CGRASS

CGRASS A System for Transforming Constraint Satisfaction Problems Alan Frisch, Ian Miguel (York) Toby Walsh (Cork)

Motivation • Modelling experts carefully transform problems to reduce greatly the effort required to solve them. • The challenge: • Automate these transformations. • Identify useful transformations.

Types of Transformation • Add constraints to eliminate symmetrical solutions. • Given: Add: • Add implied constraints. • Given: and • Infer:

Types of Transformation (2) • Replacing constraints with equivalents. • E.g. Introducing new variables as we will see later. • Removing redundant constraints. • Given: and • is redundant

Overview of Rest of Talk • Case Study: The Golomb Ruler. • Transformation at 3 Levels of Abstraction. • Conclusions/Future Work

The Golomb Ruler • A set of n ticks at integer points on a ruler, length m. • All inter-tick distances must be unique. • Minimise m. • Practical applications: astronomy, crystallography • Previously, useful implied constraints found by hand [Smith et al 2000].

The Golomb Ruler –Characteristics of a Good Model • Break Symmetry: • All n ticks are symmetrical. • Introduce distance variables: • Allows us to use the all-different constraint.

Approach 1:Instance Level Transformation • Minimise: maxi(xi) • Constraints of the form: (xi – xj xk – xl) • Subject to: i  j, k  l, i  k  j  l • Domains: [0 – n2] • Poor Model: • Quaternary Constraints. • Symmetry.

CGRASS • Constraint GeneRation And Symmetry-breaking. • Based on, and extends, Bundy’s proof planning. • Patterns in proofs captured in methods. • Strong preconditions limit applicability. • Prevent combinatorially explosive search. • Easily adaptable to problem transformation. • Methods now encapsulate common patterns in hand-transformation.

Proof Planning - Operation • Given a goal to prove: • Select a method. • Check pre-conditions. • Execute post-conditions. • Construct output goal(s). • Associated tactic constructs actual proof. • CGRASS makes transformations rather than constructing sub-goals to prove.

The Introduce Method • Preconditions • Exp has arity greater than 1, occurs more than once in the constraint set. • someVariable = Exp not already present. • Post-conditions • Generate new var, x, domain defined by Exp. • Add constraint: x = Exp.

An Instance of Eliminate • Preconditions: • Lhs = CommonExp s.t. CommonExp also present in another constraint c. • cnew obtained by replacing all occurrences of CommonExp by Lhs in c. • Size of cnew less than that of c. • cnew not obviously redundant. • Post-conditions • Add cnew, remove c.

Normalisation • Lexicographic ordering + Simplification. • Inspired by HartMath: www.hartmath.org. • Necessary to deal with associative/commutative operators (e.g. +, *). • Replaces semantic equivalence test with simple syntactic comparison (to an extent).

3-tick Golomb Ruler • Basic model produces 30 constraints. • Initial normalisation of the constraint set reduces this to just 12. • Constraints with reflection symmetry across an inequation identical following normalisation. • Some cancellation also possible E.g. x1 – x2 x1 – x3

Symmetry-breaking • Often useful implied constraints can be derived only when some/all symmetry broken. • Symmetry breaking method as pre-processing. • Symmetrical variables: • Have identical domains. • If exchange x1, x2 throughout, re-normalised constraint set returns to original state. • Symmetry-testing on the 3-tick instance: • x1, x2, x3 are symmetrical. • Hence we add: • x1 x2, x2  x3

Variable Introduction/Elimination • After symmetry breaking, strengthening inequalities. • Still 9 quaternary constraints. • Reduce arity by introducing new variables: • E.g. z0 = x1 – x2 • Eliminate substitutes new variables into quaternary constraints, reducing arity.

Final Problem State • Combination of Introduce and Eliminate • GenAllDiff greedily checks for cliques of inequations.

Results

Analysis of Results • Cost of making inferences far outweighs the benefit gained. • An instance of a bad model may have huge numbers of constraints. • E.g. bad model of 6-tick ruler has 870 quaternary constraints. • Conclusion: Inference at instance level alone unlikely to be cost-effective in general.

Approach 2: Transformation of Quantified Constraint Expressions • Minimise: maxi(xi) • This model still in terms of CSP variables. • Input small, represents whole class. • Fairly straightforward to justify distance vars. • Symmetry of ticks is difficult • Swapping ticks, re-normalising doesn’t work.

Approach 3: High Level Transformation of the Golomb Ruler • Put n ticks on a ruler of size m such that all inter-tick distances are unique. Minimise m. • Find T{0, …, m}, |T|=n. • Minimise m, (upper bound: n2). • distance({x, y}) = |x – y| • {distance({x, y})  distance({x’, y’}) | {x, y}  T, {x’,y’}  T, {x, y}  {x’,y’}}

Initial Transformations • Given recurrence of distance, natural to introduce distance variables. • {dxy = distance({x, y}) | {x, y}  T} • Substituting: • {dxy  dx’y’ | {x, y}  T, {x’, y’}  T, {x, y}  {x’, y’}} • This defines a clique. Lifted GenAllDiff: • All-diff({dxy | {x, y}  T})

Refinement • We use refinement methods to move to lower levels of abstraction: • E.g. To pick subset, size n, from set A, size m, totally ordered by : • Introduce set S of n variables, {s1, …, s2}. • Domain of each si is A. • Assignments to elements of S form a set totally ordered by , i.e.s1 < s2 < … < sn

Application of refine(subset) • Assign S = {s1, s2, …, sn} • Each si has domain {0, …, m} • s1 < s2 < … < sn • {dij = distance({si, sj}) | {si, sj} S} • All-diff({dij | {si, sj} S}) • Starting to look more like a CSP

Final Transformations • Domain of each si bounded above by m, which is to be minimised. • Refine {si, sj} from a set to an ordered pair. • Assign S = {s1, s2, …, sn} • Each si has domain {0, …, m} • s1 < s2 < … < sn • Minimise(sn) • {dij = sj – si | <si, sj> S x S, i < j} • All-diff({dxy | <x, y> S x S, i < j})

Conclusions/Future Work • CGRASS: Automatic transformation of instances. • Based on proof planning. • Transformation at instance level alone not practical. • Transformation at the `right’ level is much easier. 4+ Higher Levels… Abstract Refine Sets/Mappings 3 Refine Abstract Quantified Constraints 2 Refine Abstract Individual Instances 1

CGRASS

CGRASS

Presentation Transcript

CGRASS