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Foundations of Constraint Processing CSCE421/821, Spring 2009

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## Foundations of Constraint Processing CSCE421/821, Spring 2009

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**Basic Consistency Methods**Foundations of Constraint Processing CSCE421/821, Spring 2009 www.cse.unl.edu/~choueiry/S09-421-821/ All questions: cse421@cse.unl.edu Berthe Y. Choueiry (Shu-we-ri) Avery Hall, Room 360 choueiry@cse.unl.edu Tel: +1(402)472-5444**Lecture Sources**Required reading • Algorithms for Constraint Satisfaction Problems, Mackworth and Freuder AIJ'85 • Sections 3.1, 3.2, 3.3. Chapter 3. Constraint Processing. Dechter Recommended • Sections 3.4—3.10. Chapter 3. Constraint Processing. Dechter • Networks of Constraints: Fundamental Properties and Application to Picture Processing, Montanari, Information Sciences 74 • Consistency in Networks of Relations, Mackworth AIJ'77 • Constraint Propagation with Interval Labels, Davis, AIJ'87 • Path Consistency on Triangulated Constraint Graphs, Bliek & Sam-Haroud IJCAI'99**Outline**• Motivation and background • Node consistency and its complexity • Arc consistency and its complexity • Criteria for performance comparison and CSP parameters. • Path consistency and its complexity • Global consistency properties • Minimality • Decomposability • When PC guarantees global consistency**Consistency checking**Motivation • CSP are solved with search (conditioning) • Search performance is affected by: - problem size - amount of backtracking • Backtracking may yield.. thrashing Thrashing Exploring non-promising sub-trees and rediscovering the same inconsistencies over and over again Malady of backtrack search**V2**V1 {1, 2, 3} {1, 2, 3} V3<V1 V3<V2 V3 { 0, 1, 2} V3>0 V3<V4 V3<V5 V4 V5 {1, 2} { 1, 2} V5<V4 (Some) causes of thrashing • Search order: • V1, V2, V3, V4, V5 • What happens in search if we: • do not check CV3 (node inconsistency) • do not check constraint CV3,V5 (arc inconsistency) • do not check constraints CV3,V4, CV3,V5, and CV4,V5 (path inconsistency)**Consistency checking**Goal: eliminate inconsistent combinations Algorithms(for binary constraints): • Node consistency • Arc consistency (AC-1, AC-2, AC-3, ..., AC-7, AC-3.1, etc.) • Path consistency (PC-1, PC-2, DPC, PPC, etc.) • Constraints of arbitrary arity: • Waltz algorithm ancestor of AC's • Generalized arc-consistency (Dechter, Section 3.5.1 ) • Relational m-consistency (Dechter, Section 8.1)**Overview of Recommended Reading**• Davis, AIJ'77 • Focuses on the Waltz algorithm • Studies its performance and quiescence for given: • Constraint types (bounded diff, algebraic, etc.) • Domains types (continuous or finite) • Mackworth, AIJ'77 • presents NC, AC-1, AC-2, PC-1, PC-2 • Mackworth and Freuder, AIJ'85 • studies their complexity Mackworth and Freuder concentrate on finite domains More people work on finite domains than on continuous ones Continuous domains can be quite tough**Constraint Propagation with Interval Labels Ernest Davis**• `Old' paper (1987): terminology slightly different • Interval labels: continuous domains • Section 8: sign labels (discrete) • Concerned with Waltz algorithm (e.g., quiescence, completeness) • Constraint types vs. domain types (Table 3) • Addresses applications of reasoning about • Physical Systems (circuit, SPAM) • time relations (TMM) • Advice: read Section 3, more if you wish**Waltz algorithm for label inference**• Refine(C(Vi, Vk, Vm, Vn), Vi) finds a new label for Vi consistent with C. • Revise(C (Vi, Vk, Vm, Vn)) refines the domains of Vi, Vk, Vm, Vn by iterating over these variables until quiescence (i.e., no domains is further refined) • Waltz Algorithm • revises all constraints by iterating over each constraint connected to a variable whose domain has been revised • Is the ancestor of arc-consistency**WARNING**→ Completeness: (i.e., for solving the CSP)Running the Waltz Algorithm does not solve the problem. A=2 B=3 is still not a solution! →Quiescence: The Waltz algorithm may go into infinite loops even if problem is solvable x [0, 100] x = y y [0, 100] x = 2y →Davis characterizes the completeness and quiescence of the Waltz algorithm (see Table 3) in terms of • constraint types • domain types**Importance of this paper** Establishes that constraints of bounded differences (temporal reasoning) can be efficiently solved by the Waltz algorithm (O(n3), n number of variables) Early paper that attracts attention on the difficulty of label propagation in interval labels (continuous domains). This work has been continued by Faltings (AIJ 92) who studied the early-quiescence problem of the Waltz algorithm.**Basic consistency algorithms**Examining finite, binary CSPs and their complexity Notation: Given variables i, j, k with values x, y, z, the predicate Pijk(x, y, z) is true iff the 3-tuple x, y, z Cijk Node consistency: checking Pi(x) Arc consistency: checking Pij(x,y) Path consistency: bit-matrix manipulation**time**space Worst-case asymptotic complexity Worst-case complexity as a function of the input parameters Upper bound: f(n) is O(g(n)) means that f(n)c.g(n) f grows as g or slower Lower bound: f(n) is (h(n)) means that f(n) c.h(n) f grows as g or faster Input parameters for a CSP: n = number of variables a = (max) size of a domain dk = degree of Vk ( n-1) e = number of edges (or constraints) [(n-1), n(n-1)/2]**Outline**• Motivation and background • Node consistency and its complexity • Arc consistency and its complexity • Criteria for performance comparison and CSP parameters. • Path consistency and its complexity • Global consistency properties • Minimality • Decomposability • When PC guarantees global consistency**Node consistency (NC)**Procedure NC(i): Di Di { x | Pi(x) } Begin for i 1 until n do NC(i) end**Complexity of NC**Procedure of NC(i) DiDi { x | Pi(x) } Begin for i 1 until n do NC(i) end • For each variable, we check a values • We have n variables, we do n.a checks • NC is O(a.n)**Outline**• Motivation and background • Node consistency and its complexity • Arc consistency and its complexity • Criteria for performance comparison and CSP parameters. • Path consistency and its complexity • Global consistency properties • Minimality • Decomposability • When PC guarantees global consistency**Vj**Vi Vi Vj 1 1 1 2 2 2 2 3 3 3 Arc-consistency Adapted from Dechter Definition: Given a constraint graph G, • A variable Vi is arc-consistent relative to Vj iff for every value aDVi, there exists a value bDVj | (a, b)CVi,Vj. • The constraint CVi,Vj is arc-consistent iff • Vi is arc-consistent relative to Vj and • Vj is arc-consistent relative to Vi. • A binary CSP is arc-consistent iff every constraint (or sub-graph of size 2) is arc-consistent**Procedure Revise**Revises the domains of a variable i Procedure Revise(i,j): Begin DELETE false for each x Dido if there is no y Dj such that Pij(x, y) then begin delete x from Di DELETE true end return DELETE end • Revise is directional • What is the complexity of Revise? {a2}**Revise: example R. Dechter**Apply the Revise procedure to the following example**Vj**Vi Vi Vj 1 1 1 2 2 2 2 3 3 3 Effect of Revise Adapted from Dechter Question: Given • two variables Vi and Vj • their domains DVi and DVj, and • the constraint CVi,Vj, write the effect of the Revise procedure as a sequence of operations in relational algebra Hint: • Think about the domain DVi as a unary constraint CVi and • consider the composition of this unary constraint and the binary one.. • Solution: DVi DViVi (CVi,VjDVi) • This is actually equivalent to DViVi (CVi,VjDVi)**Arc Consistency (AC-1)**Procedure AC-1: 1 begin 2 for i 1 untilndo NC(i) 3 Q {(i, j) | (i,j) arcs(G), i j } 4 repeat 5 begin 6 CHANGE false 7 for each (i, j) Q do CHANGE ( REVISE(i, j) or CHANGE ) 8 end 9 until ¬ CHANGE 10 end • AC-1 does not update Q, the queue of arcs • No algorithm can have time complexity below O(ea2) • Alert: Algorithms do not, but should, test for empty domains**Warning**• What I don’t like about the algorithms as specified in most papers is that they do not check for domain wipe-out. • In your code, always check for domain wipe out and terminate/interrupt the algorithm when that occurs • In practice may save lots of #CC and cycles**V1**V1 V1 V1 V3 V2 V3 V3 V2 V3 V2 V2 V1 V1 V1 V3 V2 V3 V3 V2 V2 Arc consistency • AC may discover the solution Example borrowed from Dechter**Arc consistency**2. AC may discover inconsistency Example borrowed from Dechter**x**[0, 10] x [0, 7] x [4, 7] x y-3 x y-3 x y-3 y [0, 10] y [3, 10] y [7, 10] NC & AC Example courtesy of M. Fromherz In the temporal problem below • AC propagates bound • Unary constraint x>3 is imposed • AC propagates bounds again**Complexity of AC-1**Procedure AC-1: 1 begin 2 for i 1 untilndo NC(i) 3 Q {(i, j) | (i,j) arcs(G), i j 4 repeat 5 begin 6 CHANGE false 7 for each (i, j) Q do CHANGE (REVISE(i, j) or CHANGE) 8 end 9 until ¬ CHANGE 10 end Note: Q is not modified and |Q| = 2e • 4 9 repeats at most n·a times • Each iteration has |Q| = 2e calls to REVISE • Revise requires at most a2 checks of Pij AC-1 is O(a3· n · e)**AC versions**• AC-1 does not update Q, the queue of arcs • AC-2 iterates over arcs connected to at least one node whose domain has been modified. Nodes are ordered. • AC-3 same as AC-2, nodes are not ordered**Arc consistency (AC-3)**AC-3 iterates over arcs connected to at least one node whose domain has been modified Procedure AC-3: 1 begin 2 fori 1 untilndo NC(i) 3 Q { (i, j) | (i, j) arcs(G), i j } 4 WhileQ is not empty do 5 begin 6 select and delete any arc (k, m) from Q 7 If Revise(k, m) thenQQ { (i, k) | (i, k) arcs(G), i k, i m } 8 end 9 end**Complexity of AC-3**• Procedure AC-3: • 1 begin • 2 fori 1 untilndo NC(i) • 3 Q {(i, j) | (i, j) arcs(G), i j } • 4 WhileQ is not empty do • 5 begin • select and delete any arc (k, m) from Q • 7 If Revise(k, m) thenQQ { (i, k) | (i, k) arcs(G), i k, i m } • 8 end • 9 end • First |Q| = 2e, then it grows and shrinks 48 • Worst case: • 1 element is deleted from DVk per iteration • none of the arcs added is in Q • Line 7: a·(dk - 1) • Lines 4 - 8: • Revise: a2 checks of Pij • AC-3 is O(a2(2e + a(2e-n))) • Connected graph (en – 1), AC-3 is O(a3e) • If AC-p, AC-3 is O(a2e) AC-3 is (a2e) • Complete graph: O(a3n2)**V2**V2 V1 V1 { 1, 2, 3, 4 } { 1, 2, 3, 4, 5 } { 1, 3, 5 } { 1, 2, 3, 4, 5 } { 1, 3, 5 } { 1, 2, 3, 5 } { 1, 2, 3, 4, 5 } { 1, 2, 3, 4, 5 } V4 V3 V4 V3 Example: Apply AC-3 Example: Apply AC-3 Thanks to Xu Lin DV1 = {1, 2, 3, 4, 5} DV2 = {1, 2, 3, 4, 5} DV3 = {1, 2, 3, 4, 5} DV4 = {1, 2, 3, 4, 5} CV2,V3 = {(2, 2), (4, 5), (2, 5), (3, 5), (2, 3), (5, 1), (1, 2), (5, 3), (2, 1), (1, 1)} CV1,V3 = {(5, 5), (2, 4), (3, 5), (3, 3), (5, 3), (4, 4), (5, 4), (3, 4), (1, 1), (3, 1)} CV2,V4 = {(1, 2), (3, 2), (3, 1), (4, 5), (2, 3), (4, 1), (1, 1), (4, 3), (2, 2), (1, 5)}**Applying AC-3 Thanks to Xu Lin**• Queue = {CV2, V4, CV4,V2, CV1,V3, CV2,V3, CV3, V1, CV3,V2} • Revise(V2,V4): DV2DV2 \ {5} = {1, 2, 3, 4} • Queue = {CV4,V2, CV1,V3, CV2,V3, CV3, V1, CV3, V2} • Revise(V4, V2): DV4 DV4 \ {4} = {1, 2, 3, 5} • Queue = {CV1,V3, CV2,V3, CV3, V1, CV3, V2} • Revise(V1, V3): DV1 {1, 2, 3, 4, 5} • Queue = {CV2,V3, CV3, V1, CV3, V2} • Revise(V2, V3): DV2 {1, 2, 3, 4}**Applying AC-3 (cont.) Thanks to Xu Lin**• Queue = {CV3, V1, CV3, V2} • Revise(V3, V1): DV3 DV3 \ {2} = {1, 3, 4, 5} • Queue = {CV2, V3, CV3, V2} • Revise(V2, V3): DV2 DV2 • Queue = {CV3, V2} • Revise(V3, V2): DV3 {1, 3, 5} • Queue = {CV1, V3} • Revise(V1, V3): DV1 {1, 3, 5} END**Main Improvements**Mohr & Henderson (AIJ 86): • AC-3 O(a3e) AC-4 O(a2e) • AC-4 is optimal • Trade repetition of consistency-check operations with heavy bookkeeping on which and how many values support a given value for a given variable • Data structures it uses: • m: values that are active, not filtered • s: lists all vvp's that support a given vvp • counter: given a vvp, provides the number of support provided by a given variable • How it proceeds: • Generates data structures • Prepares data structures • Iterates over constraints while updating support in data structures**counter**( V , V ), 3 0 4 2 ( V , V ), 2 0 4 2 ( V , V ), 1 0 4 2 ( V , V ), 1 0 2 3 M M ( V , V ), 4 0 1 3 Step 1: Generate 3 data structures • mand s have as many rows as there are vvp’s in the problem • counterhasas many rows as there are tuples in the constraints**Step 2: Prepare data structures**Data structures: s and counter. • Checks for every constraint CVi,Vj all tuples Vi=ai, Vj=bj_) • When the tuple is allowed, then update: • s(Vj,bj) s(Vj,bj) {(Vi, ai)} and • counter(Vj,bj) (Vj,bj) + 1 Update counter ((V2, V3), 2) to value 1 Update counter ((V3, V2), 2) to value 1 Update s-htable (V2, 2) to value ((V3, 2)) Update s-htable (V3, 2) to value ((V2, 2)) Update counter ((V2, V3), 4) to value 1 Update counter ((V3, V2), 5) to value 1 Update s-htable (V2, 4) to value ((V3, 5)) Update s-htable (V3, 5) to value ((V2, 4))**Constraints CV2,V3 and CV3,V2**Updating m Note that (V3, V2),4 0 thus we remove 4 from the domain of V3 and update (V3, 4) nil in m Updating counter Since 4 is removed from DV3 then for every (Vk, l) | (V3, 4) s[(Vk, l)], we decrement counter[(Vk, V3), l] by 1**Summary of arc-consistency algorithms**• AC-4 is optimal (worst-case) [Mohr & Henderson, AIJ 86] • Warning: worst-case complexity is pessimistic. Better worst-case complexity: AC-4 Better average behavior: AC-3 [Wallace, IJCAI 93] • AC-5: special constraints [Van Hentenryck, Deville, Teng 92] functional, anti-functional, and monotonic constraints • AC-6, AC-7: general but rely heavily on data structures for bookkeeping [Bessière & Régin] • Now, back to AC-3: AC-2000, AC-2001≡AC-3.1, AC3.3, etc. • Non-binary constraints: • GAC (general) [Mohr & Masini 1988] • all-different (dedicated) [Régin, 94]**Outline**• Motivation and background • Node consistency and its complexity • Arc consistency and its complexity • Criteria for performance comparison and CSP parameters. • Path consistency and its complexity • Global consistency properties • Minimality • Decomposability • When PC guarantees global consistency**CSP parameters ‹n, a, t, d›**• n is number of variables • a is maximum domain size • t is constraint tightness: • d is constraint density where e is the #constraints, emin=(n-1), and emax = n(n-1)/2 Lately, we use constraint ratiop = e/emax → Constraints in random problems often generated uniform → Use only connected graphs (throw the unconnected ones away)**Criteria for performance comparison**• Bounding time and space complexity (theoretical) • worst-case: always • average-case: never • best- case: sometimes • Counting #CC and #NV (theoretical, empirical) • Measuring CPU time (empirical)**Performance comparisonCourtesy of Lin XU**AC-3, AC-7, AC-4 on n=10,a=10,t,d=0.6 displaying #CC and CPU time AC-3 AC-4 AC-4 AC-7 AC-3 AC-7**Wisdom (?) Free adaptation from Bessière**• When a single constraint check is very expensive to make, use AC-7. • When there is a lot of propagation, use AC-4 • When little propagation and constraint checks are cheap, use AC-3x**AC2001**• When checking (Vi,a) against Vj • Keep track of the Vj value that supports (Vi,a) • Last((Vi,a),Vi) • Next time when checking again (Vi,a) against Vj, we start from Last((Vi,a),Vi) and don’t have to traverse again the domain of Vj again or list of tuples in CViVj • Big savings..**Performance comparison Courtesy of Shant**AC3, AC3.1, AC4 on on n=40,a=16,t,d=0.15 displaying #CC and CPU time**AC-what?**Instructor’s personal opinion • Used to recommend using AC-3.. • Now, recommend using AC2001 • Do the project on AC-* if you are curious.. [Régin 03]**=**= = AC is not enough Example borrowed from Dechter Arc-consistent? Satisfiable? seek higher levels of consistency V V 1 1 b a a b V V V V 2 3 2 a b 3 b a a b a b**Outline**• Motivation and background • Node consistency and its complexity • Arc consistency and its complexity • Criteria for performance comparison and CSP parameters. • Path consistency and its complexity • Global consistency properties • Minimality • Decomposability • When PC guarantees global consistency**V2**V1 Vm-1 Vm V0 for all y DVm for all x DV0 Consistency of a path A path (V0, V1, V2, …, Vm) of length m is consistent iff • for any value xDV0 and for any value yDVm that are consistent (i.e., PV0 Vm(x, y)) • a sequence of values z1, z2, … , zm-1 in the domains of variables V1, V2, …, Vm-1, such that all constraints between them (along the path, not across it) are satisfied (i.e., PV0 V1(x, z1) PV1 V2(z1, z2) … PVm-1 Vm(zm-1, zm) )**V2**V1 Vm-1 Vm V0 for all y DVm for all x DV0 Note The same variable can appear more than once in the path Every time, it may have a different value Constraints considered: PV0,Vm and those along the path All other constraints are neglected