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Artificial Intelligence

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Artificial Intelligence

Search Problem (2)

Oracle path

Uninformed and informed searches

Since we know what the goal state is like, is it possible get there faster?

Breadth-first search

Heuristics means choosing branches in a state space (when no exact solution available as in medical diagnostic or computational cost very high as in chess) that are most likely to be acceptable problem solution.

- So far, have assumed that no nongoal state looks better than another
- Unrealistic
- Even without knowing the road structure, some locations seem closer to the goal than others
- Some states of the 8s puzzle seem closer to the goal than others

- Makes sense to expand closer-seeming nodes first

Merriam-Webster's Online Dictionary

Heuristic (pron. \hyu-’ris-tik\): adj. [from Greek heuriskein to discover.] involving or serving as an aid to learning, discovery, or problem-solving by experimental and especially trial-and-error methods

The Free On-line Dictionary of Computing (15Feb98)

heuristic 1. <programming> A rule of thumb, simplification or educated guess that reduces or limits the search for solutions in domains that are difficult and poorly understood. Unlike algorithms, heuristics do not guarantee feasible solutions and are often used with no theoretical guarantee. 2. <algorithm> approximation algorithm.

- Let evaluation function h(n) (heuristic)
- h(n)= estimated cost of the cheapest path from node n to goal node.
- If n is goal thenh(n)=0

G

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- Define f(T) = the straight-line distance from T to G

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The estimate

can be wrong!

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- Imagine the problem of finding a route on a road map and that the NET below is the road map:

- g(n) = cost from the initial state to the current state n
- h(n) = estimated cost of the cheapest path from node n to a goal node
- f(n) = evaluation function to select a node for expansion (usually the lowest cost node)

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London

- Estimate of path cost h
- From state to nearest solution
- h(state) >= 0
- h(solution) = 0

- Example: straight line distance
- As the crow flies in route finding

- Where does h come from?
- maths, introspection, inspection or programs (e.g. ABSOLVER)

Liverpool

Leeds

135

Nottingham

155

75

Peterborough

120

f2

f1

= 4

= 4

1

1

3

3

2

2

- f2(T) = number or incorrectly placed tiles on board:
- gives (rough!) estimate of how far we are from goal

8

8

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- f1(T) = the number correctly placed tiles on the board:

Most often, ‘distance to goal’ heuristics are more useful !

f2

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- f3(T) = the sum of ( the horizontal + vertical distance that each tile is away from its final destination):
- gives a better estimate of distance from the goal node

= 1 + 1 + 2 + 2 = 6

= v( ) + v( )

+ v( ) + v( )

- v( ) - v( )

- F(T) = (Value count of black pieces) - (Value count of white pieces)

f

- It evaluate the performance of the different heuristics for solving the problem.
f(n) = g(n) + h(n)

- Where f is the evaluation function
- G(n) the actual length of the path from state n to start state
- H(n) estimate the distance from state n to the goal

- Best-first search
- Greedy best-first search
- A* search
- Hill-climbing search
- Genetic algorithms

- Evaluation function f gives cost for each state
- Choose state with smallest f(state) (‘the best’)
- Agenda: f decides where new states are put
- Graph: f decides which node to expand next

- Many different strategies depending on f
- For uniform-cost search f = path cost
- greedy best-first search
- A* search

- Evaluation function f(n) = h(n) (heuristic)
= estimate of cost from n to goal

- Ignores the path cost
- Greedy best-first search expands the node that appears to be closest to goal

a

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g

h=2

h=4

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h=1

h=1

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h=1

h=0

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h=0

- Use as an evaluation function f(n) = h(n)
- Selects node to expand believed to be closest (hence “greedy”) to a goal node (i.e., select node with smallest f value)
- as in the example.
- Assuming all arc costs are 1, then greedy search will find goal g, which has a solution cost of 5.
- However, the optimal solution is the path to goal I with cost 3.

Input: State Space

Ouput: failure or path from a start state to a goal state.

Assumptions:

- L is a list of nodes that have not yet been examined ordered by their h value.
- The state space is a tree where each node has a single parent.
- Set L to be a list of the initial nodes in the problem.
- While L is not empty
- Pick a node n from the front of L.
- If n is a goal node
- stop and return it and the path from the initial node to n.
Else

- remove n from L.
- For each child c of n
- insert c into L while preserving the ordering of nodes in L and labelling c with its path from the initial node as well as its h value.
End for

End if

End while

Return failure

- insert c into L while preserving the ordering of nodes in L and labelling c with its path from the initial node as well as its h value.

- stop and return it and the path from the initial node to n.

- Complete?
- Not unless it keeps track of all states visited
- Otherwise can get stuck in loops (just like DFS)

- Not unless it keeps track of all states visited
- Optimal?
- No – we just saw a counter-example

- Time?
- O(bm), can generate all nodes at depth m before finding solution
- m = maximum depth of search space

- Space?
- O(bm) – again, worst case, can generate all nodes at depth m before finding solution

- Let g(n) be the sum of the edges costs from root to node n. If g(n) is our overall cost function, then the best first search becomes Uniform Cost Search, also known as Dijkstra’s single-source-shortest-path algorithm .
- Initially the root node is placed in Open with a cost of zero. At each step, the next node n to be expanded is an Open node whose cost g(n) is lowest among all Open nodes.

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- Assume an example tree with different edge costs, represented by numbers next to the edges.
Notations for this example:

generated node

expanded node

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- Complete? Yes
- Time? # of nodes with g ≤ cost of optimal solution, O(bceiling(C*/ ε)) where C* is the cost of the optimal solution
- Space? # of nodes with g ≤ cost of optimal solution, O(bceiling(C*/ ε))
- Optimal? Yes

- For artefact-only problems (don’t care about the path)
- Depends on some e(state)
- Hill climbing tries to maximise score e
- Gradient descent tries to minimise cost e (the same strategy!)

- Randomly choose a state
- Only choose actions which improve e
- If cannot improve e, then perform a random restart
- Choose another random state to restart the search from

- Only ever have to store one state (the present one)
- Can’t have cycles as e always improves

- Problem: depending on initial state, can get stuck in local maxima

1.Pick a random point in the search space

2.Consider all the neighbors of the current state

3.Choose the neighbor with the best quality and move to that state

4.Repeat 2 thru 4 until all the neighboring states are of lower quality

5.Return the current state as the solution state

- Place 8 queens on board
- So no one can “take” another

- Gradient descent search
- Throw queens on randomly
- e = number of pairs which can attack each other
- Move a queen out of other’s way
- Decrease the evaluation function

- If this can’t be done
- Throw queens on randomly again

- Looks one step ahead to determine if any successor is better than the current state; if there is, move to the best successor.
- Rule:If there exists a successor s for the current state n such that
- h(s) < h(n) and
- h(s) ≤h(t) for all the successors t of n,
then move from n to s. Otherwise, halt at n.

- Similar to Greedy search in that it uses h(), but does not allow backtracking or jumping to an alternative path since it doesn’t “remember” where it has been.

Evaluation function f(n)=h(n)+g(n)

h(n)estimated cost to goal from n

g(n)cost so far to reach n

A* uses admissible heuristics, i.e.,

h(n) ≤ h*(n) where h*(n) is the

true cost from n.

A* Search finds the optimal path

- Best-known form of best-first search.
- Idea: avoid expanding paths that are already expensive.
- Combines uniform-cost and greedy search
- Evaluation function f(n)=g(n) + h(n)
- g(n) the cost (so far) to reach the node
- h(n) estimated cost to get from the node to the goal
- f(n) estimated total cost of path through n to goal

- Implementation: Expand the node nwith minimum f(n)

straight-line distances

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D

F

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h(S-G)=10

h(A-G)=7

h(D-G)=1

h(F-G)=1

h(B-G)=10

h(E-G)=8

h(C-G)=20

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try yourself

- The graph above shows the step-costs for different paths going from the start (S) to
- the goal (G). On the right you find the straight-line distances.
- Draw the search tree for this problem. Avoid repeated states.
- Give the order in which the tree is searched (e.g. S-C-B...-G) for A* search.
- Use the straight-line dist. as a heuristic function, i.e. h=SLD, and indicate for each node visited what the value for the evaluation function, f, is.

- Complete? Yes (unless there are infinitely many nodes with f ≤ f(G) )
- Time? Exponential
- Space? Keeps all nodes in memory
- Optimal? Yes

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City Map

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- Problem with A* search
- You have to record all the nodes
- In case you have to back up from a dead-end

- A* searches often run out of memory, not time
- Use the same iterative deepening trick as IDS
- But iterate over f(state) rather than depth
- Define contours: f < 100, f < 200, f < 300 etc.

- Complete & optimal as A*, but less memory

- Find all nodes
- Where f(n) < 100
- Ignore f(n) >= 100

- Find all nodes
- Where f(n) < 200
- Ignore f(n) >= 200

- And so on…

- A successor state is generated by combining two parent states
- Start with k randomly generated states (population)
- A state is represented as a string over a finite alphabet (often a string of 0s and 1s)
- Evaluation function (fitness function). Higher values for better states.
- Produce the next generation of states by selection, crossover, and mutation

- Fitness function: number of non-attacking pairs of queens (min = 0, max = 8 × 7/2 = 28)
- 24/(24+23+20+11) = 31%
- 23/(24+23+20+11) = 29% etc

- Mutation randomly changes genes in the new offspring.
- For binary encoding we can switch a few randomly chosen bits from 1 to 0 or from 0 to 1.
Original offspring1101111000011110

Mutated offspring1100111000001110

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(START)

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(END)

40

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Uninformed

Breadth-first search

Depth-first search

Iterative deepening

Bidirectional search

Uniform-cost search

Informed

Greedy search

A* search

IDA* search

Hill climbing

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- Consider the following search problem. Assume a state is represented as an integer, that the initial state is the number 1, and that the two successors of a state n are the states 2n and 2n+1. For example, the successors of 1 are 2 and 3, the successors of 2 are 4 and 5, the successors of 3 are 6 and 7, etc. Assumes the goal state is the number 12. Consider the following heuristics for evaluating the state n where the goal state is g
- h1(n) = |n-g| & h2(n) = (g – n) if (n g) and h2 (n) = if (n >g)
- Show the search trees generated for each of the following strategies for the initial state 1 and the goal state 12, numbering the nodes in the order expanded.
- Depth-first searchb) Breadth-first search
- c) beast-first with heuristic h1d) A* with heuristic (h1+h2)
- If any of these strategies get lost and never find the goal, then show the few steps and say "FAILS"

The end!