informed search ece457 applied artificial intelligence spring 2007 lecture #3
TRANSCRIPT
Informed Search
ECE457 Applied Artificial IntelligenceSpring 2007 Lecture #3
ECE457 Applied Artificial Intelligence R. Khoury (2007) Page 2
Outline Heuristics Informed search techniques More on heuristics Iterative improvement
Russell & Norvig, chapter 4 Skip “Genetic algorithms” pages 116-
120 (will be covered in Lecture 12)
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Recall: Uninformed Search Travel blindly until they reach
Bucharest
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An Idea… It would be better if the agent knew
whether or not the city it is travelling to gets it closer to Bucharest
Of course, the agent doesn’t know the exact distance or path to Bucharest (it wouldn’t need to search otherwise!)
The agent must estimate the distance to Bucharest
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Heuristic Function More generally:
We want the search algorithm to be able to estimate the path cost from the current node to the goal
This estimate is called a heuristic function
Cannot be done based on problem formulation Need to add additional information Informed search
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Heuristic Function Heuristic function h(n)
h(n): estimated cost from node n to goal h(n1) < h(n2) means it’s probably
cheaper to get to the goal from n1
h(ngoal) = 0 Path cost g(n) Evaluation function f(n)
f(n) = g(n) Uniform Cost f(n) = h(n) Greedy Best-First f(n) = g(n) + h(n) A*
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Greedy Best-First Search f(n) = h(n) Always expand the node closest to
the goal and ignore path cost Complete only if m is finite
Rarely true in practice Not optimal
Can go down a long path of cheap actions
Time complexity = O(bm) Space complexity = O(bm)
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Greedy Best-First Search Worst case: goal is last node of the tree
Number of nodes generated:b nodes for each node of m levels (entire tree)
Time and space complexity: all generated nodes O(bm)
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A* Search f(n) = g(n) + h(n) Best-first search Complete Optimal, given admissible heuristic
Never overestimates the cost to the goal Optimally efficient
No other optimal algorithm will expand less nodes
Time complexity = O(bC*/є) Space complexity = O(bC*/є)
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A* Search Worst case: heuristic is the trivial h(n) = 0
A* becomes Uniform Cost Search Goal has path cost C*, all other actions have minimum cost of є
Depth explored before taking action C*: C*/є Number of generated nodes: O(bC*/є) Space & time complexity: all generated nodes
C* є
є є
є є
є є є є
є є
є є є є
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A* Search Using a good heuristic can reduce
time complexity Can go down to O(bm)
However, space complexity will always be exponential A* runs out of memory before running
out of time
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Iterative Deepening A* Search Like Iterative Deepening Search,
but cut-off limit is f-value instead of depth Next iteration limit is the smallest f-
value of any node that exceeded the cut-off of current iteration
Properties Complete and optimal like A* Space complexity of depth-first search Performs poorly if small action cost
(small step in each iteration)
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Simplified Memory-Bounded A* Uses all available memory When memory limit reached, delete
worst leaf node (highest f-value) If equality, delete oldest leaf node
SMA memory problem If the entire optimal path fills the
memory and there is only one non-goal leaf node
SMA cannot continue expanding Goal is not reachable
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Simplified Memory-Bounded A* Space complexity known and
controlled by system designer Complete if shallowest goal depth
less than memory size Shallowest goal is reachable
Optimal if optimal goal is reachable
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Example: Greedy Search h(n) = straight-line distance
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Example: A* Search h(n) = straight-line distance
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Heuristic Function Properties Admissible
Never overestimate the cost Consistency / Monotonicity
h(np) ≤ h(nc) + cost(np,nc)h(np) + g(np) ≤ h(nc) + cost(np,nc) + g(np)h(np) + g(np) ≤ h(nc) + g(nc)f(np) ≤ f(nc)
f(n) never decreases as we get closer to the goal
Domination h1(n) ≥ h2(n) for all n
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Creating Heuristic Functions Found by relaxing the problem Straight-line distance to Bucharest
Eliminate constraint of traveling on roads
8-puzzle Move each square that’s out of
place (7) Move by the number of squares
to get to place (12) Move some tiles in place
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Creating Heuristic Functions
Puzzle: move the red block through the exit
Action: move a block, if the path is clear A block can be moved any distance along a
clear path in one action Design a heuristic for this game
Relax by assuming that the red block can get to the exit following the path that has the fewest blocks in the way
Further relax by assuming that each block in the way requires only one action to be moved out of the way
But blocks must be moved out of the way! If there are no blank spots out of the way then another block will need to be moved
h(n) = 1 (cost of moving the red block to the exit) + 1 for each block in the way + 1 for each 2 out-of-the-way blank spots needed
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Creating Heuristic Functions
State
g(n) 0 70 82
h(n) 6 3 3
Cost 87 17 5
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Creating Heuristic Functions
State
g(n) 4 20 24
h(n) 8 7 8
Cost 83 67 63
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Path to the Goal Sometimes the path to the goal is
irrelevant Only the solution matters
n-queen puzzle
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Different Search Problem No longer minimizing path cost Improve quality of state
Minimize state cost Maximize state payoff
Iterative improvement
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Example: Iterative Improvement
Minimize cost: number of attacks
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Example: Travelling Salesman
Tree search method Start with home city Visit next city until
optimal round trip Iterative
improvement method Start with random
round trip Swap cities until
optimal round trip
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Graphic Visualisation State value / state plot: state
spaceValue
State
Global maximum
Global minimum
Local maxima
Local minima
Plateau
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Graphic Visualisation More complex state spaces can
have several dimensions Example: States are X-Y coordinates,
state value is Z coordinate
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Hill Climbing (Gradient Descent)
Simple but efficient local optimization strategy
Always take the action that most improves the state
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Hill Climbing (Gradient Descent)
Generate random initial state Each iteration
Generate and evaluate neighbours at step size
Move to neighbour with greatest increase/decrease (i.e. take one step)
End when there are no better neighbours
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Example: Travelling to Toronto
Trying to get to downtown Toronto Take steps toward the CN Tower
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Hill Climbing (Gradient Descent) Advantages
Fast No search tree
Disadvantages Gets stuck in local optimum Does not allow worse moves Solution dependant on initial state Selecting step size
Common improvements Random restarts Intelligently-chosen initial state Decreasing step size
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Simulated Annealing Problem with hill climbing: local
best move doesn’t lead to optimal goal
Solution: allow bad moves Simulated annealing is a popular
way of doing that Stochastic search method Simulates annealing process in
metallurgy
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Annealing Tempering technique in metallurgy Weakness and defects come from
atoms of crystals freezing in the wrong place (local optimum)
Heating to unstuck the atoms (escape local optimum)
Slow cooling to allow atoms to get to better place (global optimum)
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Simulated Annealing Allow some bad moves
Bad enough to get out of local optimum Not so bad as to get out of global
optimum Probability of accepting bad moves
given Badness of the move (i.e. variation in
state value V) Temperature T P = e-V/T
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Simulated Annealing Generate random initial state and high
temperature Each iteration
Generate and evaluate a random neighbour If neighbour better than current state
Accept Else (if neighbour worse than current state)
Accept with probability e-V/T
Reduce temperature End when temperature less than
threshold
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Simulated Annealing Advantages
Avoids local optima Very good at finding high-quality solutions Very good for hard problems with complex
state value functions Disadvantage
Can be very slow in practice
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Simulated Annealing Application Traveling-wave tube (TWT)
Uses focused electron beam to amplify electromagnetic communication waves
Produces high-power radio frequency (RF) signals
Critical components in deep-space probes and communication satellites Power efficiency becomes a key issue TWT research group at NASA working
for over 30 years on improving power efficiency
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Simulated Annealing Application Optimizing TWT efficiency
Synchronize electron velocity and phase velocity of RF wave
Using “phase velocity tapper” to control and decrease RF wave phase velocity
Improving tapper design improves synchronization, improves efficiency of TWT
Tapper with simulated annealing algorithm to optimize synchronization Doubled TWT efficiency More flexible then past tappers
Maximize overall power efficiency Maximize efficiency over various bandwidth Maximize efficiency while minimize signal distortion
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Assumptions Goal-based agent Environment
Fully observable Deterministic Sequential Static Discrete Single agent
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Assumptions Updated Utility-based agent Environment
Fully observable Deterministic Sequential Static Discrete / Continuous Single agent