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Refining the Basic Constraint Propagation Algorithm
Christian Bessière and Jean-Charles Régin
Presented by Sricharan Modali
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Outline AC3 Two refinements
AC2000 AC2001
Experiments Analytical comparison of AC2001 &
AC6 Conclusion
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Introduction Importance of constraint
propagation Propagation scheme of most of
existing constraint solving engines Constraint oriented, or Variable oriented
AC3 is a generic algorithm AC4, AC6 & AC7: value oriented
propagation
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Importance of AC3 When you know constraint semantics, use
special propagation algorithms (e.g., all-diff, functional)
When nothing is known about constraints, use a generic AC algorithm (e.g., AC1, 2, 3, 4, 6 or 7)
AC3 does not require maintaining a specific data structures during search, in contrast to AC4, AC6 & AC7
Authors focus on AC3: generic and light weight
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Contribution
Modify AC3 into AC2000 & AC2001 More efficient with heavy
propagation Light weight data structures
Variable-oriented Dominate AC3 (# CC & CPU time)
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AC2000, like AC3, is free of any data structure to be maintained during search(not really: authors use (Xi) per variable)
Regarding human cost of implementation AC2000 needs 5 more lines than AC3
AC2000 vs. AC3
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AC2001 vs. AC3 AC2001 needs an extra data
structure, an integer for each value-constraint pair: Last(Xi,vi,Xj)
Achieves optimal worst-case time complexity
Human cost (implementation): AC2001 needs management of additional data structure ((Xi), Last(Xi,vi,Xj))
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Constraint networkP = (X, D, C) X is a set of n variables {X1, …, Xn} D is a set of domains {D(X1), …, D(Xn)} C is a set of e binary constraints
between pairs of variables. Constraint check: verifying whether or
not a given pair of values (vi,vj) is allowed by Cij
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Arc consistent valuevi is an arc-consistent value on Cij:
vi D(Xi) vj D(Xj) | (vi,vj) Cij
vj is called support for (Xi,vi) on Cij
XiXj
vivj
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Viable value Viable value: vi D(Xi) is viable it
has support in all neighboring D(Xj)
Arc consistent CSP: if all the values in all the domains are viable.
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AC3 A variable-oriented propagation
scheme Difference with [Mackworth 77]
Instead of handling a queue for the constraints to be propagated,
it has a queue of the variables whose domain has been modified.
This AC3 terminates whenever any domain is empty
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A (bad) example for AC3
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AC3 overdoes it Revise3(Xj,Xi) removes vj from D(Xj) AC puts Xj in Q Propagate3 calls Revise3(Xi,Xj) for
every constraint Cij involving Xj
Revise3(Xi,Xj) will look for a support for every value in D(Xi) even when vj was not a support!
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Enhancement in AC2000 Instead of blindly looking for a
support for a value viD(Xi) each time D(Xj) is modified, it is done only when needed
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AC2000
In addition to Q, (Xj) is used (Xj) contains the values removed
from D(Xj) since the last propagation of Xj
When calling Revise2000(Xi,Xj,t) a check is made to see if vi has a support in (Xj)
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Example
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How AC2000 operates The larger (Xj),
the closer it gets in size to D(Xj) the more expensive the process is the more likely for vi to have a support in (Xj)
Hence lazymode is used only when |(Xj)| is sufficiently smaller than |D(Xj)|
Use of lazymode is controlled with Ratio |(Xj) |/ |D(Xj)| < Ratio, use lazymode |(Xj) |/ |D(Xj)| Ratio, use lazymode
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Analysis of AC2000
Assumption: AC3 is correct Prove: Lazymode of AC2000 does not lead
to arc-inconsistent values in the domain The only way the search for support for a value
vi in D(Xi) is skipped is when vi is not supported by values in (Xj)
(Xj) contains all values last deleted from D(Xj) vi has exactly the same set of supports as before on Cij
Looking again for a support for vi is useless as it remains consistent with Cij
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Space complexity of AC2000 It is bounded by the sizes of Q and
Q is O(n), is O(nd) d is the size of the largest domain
Overall complexity O(nd)
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Time Complexity of AC2000 The main change is in Revise2000,
where both (Xj) and D(Xj) are examined instead of only D(Xj)
This leads to a worst case where d2 checks are performed in Revise2000
Hence the overall time complexity is O(ed3) since Revise2000 can be called d times per constraint.
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AC2000 too overdoes it.. In AC2000 we have to look again for a
support for vi on Cij
If we can remember the support found for vi in D(Xj) the last time Cij is revised
Next time we need to check whether or not this last support belongs to (Xj).
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AC2001 saves more.. A new data structure Last(Xi,vi,Xj) is used
to store the value that supports vi
The function Revise2001 always runs in lazymode, except during the initialization phase.
Further, when supports are checked in a given ordering “<d” (i.e., sorted) we know that there isn’t any support for vi before Last(Xi,vi,Xj) in D(Xj).
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Example
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Space complexity of AC2001 Is bounded above by the size of Q,
and Last Q is in O(n) is in O(nd) But Last is in O(ed)
Since each value vi has a Last pointer for each constraint involving Xi.
This gives the overall complexity of O(ed)
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Time Complexity of AC2001 As in AC3 & AC2000, Revise2001 can be
called d times per constraint. But at each call to Revise2001(Xi,Xj,t)for
each value vi D(Xi) There will be a test on the Last(Xi,vi,Xj) And a search for support on D(Xj) greater than
Last(Xi,vi,Xj)
The overall time complexity is then bounded above by d(d+d)2e, which is in O(ed2)
O(ed2) is optimal AC2001 is the first optimal arc-consistency algorithm
proposed in the literature that is free of any lists of supported values.
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Experiments To see if AC2000 and AC2001 are
effective vs. AC3, compare #CC & CPU Context: pre-processing & search
(MAC) The goal is not to compete with
AC6/AC7 An improvement (even small) w.r.t
AC3 is significant
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AC as a preprocessing The chance of having some propagations
are very small on real instances Hence only one real – world instance is
considered Other instances are randomly generated
to fall in the phase transition region Ratio of 0.2 is taken (no justification given)
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Parameters <N, D, C/p1, T/p2> N number of variables D size of the domain C number of constraints P1 density of constraints 2C/N.(N-1) T number of forbidden tuples P2 tightness of the forbidden tuples T/D2
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Results
Low density (p1=0.045) Instance 1: under-constrained (p2=0.5) Instance 2: over-constrained (p2 =0.94)
High tightness (p2=0.918, 0.875) Instance 3: sparse (p1=0.045) Instance 4: dense (p1=1.0)
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Observation
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Maintaining Arc consistency during search
MAC-3, MAC-2000, MAC-2001 Experiments carried over all the
instances contained in FullRLFAP archive for which more than 2 secs is necessary to find a solution or to prove that none exists
Ratio is again 0.2 (no justification given)
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Results
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Observations There is a slight gain of MAC2000
over MAC3 Except for SCEN#11 On SCEN#11 it is seen that MAC2000
outperforms MAC3 for ratio 0.1 MAC2001 outperforms MAC3 with 9
times less CC and 2 times less cpu time
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Restrictions Comparison is between algorithms
with simple data structures Note that to solve SCEN#11
MAC-6 (MAC + AC6): 14.69 sec MAC3 needs 39.50 sec MAC2000 needs 38.22 sec MAC2001 needs 22.69 sec
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AC2001 vs. AC6 Time complexity and space
complexity of AC2001 is equal to that of AC6
What are the differences between AC6 and AC2001? Property1: #CC same! Property2: Difference is in the effort of
maintaining specific data structures Authors give condition who wins when
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Conclusion Two refinements to AC3: AC2000 &
AC2001 AC2000 improves slightly over AC3,
w/o maintenance of any new data structure
AC2001 needs an additional data structure Last
AC2001 achieves optimal worst-case time complexity
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Thanks