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Eliminating non-binary constraintsPowerPoint Presentation

Eliminating non-binary constraints

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Eliminating non-binary constraints

- Two methods
- Encodings:
Replace with binary constraints by introducing new vars

- Decompositions:
(For restricted class of non-binary constraints)

Replace with binary constraints on same variables

- Encodings:

Meta-motivation

- Theoretical results informative
- Comparing non-binary constraint propagation with binary
- Suggests where non-binary constraints are valuable

Bibliography

- Lots of valuable results!
Bacchus & van Beek, AAAI-98

Chen, PhD thesis, UAlberta, 2000

Dechter, AAAI-90

Mohr & Masini, ECAI-88

Regin, AAAI-94

Stergiou & Walsh, AAAI-99 & IJCAI-99

Gent, Stergiou & Walsh, Artificial Intelligence 2000

Bacchus, Chen, van Beek, Walsh, Artificial Intelligence 2002

….

Let’s start with the easy case!

Decomposable constraints:

Non-binary constraints that can be represented by binary constraints with introducing new variables

It’s a special case that sometimes occurs about which we can be (theoretically) quite precise

Binary decompositions

- Certain non-binary constraints decompose into binary constraints on same vars
- Sometimes called “network decomposable”

Binary decompositions

- Two examples:
- all-different(x1,x2,x3) is x1\=x2, x1\=x3, x2\=x3
- monotone(x1,x2,x3) is x1 < x2 < x3

- One non-example:
- even(x1+x2+x3)
- Can you see why not?

Binary decompositions

- Theoretical comparison direct
- compare pruning of vars in binary decomposition with that in non-binary

- Empirical experiments reinforce theory
- decomposing non-binary constraints can add orders of magnitude to solution cost

Binary decompositions

- Upper and lower bound on FC
nFC1 on non-binary > FC on decomposition > nFC0 on non-binary

- Gaps can be exponential
Consider n-ary all-different with n-1 values

nFC1 takes (n-1) branches

FC on decomposition takes (n-1)! branches

Binary decompositions

- GAC lower bound
GAC on non-binary > AC on decomposition

Gap again can be exponential

But if we decompose too much, GAC=AC!

- GAC upper bound
In general, GAC ~ NIC, GAC ~ PIC ..

BUT if decomposition to clique, NIC > GAC

Binary decompositions

- Tighter results provable for stricter classes
- Tree decomposable constraints
- constraint graph is tree

- Triangle preserving constraints
- non-binary constraints on all triangles

Binary decompositions

- Tree decomposable constraints
- e.g. monotone(x1,x2,x3)
- GAC=AC
- not surprising as AC enough to solve!

- Decomposition here doesn’t lose us anything
- but even one cycle is enough for GAC>AC

Binary decompositions

- Triangle preserving decomposition
e.g. all-different(x1,x2,x3), quasigroups, ...

GAC > PIC, gap can again be exponential

GAC ~ SAC, strongPC

- PIC is very strong consistency to be achieving at each node
- GAC can do even better than this!
- decomposition carries a very large price

Binary decompositions

- Experimental results
- quasigroup completion
- quasigroup existence

- Quasigroup is a Latin square
- completion is completing partially filled square
- existence is finding one with additional properties

Binary decompositions

- Modelling the quasigroup problem
- n^2 vars, each with domain of size n

- Non-binary model
- 2n all-different constraints (one for each row and column

- Binary decomposition
- 2n cliques of not-equals constraints

Binary decompositions

- Quasigroup completion
- Gomes & Selman report “heavy-tailed” distributions

- Maintaining AC on binary decomposition
- problems often take long time to solve

- Maintaining GAC on all-different
- almost all problems trivial

Binary decompositions

- Quasigroup existence
- best paper at IJCAI-93
- of interest to design theory

- Open results first proved by computer
- in some cases, only ever proved by computer

- Maintaining GAC very competitive
- compared to specialized model finders like FINDER, SEM

Binary encodings

- Not all non-binary constraint decompose into binary constraints
- on the same set of variables

- Consider again
- even(x1+x2+x3)

- But binary CSPs NP-complete

Binary encodings

- Every non-binary constraint can be encoded into binary constraints
- using polynomial number of additional (hidden) variables

- Two popular encodings
- hidden variable encoding
- dual encoding

Binary encodings

- Hidden variable encoding
- add hidden var for each non-binary constraint

- Dual encoding
- add hidden var for each non-binary constraint
- throw away original variables

Binary encodings

- Dual encoding
- consider c1:even(x1+x2), c2:odd(x2+x3)

{00,11}

c1

R21

R21=<00,01> or

<11,10>

{01,10}

c2

Binary encodings

- Hidden variable encoding
- consider c1:even(x1+x2), c2:odd(x2+x3)

{00,11}

c1

r1

r2

{0,1}

{0,1}

{0,1}

x1

x2

x3

r1=<0*,0> or

<1*,1>

r2=<*0,0> or

<*1,1>

r1

r2

{01,10}

c2

Binary encodings

- Hidden var encoding -> dual encoding
- compose relations, discard original vars

{00,11}

c1

R21 = r2 + r1

{0,1}

{0,1}

x1

R21

x3

{01,10}

c2

Binary encodings

- Double encoding
- dual + hidden var encoding
- original vars + hidden vars
- all constraints of dual and of hidden

- Also called “combined encoding”

Binary encodings

- Theoretical analysis complicated by:
- encoding “builds” in GAC for hidden vars
- must translate between (original and hidden) vars
- pruning in dual can infer large arity nogoods in original

Binary encodings

- Hidden var encoding
FC on hidden ~ nFC0 on original

each can be exponentially better than the other

- FC+ propogates through hidden vars
FC+ on hidden = nFC1 on original

Binary encodings

- Hidden var encoding
AC on hidden = GAC on original

- Before looking for efficient (specialized) GAC algorithm
- try AC on hidden var encoding

Binary encodings

- No point doing NIC on hidden var encoding
NIC on hidden = AC on hidden

due to star shaped topology of constraint graph

- Higher consistencies remain distinct
strongPC on hidden > SAC on hidden

> NIC, AC on hidden

Binary encodings

- Dual encoding
FC on dual ~ nFC0 on original

each can be exponentially better than the other

- Dual better for tight constraints
- domains for hidden vars then small

Binary encodings

- Dual encoding
AC on dual > GAC on original

- BUT domains of hidden vars very large when non-binary constraints loose
- AC on dual prohibitively expensive

Binary encodings

- Dual v hidden encoding
BT on dual = FC on hidden

AC on dual > AC on hidden

SAC on dual > SAC on hidden

strongPC on dual = strongPC on hidden

- Path consistency is enough to get through the hidden star!

Binary encodings

- Experimental results
- crossword puzzles
- Golomb rulers
- random CSPs
- random 3-SAT

Binary encodings

- Crossword puzzles
- “Original” model
- vars for letters, domains {A-Z}

- Dual model
- vars for words, domains = dictionary

- Dual at least 1,000 times faster on larger problems

Binary encodings

- Golomb ruler
- set of ticks, xi on a ruler
- all inter-tick distances, dij different

- Ternary constraints, dij=xi-xj
- encoded using hidden var/double encoding

- Competitive with non-binary model
- runs on any binary CSP solver!

Binary encodings

- Random CSPs and SAT
- Bacchus and van Beek plot contours to show constraint tightness where encoding pays
- gives good predictions for performance on structured problems like crosswords, …
- number of satisfying tuples in constraint is most important factor in such predictions

Conclusions

- Non-binary v binary decompositions
- GAC on non-binary can be stronger than PIC on decomposition

- Non-binary v binary encodings
- GAC on non-binary = AC on hidden
- AC on dual > GAC on non-binary

Conclusions

- Non-binary v binary decompositions
- decomposition can add significantly to search cost

- Non-binary v binary encodings
- encoding pays in practice on tight constraints

Future directions?

- Mixed models
- e.g. dual encoding for only some of the (non-binary) constraints

- Optimization
- objective function typically non-binary

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