for now
ICS-171:Lecture 2: 6
Defining Search Problems
•
A statement of a Search problem has 4 components
–
1. A set of states
–
2. A set of “operators” which allow one to get from one state to
another
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3. A start state S
–
4. A set of possible goal states, or ways to test for goal states
•
Search solution consists of
–
a unique goal state G
–
a sequence of operators which transform S into a goal state G
–
(this is the
sequence of actions
the agent
would
take to maximize
the success function)
–
For now we are interested in any path from S to G
•
Representing real problems in a search framework
–
may be many ways to represent states and operators
–
key idea: represent only the relevant aspects of the problem

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