CS B551: ELEMENTS OF
ARTIFICIAL INTELLIGENCE
1
Instructor: Kris Hauser
http://cs.indiana.edu/~hauserk
RECAP
http://www.cs.indiana.edu/classes/b551
 Brief history and philosophy of AI
 What is intelligence? Can a machine act/think
intelligently?


Turing machine, Chinese room
2
AGENDA
Problem Solving using Search
 Search Algorithms

3
EXAMPLE: 8-PUZZLE
8
2
3
4
5
1
1
2
3
7
4
5
6
6
7
8
Initial state
Goal state
State: Any arrangement of 8 numbered tiles and an empty tile on a 3x3 board
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SUCCESSOR FUNCTION: 8-PUZZLE
2
3
4
5
1
6
8
2
7
8
6
3
8
2
3
4
7
3
4
5
1
6
5
1
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SUCC(state)  subset of states
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The successor function is knowledge
about the 8-puzzle game, but it does
not tell us which outcome to use, nor to
which state of the board to apply it.
5
2
7
4
1
6
5
Across history, puzzles and games
requiring the exploration of alternatives
have been considered a challenge for
human intelligence:
 Chess originated in Persia and India
about 4000 years ago
 Checkers appear in 3600-year-old
Egyptian paintings
 Go originated in China over 3000 years
ago
So, it’s not surprising that AI uses games 6
to design and test algorithms
EXPLORING ALTERNATIVES
Problems that seem to require intelligence
usually require exploring multiple alternatives
 Search: a systematic way of exploring
alternatives

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8-QUEENS PROBLEM

State repr. 1


Any non-conflicting
placement of 0-8
queens
State repr. 2

Any placement of 8
queens
8
DEFINING A SEARCH PROBLEM
S
 State space S
 Successor function:
x  S  SUCC(x)  2S
 Initial state s0
 Goal test:
xS  GOAL?(x) =T or F
 Arc cost
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STATE GRAPH



Each state is
represented by a
distinct node
An arc (or edge)
connects a node s
to a node s’ if
s’  SUCC(s)
The state graph may
contain more than one
connected component
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SOLUTION TO THE SEARCH PROBLEM




A solution is a path
connecting the initial
node to a goal node
(any one)
The cost of a path is
the sum of the arc
costs along this path
An optimal solution is
a solution path of
minimum cost
There might be
no solution !
G
I
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PATHLESS PROBLEMS




Sometimes the path
doesn’t matter
A solution is any goal
node
Arcs represent
potential state
transformations
E.g. 8-queens,
Simplex for LPs, Map
coloring
G
I
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REPRESENTATION 1
State: any placement of 0-8
queens
 Initial state: 0 queens
 Successor function:



Goal test:


Place queen in empty square
Non-conflicting placement of
8 queens
# of states ~ 64x63x…x57 ~
3x1014
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REPRESENTATION 2
State: any placement of nonconflicting 0-8 queens in
columns starting from left
 Initial state: 0 queens
 Successor function:



Goal test:


A queen placed in leftmost
empty column such that it
causes no conflicts
Any state with 8 queens
# of states = 2057
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PATH PLANNING
What is the state space?
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FORMULATION #1
Cost of one horizontal/vertical step = 1
Cost of one diagonal step = 2
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OPTIMAL SOLUTION
This path is the shortest in the discretized state
space, but not in the original continuous space
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FORMULATION #2
Cost of one step: length of segment
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FORMULATION #2
Visibility graph
Cost of one step: length of segment
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SOLUTION PATH
The shortest path in this state space is also the
shortest in the original continuous space
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WHAT IS A STATE?

A state does:
Represent all information meaningful to the problem
at a given “instant in time” – past, present, or future
 Exist in an abstract, mathematical sense


A state DOES NOT:
Necessarily exist in the computer’s memory
 Tell the computer how it arrived at the state
 Tell the computer how to choose the next state
 Need to be a unique representation

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WHAT IS A STATE SPACE?

An abstract mathematical object


Membership should be trivially testable



E.g., the set of all permutations of (1,…,8,empty)
E.g., S = { s | s is reachable from the start state
through transformations of the successor function } is
not easily testable
Arcs should be easily generated
Again: the state space does NOT contain
information about which arc to take (or not to
take) in a given state
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5-MINUTE QUIZ

Formulate 2x2 Tic-Tac-Toe, where you play both
X and O’s actions, as a search problem. The goal
state is any state with two in a line. Assume O
goes first.
Draw entire state graph. For compactness’s sake,
eliminate symmetrical states
 Indicate initial and goal states on this graph


Suppose one side is allowed to pass. How does the
state graph change? Do you need to change
anything to the problem definition?
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EXAMPLE: 8-PUZZLE
8
2
3
4
5
1
1
2
3
7
4
5
6
6
7
8
Initial state
Goal state
State: Any arrangement of 8 numbered tiles and an empty tile on a 3x3 board
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15-PUZZLE

Introduced (?) in 1878 by Sam Loyd, who
dubbed himself “America’s greatest puzzleexpert”
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15-PUZZLE

Sam Loyd offered $1,000 of his own money to the
first person who would solve the following
problem:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
?
1
2
3
4
5
6
7
8
9
10
11
12
13
15
14
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
But no one ever won the prize !!
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HOW BIG IS THE STATE SPACE OF THE (N21)-PUZZLE?

8-puzzle  ?? states
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HOW BIG IS THE STATE SPACE OF THE (N21)-PUZZLE?
8-puzzle  9! = 362,880 states
 15-puzzle  16! ~ 2.09 x 1013 states
 24-puzzle  25! ~ 1025 states


But only half of these states are reachable from
any given state
(but you may not know that in advance)
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PERMUTATION INVERSIONS




Wlg, let the goal be:
1
5
9
13
2
6
10
14
3 4
7 8
11 12
15
A tile j appears after a tile i if either j appears on the same row as
i to the right of i, or on another row below the row of i.
For every i = 1, 2, ..., 15, let ni be the number of tiles j < i that
appear after tile i (permutation inversions)
N = n2 + n3 +  + n15 + row number of empty tile
1
3
4
5 10 7
8
9
2
6
11 12
13 14 15
n2 = 0
n5 = 0
n8 = 1
n11 = 0
n14 = 0
n3 = 0
n6 = 0
n9 = 1
n12 = 0
n15 = 0
n4 = 0
n7 = 1
n10 = 4
n13 = 0
N=7+4
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Proposition: (N mod 2) is invariant under any
legal move of the empty tile
 Proof:

Any horizontal move of the empty tile leaves N
unchanged
 A vertical move of the empty tile changes N by an
even increment ( 1  1  1  1)

s=
1
2
5
6
3
4
1
2
7
5
6 11 7
9 10 11 8
13 14 15 12
s’ =
9 10
3
4
8
13 14 15 12
N(s’) = N(s) + 3 + 1
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



Proposition: (N mod 2) is invariant under any
legal move of the empty tile
 For a goal state g to be reachable from a state
s, a necessary condition is that N(g) and N(s)
have the same parity
It can be shown that this is also a sufficient
condition
 The state graph consists of two connected
components of equal size
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SEARCHING THE STATE SPACE

It is often not feasible (or too expensive) to build
a complete representation of the state graph
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8-, 15-, 24-PUZZLES
8-puzzle  362,880 states
0.036 sec
15-puzzle  2.09 x 1013 states
~ 55 hours
24-puzzle  1025 states
> 109 years
100 millions states/sec 34
INTRACTABILITY
Constructing the full state graph is intractable
for most interesting problems
 n-puzzle: (n+1)! states
 k-queens:
kk states

Tractability of search hinges on the
ability to explore only a tiny portion of
the state graph!
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SEARCHING
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SEARCHING THE STATE SPACE
Search tree
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SEARCHING THE STATE SPACE
Search tree
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SEARCHING THE STATE SPACE
Search tree
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SEARCHING THE STATE SPACE
Search tree
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SEARCHING THE STATE SPACE
Search tree
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SEARCHING THE STATE SPACE
Search tree
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SEARCH NODES AND STATES
8
2
3
4
7
5
1
6
8
2
7
3
4
5
1
If states are allowed to be revisited,
the search tree may be infinite even
when the state space is finite
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8
2
8
2
8
3
4
7
3
4
7
3
5
1
6
5
1
6
5
4
1
2
8
2
7
3
4
6
5
1 43 6
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DATA STRUCTURE OF A NODE
8
2
3
4
7
5
1
6
STATE
PARENT-NODE
BOOKKEEPING
CHILDREN
...
Action
Right
Depth
5
Path-Cost 5
Expanded
yes
Depth of a node N
= length of path from root to N
(depth of the root = 0)
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8
NODE EXPANSION

The expansion of a node N of
the search tree consists of:
node generation  node
expansion
8 2
3
4
7
5
1
6
N
Evaluating the successor
function on STATE(N)
 Generating a child of N for
each state returned by the
function


2
8 4
2
7
3
4
7
3
5
1
6
5
1
6
8 2
3 4 7
5 1 45 6
FRINGE OF SEARCH TREE

The fringe is the set of all search nodes that
haven’t been expanded yet
8 2
3 4 7
5 1 6
8 2 7
3 4
5 1 6
8
2
3 4 7
5 1 6
8 2
3 4 7
5 1 6
8 2 7
3
4
5 1 6
8 2
3 4 7
5 1 6
Is it identical
to the set of
leaves?
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SEARCH STRATEGY
The fringe is the set of all search nodes that
haven’t been expanded yet
 The fringe is implemented as a priority queue
FRINGE

INSERT(node,FRINGE)
 REMOVE(FRINGE)


The ordering of the nodes in FRINGE defines the
search strategy
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SEARCH ALGORITHM #1
SEARCH#1
1. If GOAL?(initial-state) then return initial-state
2. INSERT(initial-node,FRINGE)
3. Repeat:
4. If empty(FRINGE) then return failure
5. N  REMOVE(FRINGE)
Expansion of N
6. s  STATE(N)
7. For every state s’ in SUCCESSORS(s)
8.
Create a new node N’ as a child of N
9.
If GOAL?(s’) then return path or goal state
10.
INSERT(N’,FRINGE)
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PERFORMANCE MEASURES
Completeness
A search algorithm is complete if it finds a
solution whenever one exists
[What about the case when no solution exists?]
 Optimality
A search algorithm is optimal if it returns a
minimum-cost path whenever a solution exists
 Complexity
It measures the time and amount of memory
required by the algorithm

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TOPICS OF NEXT 3-4 CLASSES

Blind (uninformed) Search


Heuristic (informed) Search


Little or no knowledge about how to search
How to use extra knowledge about the problem
Local Search

With knowledge about goal distribution
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RECAP

General problem solving framework
State space
 Successor function
 Goal test
 => State graph


Search is a methodical way of exploring
alternatives
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HOMEWORK
Register!
 Readings: R&N Ch. 3.4-3.5
 HW1




On OnCourse
Writing and programming
Due date: 9/6
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