# Smart Graphics: Methoden 3 Suche, Constraints

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1 Smart Graphics: Methoden 3 Suche, Constraints Vorlesung Smart Graphics LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 1 Themen heute Suchverfahren Hillclimbing Simulated Annealing Genetische Suche Constraints Formalisierung Lösungsverfahren LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 2

2 Reflection of results Design knowledge Domain knowledge Reasoning, Planning, Inferencing, Optimization, Rendering, Output, Presentation, Display User model, Preferences input LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 3 Local search algorithms In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solution. State space = set of "complete" configurations. Find configuration satisfying constraints, e.g., n- queens. In such cases, we can use local search algorithms. keep a single "current" state, try to improve it. LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 4

3 Example: n-queens Put n queens on an n n board with no two queens on the same row, column, or diagonal LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 5 Hill-climbing search "Like climbing Everest in thick fog with amnesia" LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 6

4 Hill-climbing search Problem: depending on initial state, can get stuck in local maxima LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 7 Hill-climbing search: 8-queens problem h = number of pairs of queens that are attacking each other, either directly or indirectly h = 17 for the above state LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 8

5 Hill-climbing search: 8-queens problem A local minimum with h = 1 LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 9 Simulated annealing search Idea: escape local maxima by allowing some "bad" moves but gradually decrease their frequency The algorithm employs a random search which not only accepts changes that decrease objective function f, but also some changes that increase it. The latter are accepted with a probability LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 10

6 Properties of simulated annealing search One can prove: If T decreases slowly enough, then simulated annealing search will find a global optimum with probability approaching 1 Widely used in VLSI layout, airline scheduling, etc. Adaptation of values for T is application driven. LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 11 Local beam search Keep track of k states rather than just one Start with k randomly generated states At each iteration, all the successors of all k states are generated If any one is a goal state, stop; else select the k best successors from the complete list and repeat. LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 12

7 Genetic algorithms 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 LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 13 Genetic algorithms Fitness function: number of non-attacking pairs of queens (min = 0, max = 8 7/2 = 28) 24/( ) = 31% 23/( ) = 29% etc LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 14

8 Genetic algorithms LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 15 Constraint Satisfaction Problems Constraint Satisfaction Problems (CSP) Backtracking search for CSPs Local search for CSPs LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 16

9 Constraint satisfaction problems (CSPs) Standard search problem: state is a "black box any data structure that supports successor function, heuristic function, and goal test CSP: state is defined by variables X i with values from domain D i goal test is a set of constraints specifying allowable combinations of values for subsets of variables Simple example of a formal representation language Allows useful general-purpose algorithms with more power than standard search algorithms LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 17 Example: Map-Coloring Variables WA, NT, Q, NSW, V, SA, T Domains D i = {red,green,blue} Constraints: adjacent regions must have different colors e.g., WA NT, or (WA,NT) in {(red,green),(red,blue),(green,red), (green,blue),(blue,red),(blue,green)} LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 18

10 Example: Map-Coloring Solutions are complete and consistent assignments, e.g., WA = red, NT = green,q = red,nsw = green,v = red,sa = blue,t = green LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 19 Constraint graph Binary CSP: each constraint relates two variables Constraint graph: nodes are variables, arcs are constraints LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 20

11 Varieties of CSPs Discrete variables finite domains: n variables, domain size d O(d n ) complete assignments e.g., Boolean CSPs, incl.~boolean satisfiability (NP-complete) infinite domains: integers, strings, etc. e.g., job scheduling, variables are start/end days for each job need a constraint language, e.g., StartJob StartJob 3 Continuous variables e.g., start/end times for Hubble Space Telescope observations linear constraints solvable in polynomial time by linear programming LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 21 Varieties of constraints Unary constraints involve a single variable, e.g., SA green Binary constraints involve pairs of variables, e.g., SA WA Higher-order constraints involve 3 or more variables, e.g., cryptarithmetic column constraints LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 22

12 Example: Cryptarithmetic Variables: F T U W R O X 1 X 2 X 3 Domains: {0,1,2,3,4,5,6,7,8,9} Constraints: Alldiff (F,T,U,W,R,O) O + O = R + 10 X 1 X 1 + W + W = U + 10 X 2 X 2 + T + T = O + 10 X 3 X 3 = F, T 0, F 0 LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 23 Real-world CSPs Assignment problems e.g., who teaches what class Timetabling problems e.g., which class is offered when and where? Transportation scheduling Factory scheduling Notice that many real-world problems involve real-valued variables Constraints may be preferred constraints rather than absolute LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 24

13 Standard search formulation (incremental) Let's start with the straightforward approach, then fix it States are defined by the values assigned so far Initial state: the empty assignment { } Successor function: assign a value to an unassigned variable that does not conflict with current assignment fail if no legal assignments Goal test: the current assignment is complete 1. This is the same for all CSPs 2. Every solution appears at depth n with n variables use depth-first search 3. Path is irrelevant, so can also use complete-state formulation 4. b = (n - l )d at depth l, hence n! d n leaves 5. But only d n complete assignments! LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 25 Backtracking search Variable assignments are commutative}, i.e., [ WA = red then NT = green ] same as [ NT = green then WA = red ] Only need to consider assignments to a single variable at each node b = d and there are \$d^n\$ leaves Depth-first search for CSPs with single-variable assignments is called backtracking search Backtracking search is the basic uninformed algorithm for CSPs Can solve n-queens for n 25 LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 26

14 Backtracking search LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 27 Backtracking example LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 28

15 Backtracking example LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 29 Backtracking example LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 30

16 Backtracking example LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 31 Improving backtracking efficiency General-purpose methods can give huge gains in speed: Which variable should be assigned next? In what order should its values be tried? Can we detect inevitable failure early? LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 32

17 Most constrained variable Most constrained variable: choose the variable with the fewest legal values a.k.a. minimum remaining values (MRV) heuristic or fail first Magnitude of 3 to 3000 times faster than BT LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 33 Most constraining variable Tie-breaker among most constrained variables Most constraining variable: choose the variable with the most constraints on remaining variables LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 34

18 Least constraining value Given a variable, choose the least constraining value: the one that rules out the fewest values in the remaining variables Combining these heuristics makes 1000 queens feasible LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 35 Forward checking Idea: Keep track of remaining legal values for unassigned variables Terminate search when any variable has no legal values LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 36

19 Forward checking Idea: Keep track of remaining legal values for unassigned variables Terminate search when any variable has no legal values LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 37 Forward checking Idea: Keep track of remaining legal values for unassigned variables Terminate search when any variable has no legal values LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 38

20 Forward checking Idea: Keep track of remaining legal values for unassigned variables Terminate search when any variable has no legal values LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 39 Constraint propagation Forward checking propagates information from assigned to unassigned variables, but doesn't provide early detection for all failures: NT and SA cannot both be blue! Constraint propagation repeatedly enforces constraints locally LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 40

21 Arc consistency Simplest form of propagation makes each arc consistent X Y is consistent if for every value x of X there is some allowed y LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 41 Arc consistency Simplest form of propagation makes each arc consistent X Y is consistent if for every value x of X there is some allowed y LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 42

22 Arc consistency Simplest form of propagation makes each arc consistent X Y is consistent if for every value x of X there is some allowed y If X loses a value, neighbors of X need to be rechecked LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 43 Arc consistency Simplest form of propagation makes each arc consistent X Y is consistent if for every value x of X there is some allowed y If X loses a value, neighbors of X need to be rechecked Arc consistency detects failure earlier than forward checking Can be run as a preprocessor or after each assignment LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 44

23 Arc consistency algorithm AC-3 Time complexity: O(n 2 d 3 ) LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 45 Local search for CSPs Hill-climbing, simulated annealing typically work with "complete" states, i.e., all variables assigned To apply to CSPs: allow states with unsatisfied constraints operators reassign variable values Variable selection: randomly select any conflicted variable Value selection by min-conflicts heuristic: choose value that violates the fewest constraints i.e., hill-climb with h(n) = total number of violated constraints LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 46

24 Example: 4-Queens States: 4 queens in 4 columns (4 4 = 256 states) Actions: move queen in column Goal test: no attacks Evaluation: h(n) = number of attacks Given random initial state, can solve n-queens in almost constant time for arbitrary n with high probability (e.g., n = 10,000,000) LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 47 Summary CSPs are a special kind of problem: states defined by values of a fixed set of variables goal test defined by constraints on variable values Backtracking = depth-first search with one variable assigned per node Variable ordering and value selection heuristics help significantly Forward checking prevents assignments that guarantee later failure Constraint propagation (e.g., arc consistency) does additional work to constrain values and detect inconsistencies Iterative min-conflicts is usually effective in practice LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 48

25 Literatur, Links Stuart Russell und Peter Norvig: Künstliche Intelligenz, ein moderner Ansatz, Prentice Hall (2004), München, ISBN (daraus auch wesentliche Teile der heutigen Vorlesung) LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 49

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