Problem Solving with Constraints Intelligent Backtracking Algorithms 1 Problem Solving with Constraints CSCE496/896, Fall2011: www.cse.unl.edu/~choueiry/F11-496-896.

Problem Solving with Constraints Intelligent Backtracking Algorithms 1 Problem Solving with Constraints CSCE496/896, Fall2011: www.cse.unl.edu/~choueiry/F11-496-896 Berthe Y. Choueiry (Shu-we-ri) Avery Hall, Room 360 choueiry@cse.unl.edu Intelligent Backtracking Algorithms

Problem Solving with Constraints Intelligent Backtracking Algorithms 2 Reading Required reading –Hybrid Algorithms for the Constraint Satisfaction Problem [Prosser, CI 93] Recommended reading –Chapters 5 and 6 of Dechter’s textbook –Tsang, Chapter 5 Available upon request –Notes of Fahiem Bacchus: Chapter 2, Section 2.4

Problem Solving with Constraints Intelligent Backtracking Algorithms 3 Outline Review of terminology of search Hybrid backtracking algorithms

Problem Solving with Constraints Intelligent Backtracking Algorithms 4 Backtrack search (BT) Variable/value ordering Variable instantiation (Current) path Current variable Past variables Future variables Shallow/deep levels /nodes Search space / search tree Back-checking Backtracking

Problem Solving with Constraints Intelligent Backtracking Algorithms 5 Outline Review of terminology of search Hybrid backtracking algorithms –Vanilla: BT –Improving back steps: {BJ, CBJ} –Improving forward step: {BM, FC}

Problem Solving with Constraints Intelligent Backtracking Algorithms 6 Two main mechanisms in BT 1.Backtracking: To recover from dead-ends To go back 2.Consistency checking: To expand consistent paths To move forward

Problem Solving with Constraints Intelligent Backtracking Algorithms 7 Backtracking To recover from dead-ends 1.Chronological (BT) 2.Intelligent Backjumping (BJ) Conflict directed backjumping (CBJ) With learning algorithms (Dechter Chapt 6.4) Etc.

Problem Solving with Constraints Intelligent Backtracking Algorithms 8 Consistency checking To expand consistent paths 1.Back-checking: against past variables Backmarking (BM) 2.Look-ahead: against future variables Forward checking (FC) (partial look-ahead) Directional Arc-Consistency (DAC) (partial look-ahead) Maintaining Arc-Consistency (MAC) (full look-ahead)

Problem Solving with Constraints Intelligent Backtracking Algorithms 9 Hybrid algorithms Backtracking + checking = new hybrids BTBJCBJ BMBMJBM-CBJ FCFC-BJFC-CBJ Evaluation: Empirical: Prosser 93. 450 instances of Zebra Theoretical: Kondrak & Van Beek 95

Problem Solving with Constraints Intelligent Backtracking Algorithms 10 Notations (in Prosser’s paper) Variables: V i, i in [1, n] Domain: D i = {v i1, v i2, …,v iMi } Constraint between V i and V j : C i,j Constraint graph: G Arcs of G: Arc(G) Instantiation order (static or dynamic) Language primitives: list, push, pushnew, remove, set-difference, union, max-list

Problem Solving with Constraints Intelligent Backtracking Algorithms 11 Main data structures v: a (1xn) array to store assignments –v[i] gives the value assigned to i th variable –v[0]: pseudo variable (root of tree), backtracking to v[0] indicates insolvability domain[i]: a (1xn) array to store the original domains of variables current-domain[i]: a (1xn) array to store the current domains of variables –Upon backtracking, current-domain[i] of future variables must be refreshed check(i,j): a function that checks whether the values assigned to v[i] and v[j] are consistent

Problem Solving with Constraints Intelligent Backtracking Algorithms 12 Generic search: bcssp 1.Procedure bcssp (n, status) 2.Begin 3.consistent  true 4.status  unknown 5.i  1 6.While status = unknown 7.Do Begin 8.If consistent 9.Then i  label (i, consistent) 10.Else i  unlabel (i, consistent) 11.If i > n 12.Then status  “solution” 13.Else If i=0 then status  “impossible” 14.End 15.End Forward move: x-label Backward move: x-unlabel Parameters: i: current variable, consistent: Boolean Return: i: new current variable

Problem Solving with Constraints Intelligent Backtracking Algorithms 13 Chronological backtracking (BT) Uses bt-label and bt-unlabel bt-label: –When v[i] is assigned a value from current-domain[i], we perform back-checking against past variables (check(i,k)) –If back-checking succeeds, bt-label returns i+1 –If back-checking fails, we remove the assigned value from current- domain[i], assign the next value in current-domain[i], etc. –If no other value exists, consistent  nil (bt-unlabel will be called) bt-unlabel –Current level is set to i-1 (notation for current variable: v[h]) –For all future variables j: current-domain[j]  domain[j] –If domain[h] is not empty, consistent  true (bt-label will be called) –Note: for all past variables g, current-domain[g]  domain[g]

Problem Solving with Constraints Intelligent Backtracking Algorithms 14 BT-label 1.Function bt-label(i,consistent): INTEGER 2.BEGIN 3.consistent  false 4.For v[i]  each element of current-domain[i] while not consistent 5.Do Begin 6.consistent  true 7.For h  1 to (i-1) While consistent 8.Do consistent  check(i,h) 9.If not consistent 10.Then current-domain[i]  remove(v[i], current-domain[i]) 11.End 12.If consistent then return(i+1) ELSE return(i) 13.END Terminates: consistent=true, return i+1 consistent=false, current- domain[i]=nil, returns i

Problem Solving with Constraints Intelligent Backtracking Algorithms 15 BT-unlabel 1.FUNCTION bt-unlabel(i,consistent):INTEGER 2.BEGIN 3.h  i -1 4.current-domain[i]  domain[i] 5.current-domain[h]  remove(v[h],current-domain[h]) 6.consistent  current-domain[h]  nil 7.return(h) 8.END Is called when consistent=false and current-domain[i]=nil Selects v h to backtrack to (Uninstantiates all variables between v h and v i ) Uninstantiates v[h]: removes v[h] from current-domain [h]: Sets consistent to true if current-domain[h]  0 Returns h

Problem Solving with Constraints Intelligent Backtracking Algorithms 16 Example: BT (the dumbest example ever) {1,2,3,4,5} V2 V1 V3 V4 V5 C V3,V4 ={(V3=1,V4=3)} C V2,V5 ={(V2=5,V5=1),(V2=5,V5=4)} - v[1] v[2] v[3] v[4] v[5] v[0] 1 1 1 1 2134 2345 etc…

Problem Solving with Constraints Intelligent Backtracking Algorithms 17 Outline Review of terminology of search Hybrid backtracking algorithms –Vanilla: BT –Improving back steps: BJ, CBJ –Improving forward step: BM, FC

Problem Solving with Constraints Intelligent Backtracking Algorithms 18 Danger of BT: thrashing BT assumes that the instantiation of v[i] was prevented by a bad choice at (i-1). It tries to change the assignment of v[i-1] When this assumption is wrong, we suffer from thrashing (exploring ‘barren’ parts of solution space) Backjumping (BT) tries to avoid that –Jumps to the reason of failure –Then proceeds as BT

Problem Solving with Constraints Intelligent Backtracking Algorithms 19 Backjumping (BJ) Tries to reduce thrashing by saving some backtracking effort When v[i] is instantiated, BJ remembers v[h], the deepest node of past variables that v[i] has checked against. Uses: max-check[i], global, initialized to 0 At level i, when check(i,h) succeeds max-check[i]  max(max-check[i], h) If current-domain[h] is getting empty, simple chronological backtracking is performed from h –BJ jumps then steps! 1 2 3 0 2 3 1 i h-1 h h h-2 0 0 0 Current variable Past variable

Problem Solving with Constraints Intelligent Backtracking Algorithms 20 BJ: label/unlabel bj-label: same as bt-label, but updates max-check[i] bj-unlabel, same as bt-unlabel but –Backtracks to h = max-check[i] –Resets max-check[j]  0 for j in [h+1,i]  Important: max-check is the deepest level we checked against, could have been success or could have been failure 1 2 3 0 2 3 1 i h-1 h h h-2 0 0 0

Problem Solving with Constraints Intelligent Backtracking Algorithms 21 Example: BJ 2 {1,2,3,4,5} V2 V1 V3 V4 V5 C V2,V4 ={(V2=1,V4=3)} C V1,V5 ={(V1=1,V5=2)} C V2,V5 ={(V2=5,V5=1)} - v[1] v[2] v[3] v[4] v[5] v[0] = 0 1 1 1 1 2134 2345 Max-check[1] = 0 Max-check[2] = 1 max-check[4] = 3 max-check[5] = 2 V4=1, fails for V2, mc=2 V4=2, fails for V2, mc=2 V4=3, succeeds V5=1, fails for V1, mc=1 V5=2, fails for V2, mc=2 V5=3, fails for V1 V5=4, fails for V1 V5=5, fails for V1

Problem Solving with Constraints Intelligent Backtracking Algorithms Backtracking22 Conflict-directed backjumping (CBJ) Backjumping –jumps from v[i] to v[h], –but then, it steps back from v[h] to v[h-1]  CBJ improves on BJ –Jumps from v[i] to v[h] –And jumps back again, across conflicts involving both v[i] and v[h] –To maintain completeness, we jump back to the level of deepest conflict

Problem Solving with Constraints Intelligent Backtracking Algorithms 23 CBJ: data structure Maintains a conflict set: conf-set conf-set[i] are first initialized to {0} At any point, conf-set[i] is a subset of past variables that are in conflict with i {0} conf-set[g] conf-set[h] conf-set[i] 0 1 2 g h-1 h i conf-set

Problem Solving with Constraints Intelligent Backtracking Algorithms 24 CBJ: conflict-set {x} {3} {1, g, h} {0} conf-set[g] conf-set[h] conf-set[i] 1 2 3 g h-1 h Current variable i Past variables {3,1, g} {x, 3,1} When a check(i,h) fails conf-set[i]  conf-set[i]  {h} When current-domain[i] empty 1.Jumps to deepest past variable h in conf-set[i] 2.Updates conf-set[h]  conf-set[h]  (conf-set[i] \{h}) Primitive form of learning (while searching)

Problem Solving with Constraints Intelligent Backtracking Algorithms 25 Example CBJ {1,2,3,4,5} V2 V1 V3 V4 V5 {(V1=1, V6=3)} - v[1] v[2] v[3] v[4] v[6] v[0] = 0 1 1 1 1 213 2345 conf-set[1] = {0} conf-set[2] = {0} conf-set[3] = {0} {(V4=5, V6=3)} {(V2=1, V4=3), (V2=4, V4=5)} conf-set[6] = {1} {1,2,3,4,5} V6 {(V1=1, V5=3)} conf-set[4] = {2} v[5] 213 conf-set[6] = {1} conf-set[6] = {1,4} conf-set[4] = {1, 2} conf-set[5] = {1}

Problem Solving with Constraints Intelligent Backtracking Algorithms CBJ for finding all solutions After finding a solution, if we jump from this last variable, then we may miss some solutions and lose completeness Two solutions, proposed by Chris Thiel (S08) 1.Using conflict sets 2.Using cbf of Kondrak, a clear pseudo-code Rationale by Rahul Purandare (S08) –We cannot skip any variable without chronologically backtracking to it at least once –In fact, exactly once 26

Problem Solving with Constraints Intelligent Backtracking Algorithms CBJ/All solutions without cbf When a solution is found, force the last variable, N, to conflict with everything before it –conf-set[N]  {1, 2,..., N-1}. This operation, in turn, forces some chronological backtracking as the conf-sets are propagated backward 27

Problem Solving with Constraints Intelligent Backtracking Algorithms CBJ/All solutions with cbf Kondrak proposed to fix the problem using cbf (flag), a 1xn vector –  i, cbf[i]  0 –When you find a solution,  i, cbf[i]  1 In unlabel –if (cbf[i]=1) Then h  i-1; cbf[i]  0 Else h  max-list (conf-set[i]) 28

Problem Solving with Constraints Intelligent Backtracking Algorithms 29 Backtracking: summary Chronological backtracking –Steps back to previous level –No extra data structures required Backjumping –Jumps to deepest checked-against variable, then steps back –Uses array of integers: max-check[i] Conflict-directed backjumping –Jumps across deepest conflicting variables –Uses array of sets: conf-set[i]

Problem Solving with Constraints Intelligent Backtracking Algorithms 30 Outline Review of terminology of search Hybrid backtracking algorithms –Vanilla: BT –Improving back steps: BJ, CBJ –Improving forward step: BM, FC

Problem Solving with Constraints Intelligent Backtracking Algorithms 31 Backmarking: goal Tries to reduce amount of consistency checking Situation: –v[i] about to be re-assigned k –v[i]  k was checked against v[h]  g –v[h] has not been modified v[h] = g v[i] k k

Problem Solving with Constraints Intelligent Backtracking Algorithms 32 BM: motivation Two situations 1.Either (v[i]=k,v[h]=g) has failed  it will fail again 2.Or, (v[i]=k,v[h]=g) was founded consistent  it will remain consistent v[h] = g v[i] k k  v[h] = g v[i] k k In either case, back-checking effort against v[h] can be saved!

Problem Solving with Constraints 33 Data structures for BM: 2 arrays 000000000 0 0 0 0 Number of variables n max domain size m Number of variables n maximum checking level: mcl (n x m) Minimum backup level: mbl (n x 1)

Problem Solving with Constraints 34 Maximum checking level 000000000 0 0 0 0 Number of variables n max domain size m mcl[i,k] stores the deepest variable that v[i]  k checked against mcl[i,k] is a finer version of max-check[i]

Problem Solving with Constraints 35 Minimum backup level Number of variables n mbl[i] gives the shallowest past variable whose value has changed since v[i] was the current variable BM (and all its hybrid) do not allow dynamic variable ordering

Problem Solving with Constraints Intelligent Backtracking Algorithms 36 When mcl[i,k]=mbl[i]=j v[i] k k v[j] mbl[i] = j BM is aware that The deepest variable that (v[i]  k) checked against is v[j] Values of variables in the past of v[j] (h<j) have not changed So We do need to check (v[i]  k) against the values of the variables between v[j] and v[i] We do not need to check (v[i]  k) against the values of the variables in the past of v[j]

Problem Solving with Constraints Intelligent Backtracking Algorithms 37 Type a savings v[h] v[i] k k v[j] mcl[i,k]=h mcl[i,k] < mbl[i]=j When mcl[i,k] < mbl[i], do not check v[i]  k because it will fail

Problem Solving with Constraints Intelligent Backtracking Algorithms 38 Type b savings h v[i] k k v[j] v[g] mcl[i,k]=g mbl[i] = j mcl[i,k]  mbl[i] When mcl[i,k]  mbl[i], do not check (i,h<j) because they will succeed

Problem Solving with Constraints Intelligent Backtracking Algorithms 39 Hybrids of BM mcl can be used to allow backjumping in BJ Mixing BJ & BM yields BMJ –avoids redundant consistency checking (types a+b savings) and –reduces the number of nodes visited during search (by jumping) Mixing BM & CBJ yields BM-CBJ

Problem Solving with Constraints Intelligent Backtracking Algorithms 40 Problem of BM and its hybrids: warning v[m] v[g] v[h] v[i] v[g] v[h] v[i] v[m] v[g] v[h] v[i] v[h] v[f] Backjumping from v[i]: –v[i] backjumps up to v[g] Backmarking of v[h]: –When reconsidering v[h], v[h] will be checked against all f  [m,g) –effort could be saved  Phenomenon will worsen with CBJ Problem fixed by Kondrak & van Beek 95 BMJ enjoys only some of the advantages of BM Assume: mbl[h] = m and max-check[i]=max(mcl[i,x])=g

Problem Solving with Constraints Intelligent Backtracking Algorithms 41 Forward checking (FC) Looking ahead: from current variable, consider all future variables and clear from their domains the values that are not consistent with current partial solution FC makes more work at every instantiation, but will expand fewer nodes When FC moves forward, the values in current-domain of future variables are all compatible with past assignment, thus saving backchecking FC may “wipe out” the domain of a future variable (aka, domain annihilation) and thus discover conflicts early on. FC then backtracks chronologically Goal of FC is to fail early (avoid expanding fruitless subtrees)

Problem Solving with Constraints Intelligent Backtracking Algorithms 42 FC: data structures v[i] v[k] v[l] v[n] v[m] v[j] When v[i] is instantiated, current-domain[j] are filtered for all j connected to i and I < j  n reduction[j] store sets of values remove from current-domain[j] by some variable before v[j] reductions[j] = {{a, b}, {c, d, e}, {f, g, h}} future-fc[i]: subset of the future variables that v[i] checks against (redundant) future-fc[i] = {k, j, n} past-fc[i]: past variables that checked against v[i] All these sets are treated like stacks

Problem Solving with Constraints Intelligent Backtracking Algorithms 43 Forward Checking: functions check-forward undo-reductions update-current-domain fc-label fc-unlabel

Problem Solving with Constraints Intelligent Backtracking Algorithms 44 FC: functions check-forward(i,j) is called when instantiating v[i] –It performs Revise (j,i) –Returns false if current-domain[j] is empty, true otherwise –Values removed from current-domain[j] are pushed, as a set, into reductions[j] These values will be popped back if we have to backtrack over v[i] (undo-reductions)

Problem Solving with Constraints Intelligent Backtracking Algorithms 45 FC: functions update-current-domain –current-domain[i]  domain[i] \ reductions[i] –actually, we have to iterate over reductions, which is a set of sets fc-label –Attempts to instantiate current-variable –Then filters domains of all future variables (push into reductions) –Whenever current-domain of a future variable is wiped-out: v[i] is un-instantiated and domain filtering is undone (pop reductions)

Problem Solving with Constraints Intelligent Backtracking Algorithms 46 Hybrids of FC FC suffers from thrashing: it is based on BT FC-BJ: –max-check is integrated in fc-bj-label and fc-bj-unlabel –Enjoys advantages of FC and BJ… but suffers malady of BJ (first jumps, then steps back) FC-CBJ: –Best algorithm so far –fc-cbj-label and fc-cbj-unlabel

Problem Solving with Constraints Intelligent Backtracking Algorithms 47 Consistency checking: summary Chronological backtracking –Uses back-checking –No extra data structures Backmarking –Uses mcl and mbl –Two types of consistency-checking savings Forward-checking –Works more at every instantiation, but expands fewer subtrees –Uses: reductions[i], future-fc[i], past-fc[i]

Problem Solving with Constraints Intelligent Backtracking Algorithms 48 Experiments Empirical evaluations on Zebra –Representative of design/scheduling problems –25 variables, 122 binary constraints –Permutation of variable ordering yields new search spaces –Variable ordering: different bandwidth/induced width of graph 450 problem instances were generated Each algorithm was applied to each instance Experiments were carried out under static variable ordering

Problem Solving with Constraints Intelligent Backtracking Algorithms 49 Analysis of experiments Algorithms compared with respect to: 1.Number of consistency checks (average) FC-CBJ ≼ FC-BJ ≼ BM-CBJ ≼ FC ≼ CBJ ≼ BMJ ≼ BM ≼ BJ ≼ BT 2.Number of nodes visited (average) FC-CBJ ≼ FC-BJ ≼ FC ≼ BM-CBJ ≼ BMJ=BJ ≼ BM=BT 3.CPU time (average) FC-CBJ ≼ FC-BJ ≼ FC ≼ BM-CBJ ≼ CBJ ≼ BMJ ≼ BJ ≼ BT ≼ BM FC-CBJ apparently the champion

Problem Solving with Constraints Intelligent Backtracking Algorithms 50 Additional developments Other backtracking algorithms exist: –Graph-based backjumping (GBJ), etc. [Dechter] –Pseudo-trees [Freuder 85] Other look-ahead techniques exist –DAC, MAC, etc. More empirical evaluations –over randomly generated problems Theoretical comparisons [Kondrak & van Beek IJCAI’95]

Problem Solving with Constraints Intelligent Backtracking Algorithms Implementing BT-based algorithms Preprocessing –Enforce NC, do not include in #CC (e.g., Zebra) –Normalize all constraints (fapp01-0200-0)fapp01-0200-0 –Check for empty relations (bqwh-15-106-0_ext)bqwh-15-106-0_ext Interrupt as soon as you detect domain wipe out Dynamic variable ordering –Apply domino effect 51

Problem Solving with Constraints Intelligent Backtracking Algorithms 1 Problem Solving with Constraints CSCE496/896, Fall2011: www.cse.unl.edu/~choueiry/F11-496-896.

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Problem Solving with Constraints Intelligent Backtracking Algorithms 1 Problem Solving with Constraints CSCE496/896, Fall2011: www.cse.unl.edu/~choueiry/F11-496-896.

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Presentation on theme: "Problem Solving with Constraints Intelligent Backtracking Algorithms 1 Problem Solving with Constraints CSCE496/896, Fall2011: www.cse.unl.edu/~choueiry/F11-496-896."— Presentation transcript:

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