Systematic search guided by local search with conflict based heuristic in n queen problem
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Systematic Search Guided by Local Search with Conflict-based Heuristic in N-queen problem. Hyoung rae Kim Debasis Mitra Ph.D. Florida Institute of Technology Department of Computer Science. Contents. Introduction Proposed method Implementation design Experiments and analysis Related work

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Systematic search guided by local search with conflict based heuristic in n queen problem

Systematic Search Guided by Local Search with Conflict-based Heuristic in N-queen problem

Hyoung rae Kim

Debasis Mitra Ph.D

Florida Institute of Technology

Department of Computer Science


Contents
Contents Heuristic in N-queen problem

  • Introduction

  • Proposed method

  • Implementation design

  • Experiments and analysis

  • Related work

  • Conclusion

  • Future works

  • References

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1 introduction
1. Introduction Heuristic in N-queen problem

  • Constraint Satisfaction Problem(CSP) does very important role in Artificial Intelligence (AI). CSP appears in many areas, for instance vision, resource allocation in scheduling and temporal reasoning [2].

  • What is a constraint satisfaction problem

    • A CSP is a problem composed of a finite set of variables, each of which is associated with a finite domain, and a set of constraints.

    • The task is to assign a value to each variable satisfying all the constraints.

1


Resource allocation in scheduling
Resource allocation in scheduling Heuristic in N-queen problem

[2]

1


N queens problem
N-queens problem Heuristic in N-queen problem

  • Place eight queens on an 8 × 8 chessboard satisfying the constraint that no two queens should be on the same row, column or diagonal.

[4 × 4 queens problem]

1


N-queens problem Heuristic in N-queen problem

  • Problem formalization

    • The set of variables: Z = {Q1, Q2, …, Q8}

    • Domain: DQ1 = DQ2 = … = DQ8 = {1,2,3,4,5,6,7,8}

    • Constraint (1): i,j: QiQj

    • Constraint (2): i,j, if Qi=a and Qj=b, then i-j  a-b, and i-j  b-a.

  • The variable is considered row number.

  • The domain of each variable is set of column numbers.

1


Problem reduction and search Heuristic in N-queen problem

  • There are two approaches to solve CSP

  • Problem reduction

    • Pruning off search spaces that contain no solution

    • Reducing the size of domains of the variables* Tightening constraints potentially reduce the search space at a later stage of the search

    • Pruning off branches in the search space

    • It can be performed at any stage of the search

  • Search

    • Find solution in the search space, all or one of the solutions.

  • One often has to find a balance between the efforts made and the potential gains in problem reduction.

1


An example of a search space Heuristic in N-queen problem

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Search strategies
Search strategies Heuristic in N-queen problem

  • Systematic algorithm

    • Starts from an empty variable assignment that is extended until obtaining a complete assignment that satisfies all the constraints in the problem.

    • Look-back enhancements (backward checking, back jumping, etc.)

    • Look-ahead enhancements (forward checking, etc.)

  • Local search algorithm

    • Perform an incomplete exploration of the search space by repairing infeasible complete assignments (min-conflict, GSAT, tabu search).

  • Hybrid approach

    • Performing a local search before or after a systematic search.

    • Performing a systematic search improved with a local search at some point of the search.

    • Performing an overall local search, and using systematic search either to select a candidate neighbor or to prune the search space [1].

1


The contributions of this work
The contributions of this work Heuristic in N-queen problem

  • Explain the relationship between Local search algorithm (MC) and Systematic algorithm (FC).

  • Trying to find faster searching algorithm by combining them.

1


2 proposed method
2. Proposed method Heuristic in N-queen problem

  • We improve the speed by hybrid of Forward Checking and Min-Conflict: Forward checking after Min-conflict.

  • We examine the complexity and accuracy as gradually varying the coverage of Min-conflict.

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Forward checking algorithm
Forward checking algorithm Heuristic in N-queen problem

(12)

(12)

(4)

(4)

(5)

(1)

(2)

Total comparison: 40

(0)

1


Forward checking algorithm1
Forward checking algorithm Heuristic in N-queen problem

FC1 (UNLABELLED, COMPOUND_LABEL, D,C){

if (UNLABELLED={}) {return UNLABELLED;};

Pick one variable x from UNLABELLED;

{

pick one value v from Dx; Delete v from Dx;

D’=Update1(UNLABELLED-{X}, D, C, <x,v>);

Result = FC1 (UNLABELLED-{X}, COMPOUND_LABEL+{<x,v>}, D’, C);

if (Result != Nil) {return Result;};

} until (Dx={});

return (NIL);

}

S


Update1(W,D,C,Lable) Heuristic in N-queen problem

{

D’=D;

for each variable y in W {

for each value v in D’y {

if (<y,v> is incompatible with Label with respect to the constraints in C)

D’y=D’y-{v};

}

}

return D’;

}

S


Min conflict algorithm

3 Heuristic in N-queen problem

2

3

1

Min-conflict algorithm

Initial status

Checking (1)

Ordering (16)

Checking (1)

Checking (1)

Ordering (10)

Ordering (7)

Checking (3)

C(3)

Checking (5)

Ordering (8)

Total comparison: 71

Checking (2)

2

Ordering (7)


Min conflict algorithm1
Min-conflict algorithm Heuristic in N-queen problem

Informed_Backtrack(Z,D,C)

{

LEFT = {};

for each variable x in Z {

pick a random value from Dx;

add <x,v> to LEFT;

}

InfBack(LEFT, {}, D, C);

}

S


InfBack(LEFT, DONE, D,C) Heuristic in N-queen problem

{

if (LEFT+DONE is compatible with constraints)

{return LEFT+DONE;};

x = any variable such that label <x,v> is in LEFT;

Queue = Order_values(x, Dx, Labels_left, Labels_done, C);

while (Queue != {}){

w = first element in Queue; Delete w from Queue;

DONE = DONE + {<x,w>};

Result = InfBack(LEFT-{<x,v>}, DONE, D, C):

if (Result != Nil) {return Result;};

}

return Nil;

}

S


Order_values(x, Dx, LEFT, DONE, C) Heuristic in N-queen problem

{

List = {};

for each v in Dx {

if (<x,v> is compatible with all the labels in DONE)

{

Count [v] = 0;

for each <y,w> in LEFT {

if NOT satisfies ((<x,v><y,w>), Cx,y)

Count[v]=count[v]+1;

}

List = List + {v};

}

}

Queue = the values in List ordered in ascending order of Count[v];

return Queue;

}

S


Comparison between mc and fc
Comparison between MC and FC Heuristic in N-queen problem

  • Forward checking (FC)

    • Advantage: Completeness – it always find a solution if one exists. One of the best Systematic algorithm.

    • Disadvantage: FC is typically cursed with early mistakes in the search, a wrong variable value can cause a whole sub-tree to be explored with no success.

  • Min-conflict (MC)

    • Advantage: Do not suffer from the early-mistake problem. It may be far more efficient than systematic ones to find a first solution.

    • Disadvantage: Not complete. It can be undone, without having anything to prove.

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Explanation of hybrid method
Explanation of hybrid method Heuristic in N-queen problem

  • Forward checking after Min-conflict.

  • K=0 means pure FC, K=n means pure MC.

Solve this portion by MC

K

* Vary this K value

Solve this portion by FC

1

[8-queens problem]


3 implementation design
3. Implementation design Heuristic in N-queen problem

  • Input variable: N-queens problem

  • Output variable:

    • Counted number of visited label.

    • Counted number of executed constraints.

  • MC-FC algorithm runs MC and then FC with the results from MC.

  • We use standard MC algorithm [2].

  • We use standard FC algorithm [2].

1


Hybrid algorithm
Hybrid algorithm Heuristic in N-queen problem

K=2

MC

FC

2


Hybrid algorithm1
Hybrid algorithm Heuristic in N-queen problem

SEARCH (n) {

for each k=0 to n

1. MC_FC (k, Success, Count_Label, Count_Constraint);

2. print (k, Success, Count_Label, Count_Constraint);

}

MC_FC (k, Success, Count_Label, Count_Constraint) {

Repeat until it gets a result or reach to the max iteration

1. Initialize cZ, cD, CC

2. COMPOUND_LABEL = MC(k, cZ, cD, cC, Count_Label, Count_Constraint);

3. If COMPOUND_LABEL is valid

Result=FC(k,COMPOUND_LABEL,

cZ,cD,cC,Count_Label,Count_Constraint)

4. If Result is valid

Success = True

return;

}

S


4 experiment and analysis
4. Experiment and analysis Heuristic in N-queen problem

  • We use 24-queens problem.

  • We ran the algorithm 300 times on a Sun Ultra 60.

  • The max iteration number was 1000 (if FC part does not have solutions, it randomly re-execute MC part).

  • We recorded every k value from 0 through n with an interval of 2.

  • An output parameter ‘Label count’ is the number of label that the algorithm visited.

  • The other parameter ‘Total count’ is the number of how many times the constraint is checked. ‘Total count’ subsumes the ‘Label count’.

  • We analyze the ‘Label count’ and ‘Total count’.

  • We use this formula to compare the quality of data points, which is often referred to as standard error of the mean:

    S.D. of Total count / Sqrt(n) [3].

1


Compare the label count
Compare the label count Heuristic in N-queen problem

1


Complexity Heuristic in N-queen problem

Pure Forward checking

Plot label count in a graph

Pure Min Conflict

1

k

k


Compare the total count
Compare the total count Heuristic in N-queen problem

1


Complexity Heuristic in N-queen problem

Pure Forward checking

Plot total count in a graph

Pure Min Conflict

1

k


Explanation of the results
Explanation of the results Heuristic in N-queen problem

  • The reason of gradual shrinking of the width.

    • 4-queens problem has two solutions with following conditions.

    • When K=1,

      • There are two solution marks (called A), it takes 4 steps to know the results.

      • There are two un-solution marks (called B), it takes 6 steps to know the results.

      • Starts with solution mark A (50%): 4

      • Starts with un-solution mark B (50%): 10

    • When K=2,

      • There are two solution marks (called A), it takes 2 steps to know the results.

      • There are four un-solution marks, 2 has 1step (called B), 2 has 2 steps (called C).

      • Starts with solution mark (34%): 2

      • Starts with un-solution mark, B -> A (16.5%) : 3

      • Starts with un-solution mark, B -> C -> A (16.5%): 5

      • Starts with un-solution mark, C -> A (16.5%): 4

      • Starts with un-solution makr, C-> B -> A (16.5%): 5

    • This tells when K=2 the S.D is much smaller.

      • Case 1=12={4,10,4,10,…}, S.D. = 3.1; Case 2=12={2,2,3,5,4,5,…}, S.D. = 1.3

2



5 related work
5. Related work the explanation.

  • A research tried to show that the look-back and look-ahead enhancements of backtracking-based algorithms can be exploited for local search algorithms, and can greatly improve their behavior too. They propose a generic search technique over CSP which is called decision-repair, which show great performance [1].

S


6 conclusion
6. Conclusion the explanation.

  • We performed a hybrid search: Performing a local search (MC) before a systematic search (FC).

  • The purpose of our research is to understand the relationship between MC and FC and to improve the speed of searching algorithm.

  • The algorithm shows the best performance when K value is in the middle.

  • We need theoretical explanation for this results.

  • Even without the theoretical explanation, the Hybrid algorithm is better than pure MC and FC.

1


7 future works
7. Future works the explanation.

  • Vary N to bigger number.

  • For other problems other than N-queens.

  • Theoretical studies for the result.

1


8 references
8. References the explanation.

  • [1] N. Jussien, O. Lhomme, Local Search with Constraint Propagation and Conflict-absed Heuristics, Artificial Intelligence 139 (2002) 21-45.

  • [2] E. Tsang, “Foundations of Constraint Satisfaction”, University of Essex Colchester Essex, UK., (1995).

  • [3] John Mandel, The statistical analysis of experimental data, Dover, (1964) 63.

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