1. CS 188: Artificial Intelligence Fall 2009
Lecture 5: CSPs II
9/10/2008
Dan Klein – UC Berkeley
Multiple slides over the course adapted from either Stuart Russell or Andrew Moore

2. Announcements
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3. Today
Efficient Solution of CSPs
Local Search

4. Reminder: CSPs CSPs: Unary Constraints Binary Constraints
Variables
Domains
Constraints
Implicit (provide code to compute)
Explicit (provide a subset of the possible tuples)
Unary Constraints
Binary Constraints
N-ary Constraints

5. DFS Example
[demo]

6. Backtracking Search
Backtracking = DFS + var-ordering + fail-on-violation
What are the choice points?
[demo: backtracking]

7. Improving Backtracking
General-purpose ideas give huge gains in speed
Ordering:
Which variable should be assigned next?
In what order should its values be tried?
Filtering: Can we detect inevitable failure early?
Structure: Can we exploit the problem structure?

8. Filtering: Forward CheckingSA NT Q
NSW V
Filtering: Forward Checking
Idea: Keep track of remaining legal values for unassigned variables (using immediate constraints)
Idea: Terminate when any variable has no legal values
[demo: forward checking animation]

9. Filtering: Constraint PropagationWA SA NT Q
NSW V
Filtering: 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!
Why didn’t we detect this yet?
Constraint propagation propagates from constraint to constraint

10. WA SA NT Q
NSW V
Consistency of An Arc
An arc X  Y is consistent iff for every x in the tail there is some y in the head which could be assigned without violating a constraint
Forward checking = Enforcing consistency of each arc pointing to the new assignment
Delete from tail!

11. Arc Consistency of a CSPWA SA NT Q
NSW V
Arc Consistency of a CSP
A simple form of propagation makes sure all arcs are consistent:
If X loses a value, neighbors of X need to be rechecked!
Arc consistency detects failure earlier than forward checking
What’s the downside of enforcing arc consistency?
Can be run as a preprocessor or after each assignment
Delete from tail!

12. Arc Consistency Runtime: O(n2d3), can be reduced to O(n2d2)
… but detecting all possible future problems is NP-hard – why?
[DEMO]

13. Limitations of Arc Consistency
After running arc consistency:
Can have one solution left
Can have multiple solutions left
Can have no solutions left (and not know it)
What went wrong here?

14. K-Consistency Increasing degrees of consistency
1-Consistency (Node Consistency): Each single node’s domain has a value which meets that node’s unary constraints
2-Consistency (Arc Consistency): For each pair of nodes, any consistent assignment to one can be extended to the other
K-Consistency: For each k nodes, any consistent assignment to k-1 can be extended to the kth node.
Higher k more expensive to compute
(You need to know the k=2 algorithm)

15. Strong K-Consistency
Strong k-consistency: also k-1, k-2, … 1 consistent
Claim: strong n-consistency means we can solve without backtracking!
Why?
Choose any assignment to any variable
Choose a new variable
By 2-consistency, there is a choice consistent with the first
By 3-consistency, there is a choice consistent with the first 2 …
Lots of middle ground between arc consistency and n-consistency! (e.g. path consistency)

16. Ordering: Minimum Remaining Values
Minimum remaining values (MRV):
Choose the variable with the fewest legal values
Why min rather than max?
Also called “most constrained variable”
“Fail-fast” ordering

17. Ordering: Least Constraining Value
Given a choice of variable:
Choose the least constraining value
The one that rules out the fewest
values in the remaining variables
Note that it may take some
computation to determine this!
Why least rather than most?
Combining these heuristics makes 1000 queens feasible
… but how do we know which is the MRV / LCV choice?

18. Problem Structure Tasmania and mainland are independent subproblems
Identifiable as connected components of constraint graph
Suppose each subproblem has c variables out of n total
Worst-case solution cost is O((n/c)(dc)), linear in n
E.g., n = 80, d = 2, c =20
280 = 4 billion years at 10 million nodes/sec
(4)(220) = 0.4 seconds at 10 million nodes/sec

19. Tree-Structured CSPs
Theorem: if the constraint graph has no loops, the CSP can be solved in O(n d2) time
Compare to general CSPs, where worst-case time is O(dn)
This property also applies to probabilistic reasoning (later): an important example of the relation between syntactic restrictions and the complexity of reasoning.

20. Tree-Structured CSPs Choose a variable as root, order
variables from root to leaves such
that every node’s parent precedes
it in the ordering
For i = n : 2, apply RemoveInconsistent(Parent(Xi),Xi)
For i = 1 : n, assign Xi consistently with Parent(Xi)
Runtime: O(n d2) (why?)

21. Tree-Structured CSPs Why does this work?
Claim: After each node is processed leftward, all nodes to the right can be assigned in any way consistent with their parent.
Proof: Induction on position
Why doesn’t this algorithm work with loops?
Note: we’ll see this basic idea again with Bayes’ nets

22. Nearly Tree-Structured CSPs
Conditioning: instantiate a variable, prune its neighbors' domains
Cutset conditioning: instantiate (in all ways) a set of variables such that the remaining constraint graph is a tree
Cutset size c gives runtime O( (dc) (n-c) d2 ), very fast for small c

23. Tree Decompositions*    
Create a tree-structured graph of overlapping subproblems, each is a mega-variable
Solve each subproblem to enforce local constraints
Solve the CSP over subproblem mega-variables using our efficient tree-structured CSP algorithm M1 M2 M3 M4 NT SA  WA Q SA  NT
NSW SA  Q Q SA 
NSW
Agree on shared vars
Agree on shared vars
Agree on shared vars
{(WA=r,SA=g,NT=b),
(WA=b,SA=r,NT=g), …}
{(NT=r,SA=g,Q=b),
(NT=b,SA=g,Q=r), …}
Agree: (M1,M2) 
{((WA=g,SA=g,NT=g), (NT=g,SA=g,Q=g)), …}

24. Iterative Algorithms for CSPs
Local search methods typically work with “complete” states, i.e., all variables assigned
To apply to CSPs:
Start with some assignment with unsatisfied constraints
Operators reassign variable values
No fringe! Live on the edge.
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

25. Example: 4-Queens States: 4 queens in 4 columns (44 = 256 states)
Operators: move queen in column
Goal test: no attacks
Evaluation: c(n) = number of attacks
[DEMO]

26. Performance of Min-Conflicts
Given random initial state, can solve n-queens in almost constant time for arbitrary n with high probability (e.g., n = 10,000,000)
The same appears to be true for any randomly-generated CSP except in a narrow range of the ratio

27. Summary CSPs are a special kind of search 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 legal 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
Constraint graphs allow for analysis of problem structure
Tree-structured CSPs can be solved in linear time
Iterative min-conflicts is usually effective in practice

28. Local Search Methods
Tree search keeps unexplored alternatives on the fringe (ensures completeness)
Local search: improve what you have until you can’t make it better
Generally much faster and more memory efficient (but incomplete)

29. Types of Search Problems
Planning problems:
We want a path to a solution (examples?)
Usually want an optimal path
Incremental formulations
Identification problems:
We actually just want to know what the goal is (examples?)
Usually want an optimal goal
Complete-state formulations
Iterative improvement algorithms

30. Hill Climbing Simple, general idea: Why can this be a terrible idea?
Start wherever
Always choose the best neighbor
If no neighbors have better scores than current, quit
Why can this be a terrible idea?
Complete?
Optimal?
What’s good about it?

31. Hill Climbing Diagram
Random restarts?
Random sideways steps?

32. Simulated Annealing
Idea: Escape local maxima by allowing downhill moves
But make them rarer as time goes on

33. Simulated Annealing Theoretical guarantee:
Stationary distribution:
If T decreased slowly enough,
will converge to optimal state!
Is this an interesting guarantee?
Sounds like magic, but reality is reality:
The more downhill steps you need to escape, the less likely you are to ever make them all in a row
People think hard about ridge operators which let you jump around the space in better ways

34. Genetic Algorithms Genetic algorithms use a natural selection metaphor
Like beam search (selection), but also have pairwise crossover operators, with optional mutation
Probably the most misunderstood, misapplied (and even maligned) technique around!

35. Example: N-Queens Why does crossover make sense here?
When wouldn’t it make sense?
What would mutation be?
What would a good fitness function be?

36. Continuous Problems Placing airports in Romania
States: (x1,y1,x2,y2,x3,y3)
Cost: sum of squared distances to closest city

37. Gradient Methods
How to deal with continous (therefore infinite) state spaces?
Discretization: bucket ranges of values
E.g. force integral coordinates
Continuous optimization
E.g. gradient ascent
Image from vias.org

38. Example: Fault Diagnosis
Causes
Symptoms
Fault networks:
Variables?
Domains?
Constraints?
Various ways to query,
given symptoms
Some cause (abduction)
Simplest cause
All possible causes
What test is most useful?
Prediction: cause to effect
We’ll see this idea again with Bayes’ nets
SMTP down
Can’t
DNS down
Can’t IM
Firewall blocking
Can’t print
Printer jam

39. Example: N-Queens
Formulation 3:
Variables:
Domains:
Constraints:

40. Example: N-Queens
Formulation 2:
Variables:
Domains:
Constraints:

41. Example: Boolean Satisfiability
Given a Boolean expression, is it satisfiable?
Very basic problem in computer science
Turns out you can always express in 3-CNF
3-SAT: find a satisfying truth assignment

42. Example: 3-SAT Variables: Domains: Constraints:
Implicitly conjoined (all clauses must be satisfied)

43. CSPs: Queries Types of queries: Legal assignment (last class)
All assignments
Possible values of some query variable(s) given some evidence (partial assignments)

44. Reminder: Search Basic solution: DFS / backtracking Ordering:
Add a new assignment
Filter by checking for immediate violations
Ordering:
Heuristics to choose variable order (MRV)
Heuristics to choose value order (LCV)
Filtering:
Pre-filter unassigned domains after every assignment
Forward checking: remove values which immediately conflict with current assignments (makes MRV easy!)
Today: Arc consistency – propagate indirect consequences of assignments

45. Types of Problems Planning problems: Identification problems:
We want a path to a solution (examples?)
Usually want an optimal path
Incremental formulations
Identification problems:
We actually just want to know what the goal is (examples?)
Usually want an optimal goal
Complete-state formulations
Iterative improvement algorithms

46. Limitations of Arc Consistency
After running arc consistency:
Can have one solution left
Can have multiple solutions left
Can have no solutions left (and not know it)

47. Beam Search
Like greedy hillclimbing search, but keep K states at all times:
Variables: beam size, encourage diversity?
The best choice in MANY practical settings
Complete? Optimal?
Why do we still need optimal methods?
Greedy Search
Beam Search