informed search next time: search application reading: machine translation paper under links...
Post on 22-Dec-2015
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Informed Search
Next time: Search Application
Reading: Machine Translation paper under Links
Username and password will be mailed to class
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Questions on the HW?
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Agenda
A* example Hill climbing
Example: n-Queens Online search
Depth first Example: maze
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A* Search OPEN = start node; CLOSED = empty While OPEN is not empty do
Remove leftmost state from OPEN, call it X
If X = goal state, return success Put X on CLOSED
SUCCESSORS = Successor function (X) Remove any successors on OPEN or CLOSED Compute f(n)= g(n) + h(n) Put remaining successors on either end of OPEN Sort nodes on OPEN by value of heuristic function
End while
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Heuristics for other problems
Problems
Shortest path from one city to another
Challenge: Is there an admissable heuristic for sodoku?
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Romania with step costs in km
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Local Search Algorithms
Operate using a single current state
Move only to neighbors of the state
Paths followed by search are not retained
Iterative improvement Keep a single current state and try to improve
it
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Advantages to local search
Use very little memory – usually a constant amount
Can often find reasonable solutions in large or infinite state spaces (e.g., continuous)
Unsuitable for systematic search
Useful for pure optimatization problems Find the best state according to an objective
function Traveling salesman
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Hill-climbing aka gradient ascent/descent; steepest ascent Function Hill-Climbing(problem)
Returns a local maximum Inputs: problem Local variables: current (a node)
neighbor (a node)
Current <- make-node(initial-state[problem]) Loop do
Neighbor <- highest valued successor of current If value(neighbor) <- value(current) return state(current) current <- neighbor
End.
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What we think hill-climbinglooks like
What we learn hill-climbing isUsually like
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Problems for hill climbing
When the higher the heuristic function the better: maxima (objective fns); when the lower the function the better: minima (cost fns)
Local maxima: A local maximum is a peak that is higher than each of its neighboring states, but lower than the global maximum
Ridges: a sequence of local maxima Plateaux: an area of the state space landscape
where the evaluation function is flat
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Hill-climbing search: 8-queens problem
A local minimum with h = 1
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Some solutions
Stochastic hill-climbing Chose at random from among the uphill moves
First-choice hill climbing Generates successors randomly until one is
generated that is better than current state Simulated annealing
Generate a random move. Accept if improvement. Otherwise accept with continually decreasing probability.
Local beam search Keep track of k states rather than just 1
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Another point of view
Wrong peak has been climbed for over 22 years According to the official Nepalese peak list the highest Trekking Peak, Mera Peak -6654m- has never been climbed. Despite having been a popular climb for over 22 years, all expeditions attacked a peak that has now been revealed as the wrong one. Both new Nepalese maps and the official peak list confirm this new information. A first attempt to climb the new peak was made in May 2000, but the peak remains unclimbed. You can read more in Rock and Ice Super Guide 104 and Climber UK Sept 2000.http://www.abc.net.au/science/k2/moments/s1086384.htm
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Avoiding climbing the wrong peak
Random-restart hill climbing Keep restarting from randomly generated
initial states, stopping when goal is found
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ROOMBA
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Online Search
Agent operates by interleaving computation and action
No time for thinking The agent only knows
Actions (s) The step-cost function c(s,a,s’) Goal-test (s)
Cannot access the successors of a state without trying all actions
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Assumptions
Agent recognizes a state it has seen before
Actions are deterministic
Competitive ratio: Compare cost that agent actually travels with cost of the actual shortest path
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What properties of search are desirable?
Will A* work?
Expand nodes in a local order Depth first Variant of greedy search
Difference from offline search: Agent must physically backtrack Record states to which agent can backtrack
and has not yet explored
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Depth-first
OPEN = start node; CLOSED = empty While OPEN is not empty do
Remove leftmost state from OPEN, call it X
If X = goal state, return success Put X on CLOSED
SUCCESSORS = Successor function (X) Remove any successors on OPEN or CLOSED Put remaining successors on left end of OPEN
End while
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Online DFS - setup
Inputs: s’, a percept that identifies the current state
Static: result, a table indexed by action and state,
initially empty unexplored: a table that lists, for each visited
state, the actions not yet tried unbacktracked: a table that lists, for each
visited state, the backtracks not yet tried s,a: the previous state and action, initially
null
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Online DFS – the algorithm If Goal-test(s’) then return stop If s’ is a new state then unexplored[s’] actions(s’) If s is not null then do
result[a,s]s’, result[reverse(a),s’)-< s Add s to the front of unbacktracked[s’]
If unexplored[s’] is empty Then return stop Else a action b such that
result[b,s’]=pop(unbacktracked[s’]) Else apop(unexplored[s’]) s s’ Return a
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Learning Real Time A* (LRTA*)
Augment hill-climbing with memory
Store current best estimate of cost from node to goal: H(s)
Initially, H(s) = h(s)
Update H(s) through experience
Estimated cost to reach the goal through neighbor s’ H(s) = c(s,a,s’)+ H(s’)
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End of Class Questions