Extended Partial-Order Dynamic Backtracking Algorithm for Dynamically Changed Environments

Acodad, Yosra; Benelallam, Imade; Hammoujan, Saida; Bouyakhf, El Houssine

doi:10.1109/ictai.2012.83

Cited by 3 publications

(6 citation statements)

References 4 publications

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Order By: Relevance

“…As deg(X1) = deg(X4), any order is chosen, we assume that in similar cases, the chosen order is the lexicographic one. Thus, the nogood is generated as (6). X4 changes the value, then, C 14 is consistent (line 8) and it is moved to free cstrs.…”

Section: An Example Runmentioning

confidence: 99%

“…The next constraint to check is C 24 which is violated (line 9), ( 5) and ( 6) are tested, then, ( 7) is generated, the value of X4 is changed (line 10) and C 14 , the only constraint in free cstrs concerning X4, is moved to to check list. The next checked constraint is C 23 which is violated (line 11), the tested orders are (5) then (6), and (7). As no order exists between the two variables, the chosen heuristic is used to generate (8), then, the value of X3 is changed (line 12).…”

Section: An Example Runmentioning

confidence: 99%

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Lazy Repairing Backtracking for Dynamic Constraint Satisfaction Problems

Acodad

Benamrane

Bouyakhf³

et al. 2021

cai

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Extended Partial Dynamic Backtracking (EPDB) is a repair algorithm based on PDB. It deals with Dynamic CSPs based on ordering heuristics and retroactive data structures, safety conditions, and nogoods which are saved during the search process. In this paper, we show that the drawback of both EPDB and PDB is the exhaustive verification of orders, saved in safety conditions and nogoods, between variables. This verification affects remarkably search time, especially since orders are often indirectly deduced. Therefore, we propose a new approach for dynamically changing environments, the Lazy Repairing Backtracking (LRB), which is a fast version of EPDB insofar as it deduces orders directly through the used ordering heuristic. We evaluate LRB on various kinds of problems, and compare it, on the one hand, with EPDB to show its effectiveness compared to this approach, and, on the other hand, with MAC-2001 in order to conclude, from what perturbation rate resolving a DCSP with an efficient approach can be more advantageous than repair.

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Section: An Example Runmentioning

confidence: 99%

Section: An Example Runmentioning

confidence: 99%

Lazy Repairing Backtracking for Dynamic Constraint Satisfaction Problems

Acodad

Benamrane

Bouyakhf³

et al. 2021

cai

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show abstract

“…We suppose that the DCSP has undergone a disturbance that is the change of the nature of the constraint C AB , that was: A = B, and that became: A < B. To satisfy this constraint, the EP DB algorithm [1] is restarted in order to repair solution. EP DB chooses to change the value of A, or the one of B, depending on the used heuristic.…”

Section: Execution Of Heuristicmentioning

confidence: 99%

Profound Degree: A Conservative Heuristic to Repair Dynamic CSPs

Acodad

Benamrane

Benelallam

et al. 2014

IFIP Advances in Information and Communication Technology

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Part 4: Hybrid - Changing EnvironmentsInternational audienceFor a better treatment of Dynamic Constraint Satisfaction Problems (DCSPs), several techniques have been developed to be used in repair algorithms. We cite, for example, the variables/values ordering heuristics and local search techniques.We distinguish between static heuristics, which calculate their values once at the beginning of the search, and dynamic heuristics that use an expensive intelligence in terms of solving time.In this paper, we propose a new static variable ordering heuristic, Profound Degree (pdeg), based on deg heuristic, which calculates the degree of influence of a given variable, on the whole constraints network, relatively to its position in the network.We evaluate this heuristic on the Extended Partial-order Dynamic Backtracking (EPBD) approach, which is an approach to repair DCSPs solutions, and we compare it to the best-known variables ordering heuristics (VOHs) for repairing. The evaluation of performance is on random binary problems and meeting scheduling problems, with the criteria of computation time, number of constraints checks and Hamming distance between the former and the current solution

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“…And that the question, "How can we exploit this flexibility ?" has been remained open till our recent contribution on the Dynamic Variable Ordering heuristics [17].…”

Section: Definition 33 (Dcsp)mentioning

confidence: 99%

“…If RepairSolution(x i , C) cannot assign a value to the variable x i (e.g. domain wipe-out), it builds a nogood ng that is the result of the nogood resolution of all nogoods where x i appears in the right hand side of the resolved nogood Rhs(ng) using the function N gResolution(x i )(lines [16][17]. If the resolved nogood is empty, it means that no solution can be found and the search is ended (lines 18 to 20), else the function RepairSolution(Rhs(ng), C) is called recursively and tries to find a new instantiation to the variable Rhs(ng).…”

Section: Definition 33 (Dcsp)mentioning

confidence: 99%

Dynamic constraint ordering heuristics for dynamic CSPs

Hammoujan¹,

Acodad²,

Benelallam³

et al. 2013

ams

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Many research studies have shown that the choice of a relevant order of variables and/or values can significantly improve the efficiency of search algorithms. However, in Constraint Satisfaction Problems CSPs, constraint ordering heuristics have received less attention than variables and values ordering heuristics. In the literature, only some few contributions have been done on this area, and mainly as a preprocessing phase before starting the search process. In this paper, we show that constraint ordering heuristics are promising and able to make relevant choices during the search process within a Dynamic CSP. Our proposed approach, called Dynamic Constraint Ordering heuristic DCO, selects dynamically constraints which are likely to lead to conflicts. So, we apply a "fail first" principle, in a manner that a dynamic intelligent backtracking event can be performed earlier. This method is able to guide a dynamic repair algorithm towards the hardest constraint subproblems and to tackle inconsistencies in order to correct solution assignment. The conducted experiments have confirmed that the proposed approach is interesting. Our results show clearly the efficiency of the heuristics combined with Partial-Order Dynamic Backtracking

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Extended Partial-Order Dynamic Backtracking Algorithm for Dynamically Changed Environments

Cited by 3 publications

References 4 publications

Lazy Repairing Backtracking for Dynamic Constraint Satisfaction Problems

Lazy Repairing Backtracking for Dynamic Constraint Satisfaction Problems

Profound Degree: A Conservative Heuristic to Repair Dynamic CSPs

Dynamic constraint ordering heuristics for dynamic CSPs

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