Informed Search

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Informed Search. Russell and Norvig: Ch. 4.1 - 4.3 CSMSC 421 – Fall 2006. Outline. Informed = use problem-specific knowledge Which search strategies? Best-first search and its variants Heuristic functions? How to invent them Local search and optimization

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Informed Search

Russell and Norvig: Ch. 4.1 - 4.3

CSMSC 421 – Fall 2006

Outline
• Informed = use problem-specific knowledge
• Which search strategies?
• Best-first search and its variants
• Heuristic functions?
• How to invent them
• Local search and optimization
• Hill climbing, simulated annealing, local beam search,…
Previously: Graph search algorithm

function GRAPH-SEARCH(problem,fringe) return a solution or failure

closed an empty set

fringe INSERT(MAKE-NODE(INITIAL-STATE[problem]), fringe)

loop do

if EMPTY?(fringe) then return failure

node REMOVE-FIRST(fringe)

if GOAL-TEST[problem] applied to STATE[node] succeeds

then return SOLUTION(node)

if STATE[node] is not in closedthen

fringe INSERT-ALL(EXPAND(node, problem), fringe)

A strategy is defined by picking the order of node expansion

Blind Search…
• …[the ant] knew that a certain arrangement had to be made, but it could not figure out how to make it. It was like a man with a tea-cup in one hand and a sandwich in the other, who wants to light a cigarette with a match. But, where the man would invent the idea of putting down the cup and sandwich—before picking up the cigarette and the match—this ant would have put down the sandwich and picked up the match, then it would have been down with the match and up with the cigarette, then down with the cigarette and up with the sandwich, then down with the cup and up with the cigarette, until finally it had put down the sandwich and picked up the match. It was inclined to rely on a series of accidents to achieve its object. It was patient and did not think… Wart watched the arrangements with a surprise which turned into vexation and then into dislike. He felt like asking why it did not think things out in advance… T.H. White, The Once and Future King
Search Algorithms
• Blind search – BFS, DFS, uniform cost
• no notion concept of the “right direction”
• can only recognize goal once it’s achieved
• Heuristic search – we have rough idea of how good various states are, and use this knowledge to guide our search
Best-first search
• General approach of informed search:
• Best-first search: node is selected for expansion based on an evaluation function f(n)
• Idea: evaluation function measures distance to the goal.
• Choose node which appears best
• Implementation:
• fringe is queue sorted in decreasing order of desirability.
• Special cases: Greedy search, A* search
Heuristic

Webster's Revised Unabridged Dictionary (1913) (web1913)

Heuristic \Heu*ris"tic\, a. [Greek. to discover.] Serving to discover or find out.

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

heuristic 1. 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. approximation algorithm.

From WordNet (r) 1.6

heuristic adj 1: (computer science) relating to or using a heuristic rule 2: of or relating to a general formulation that serves to guide investigation [ant: algorithmic] n : a commonsense rule (or set of rules) intended to increase the probability of solving some problem [syn: heuristic rule, heuristic program]

Informed Search
• Add domain-specific information to select the best path along which to continue searching
• Define a heuristic function, h(n), that estimates the “goodness” of a node n.
• Specifically, h(n) = estimated cost (or distance) of minimal cost path from n to a goal state.
• The heuristic function is an estimate, based on domain-specific information that is computable from the current state description, of how close we are to a goal
Greedy Best-First Search
• f(N) = h(N)  greedy best-first
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f(N) = h(N), with h(N) = Manhattan distance to the goal

f(N) = h(N), with h(N) = Manhattan distance to the goal

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Greedy Search
• f(N) = h(N) Greedy best-first
• Is it complete?
• Is it optimal?
• Time complexity?
• Space complexity?
• We kept looking at nodes closer and closer to the goal, but were accumulating costs as we got further from the initial state
• Our goal is not to minimize the distance from the current head of our path to the goal, we want to minimize the overall length of the path to the goal!
• Let g(N) be the cost of the best path found so far between the initial node and N
• f(N) = g(N) + h(N)  A*
A* search
• Best-known form of best-first search.
• Idea: avoid expanding paths that are already expensive.
• Evaluation function f(n)=g(n) + h(n) A*
• 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.
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f(N) = g(N)+h(N), with h(N) = Manhattan distance to goal

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Can we Prove Anything?
• If the state space is finite and we avoid repeated states, the search is complete, but in general is not optimal
• If the state space is finite and we do not avoid repeated states, the search is in general not complete
• If the state space is infinite, the search is in general not complete
• Let h*(N) be the true cost of the optimal path from N to a goal node
• Heuristic h(N) is admissible if: 0 h(N)  h*(N)
• An admissible heuristic is always optimistic
Optimality of A*(standard proof)
• Suppose suboptimal goal G2 in the queue.
• Let n be an unexpanded node on a shortest to optimal goal G.

f(G2 ) = g(G2 ) since h(G2 )=0

> g(G) since G2 is suboptimal

>= f(n) since h is admissible

Since f(G2) > f(n), A* will never select G2 for expansion

BUT … graph search
• Discards new paths to repeated state.
• Previous proof breaks down
• Solution:
• Add extra bookkeeping i.e. remove more expensive of two paths.
• Ensure that optimal path to any repeated state is always first followed.
• Extra requirement on h(n): consistency (monotonicity)
Consistency
• A heuristic is consistent if
• If h is consistent, we have

i.e. f(n) is nondecreasing along any path.

Optimality of A*(more useful)
• A* expands nodes in order of increasing f value
• Contours can be drawn in state space

1) nodes with f(n)

2) Some nodes on the goal

Contour (f(n)=C*).

Contour I has all

Nodes with f=fi, where

fi < fi+1.

A* search, evaluation
• Completeness: YES
• Since bands of increasing f are added
• Unless there are infinitely many nodes with f
A* search, evaluation
• Completeness: YES
• Time complexity:
• Number of nodes expanded is still exponential in the length of the solution.
A* search, evaluation
• Completeness: YES
• Time complexity: (exponential with path length)
• Space complexity:
• It keeps all generated nodes in memory
• Hence space is the major problem not time
A* search, evaluation
• Completeness: YES
• Time complexity: (exponential with path length)
• Space complexity:(all nodes are stored)
• Optimality: YES
• Cannot expand fi+1 until fi is finished.
• A* expands all nodes with f(n)< C*
• A* expands some nodes with f(n)=C*
• A* expands no nodes with f(n)>C*

Also optimally efficient (not including ties)

Memory-bounded heuristic search
• Some solutions to A* space problems (maintain completeness and optimality)
• Iterative-deepening A* (IDA*)
• Here cutoff information is the f-cost (g+h) instead of depth
• Recursive best-first search(RBFS)
• Recursive algorithm that attempts to mimic standard best-first search with linear space.
• (simple) Memory-bounded A* ((S)MA*)
• Drop the worst-leaf node when memory is full
Recursive best-first search

function RECURSIVE-BEST-FIRST-SEARCH(problem) return a solution or failure

return RFBS(problem,MAKE-NODE(INITIAL-STATE[problem]),∞)

function RFBS( problem, node, f_limit) return a solution or failure and a new f-cost limit

if GOAL-TEST[problem](STATE[node]) then return node

successors  EXPAND(node, problem)

ifsuccessors is empty then return failure, ∞

for eachsinsuccessorsdo

f [s]  max(g(s) + h(s), f [node])

repeat

best the lowest f-value node in successors

iff [best] > f_limitthen return failure, f [best]

alternative the second lowest f-value among successors

result, f [best]  RBFS(problem, best, min(f_limit, alternative))

ifresult failure then returnresult

Recursive best-first search
• Keeps track of the f-value of the best-alternative path available.
• If current f-values exceeds this alternative f-value than backtrack to alternative path.
• Upon backtracking change f-value to best f-value of its children.
• Re-expansion of this result is thus still possible.
RBFS evaluation
• RBFS is a bit more efficient than IDA*
• Still excessive node generation (mind changes)
• Like A*, optimal if h(n) is admissible
• Space complexity is O(bd).
• IDA* retains only one single number (the current f-cost limit)
• Time complexity difficult to characterize
• Depends on accuracy if h(n) and how often best path changes.
• IDA* and RBFS suffer from too little memory.
(simplified) memory-bounded A*
• Use all available memory.
• I.e. expand best leafs until available memory is full
• When full, SMA* drops worst leaf node (highest f-value)
• Like RFBS backup forgotten node to its parent
• What if all leafs have the same f-value?
• Same node could be selected for expansion and deletion.
• SMA* solves this by expanding newest best leaf and deleting oldest worst leaf.
• SMA* is complete if solution is reachable, optimal if optimal solution is reachable.
Heuristic functions
• E.g for the 8-puzzle
• Avg. solution cost is about 22 steps (branching factor +/- 3)
• Exhaustive search to depth 22: 3.1 x 1010 states.
• A good heuristic function can reduce the search process.
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Heuristic Function
• Function h(N) that estimates the cost of the cheapest path from node N to goal node.
• Example: 8-puzzle

h1(N) = number of misplaced tiles = 6

goal

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Heuristic Function
• Function h(N) that estimate the cost of the cheapest path from node N to goal node.
• Example: 8-puzzle

h2(N) = sum of the distances of

every tile to its goal position

= 2 + 3 + 0 + 1 + 3 + 0 + 3 + 1

= 13

goal

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8-Puzzle

f(N) = h1(N) = number of misplaced tiles

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8-Puzzle

f(N) = g(N) + h(N)

with h1(N) = number of misplaced tiles

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8-Puzzle

f(N) = h2(N) =  distances of tiles to goal

0+48-Puzzle

EXERCISE: f(N) = g(N) + h2(N)

with h2(N) =  distances of tiles to goal

Heuristic quality
• Effective branching factor b*
• Is the branching factor that a uniform tree of depth d would have in order to contain N+1 nodes.
• Measure is fairly constant for sufficiently hard problems.
• Can thus provide a good guide to the heuristic’s overall usefulness.
• A good value of b* is 1.
Heuristic quality and dominance
• 1200 random problems with solution lengths from 2 to 24.
• If h2(n) >= h1(n) for all n (both admissible)

then h2 dominates h1 and is better for search

• Admissible heuristics can be derived from the exact solution cost of a relaxed version of the problem:
• Relaxed 8-puzzle for h1 : a tile can move anywhere

As a result, h1(n) gives the shortest solution

• Relaxed 8-puzzle for h2 : a tile can move to any adjacent square.

As a result, h2(n) gives the shortest solution.

The optimal solution cost of a relaxed problem is no greater than the optimal solution cost of the real problem.

Another approach… Local Search
• Previously: systematic exploration of search space.
• Path to goal is solution to problem
• YET, for some problems path is irrelevant.
• E.g 8-queens
• Different algorithms can be used:Local Search
• Simulated Annealing
• Genetic Algorithms, others…
• Also applicable to optimization problems
• systematic search doesn’t work
Local search and optimization
• Local search= use single current state and move to neighboring states.
• Use very little memory
• Find often reasonable solutions in large or infinite state spaces.
• Are also useful for pure optimization problems.
• Find best state according to some objective function.
Hill-climbing search
• “is a loop that continuously moves in the direction of increasing value”
• It terminates when a peak is reached.
• Hill climbing does not look ahead of the immediate neighbors of the current state.
• Hill-climbing chooses randomly among the set of best successors, if there is more than one.
• Hill-climbing a.k.a. greedy local search
• Some problem spaces are great for hill climbing and others are terrible.
Hill-climbing search

function HILL-CLIMBING(problem) return a state that is a local maximum

input:problem, a problem

local variables: current, a node.

neighbor, a node.

current  MAKE-NODE(INITIAL-STATE[problem])

loop do

neighbor a highest valued successor of current

if VALUE[neighbor] ≤ VALUE[current] then return STATE[current]

current neighbor

Local-minimum problem

f(N) = h(N) = straight distance to the goal

Drawbacks of hill climbing
• Problems:
• Local Maxima: peaks that aren’t the highest point in the space
• Plateaus: the space has a broad flat region that gives the search algorithm no direction (random walk)
• Ridges: flat like a plateau, but with dropoffs to the sides; steps to the North, East, South and West may go down, but a combination of two steps (e.g. N, W) may go up.
• Remedy:
• Introduce randomness
Hill-climbing variations
• Stochastic hill-climbing
• Random selection among the uphill moves.
• The selection probability can vary with the steepness of the uphill move.
• First-choice hill-climbing
• Stochastic hill climbing by generating successors randomly until a better one is found.
• Random-restart hill-climbing
• Tries to avoid getting stuck in local maxima.
• If at first you don’t succeed, try, try again…
Simulated annealing
• Escape local maxima by allowing “bad” moves.
• Idea: but gradually decrease their size and frequency.
• Origin; metallurgical annealing
• Bouncing ball analogy:
• Shaking hard (= high temperature).
• Shaking less (= lower the temperature).
• If T decreases slowly enough, best state is reached.
• Applied for VLSI layout, airline scheduling, etc.
Simulated annealing

function SIMULATED-ANNEALING( problem, schedule) return a solution state

input:problem, a problem

schedule, a mapping from time to temperature

local variables: current, a node.

next, a node.

T, a “temperature” controlling the probability of downward steps

current  MAKE-NODE(INITIAL-STATE[problem])

for t  1 to ∞ do

T  schedule[t]

ifT = 0then returncurrent

next a randomly selected successor of current

∆E VALUE[next] - VALUE[current]

if∆E > 0 then current next

elsecurrent next only with probability e∆E /T

Simulated Annealing
• applet
• Successful application: circuit routing, traveling sales person (TSP)
Local beam search
• Keep track of k states instead of one
• Initially: k random states
• Next: determine all successors of k states
• If any of successors is goal  finished
• Else select k best from successors and repeat.
• Major difference with random-restart search
• Information is shared among k search threads.
• Can suffer from lack of diversity.
• Stochastic variant: choose k successors at proportionally to state success.
When to Use Search Techniques?
• The search space is small, and
• There is no other available techniques, or
• It is not worth the effort to develop a more efficient technique
• The search space is large, and
• There is no other available techniques, and
• There exist “good” heuristics
Summary: Informed Search
• Heuristics
• Best-first Search Algorithms
• Greedy Search
• A*