Super Solutions in Constraint Programming

Abstract

Abstract.To improve solution robustness, we introduce the concept of super solutions to constraint programming. An (a, b)-super solution is one in which if a variables lose their values, the solution can be repaired by assigning these variables with a new values and at most b other variables. Super solutions are a generalization of supermodels in propositional satisfiability. We focus in this paper on (1,0)-super solutions, where if one variable loses its value, we can find another solution by re-assigning this variable with a new value. To find super solutions, we explore methods based both on reformulation and on search. Our reformulation methods transform the constraint satisfaction problem so that the only solutions are super solutions. Our search methods are based on a notion of super consistency. Experiments show that super MAC, a novel search-based method shows considerable promise. When super solutions do not exist, we show how to find the most robust solution. Finally, we extend our approach from robust solutions of constraint satisfaction problems to constraint optimization problems.

Constraint-Based Local Search for the Automatic Generation of Architectural Tests

“…Test Scenario Constraints that specify the intention of a validation engineer. 3. State Constraints that specify the current architectural state maintained by the test generator.…”

“…In this paper, we reconsider the issue of producing diverse solutions for ATGPs. We propose to view the ATGP as a MaxDiversekSet problem in the sense of [3] over the CCSP and to use constraint-based local search to obtain high-quality solutions to the model. We show that the resulting approach produces significant improvements in solution diversity compared to pseudo-random solutions, while the running times remain reasonable.…”

“…The goal of this paper is to provide a more principled approach to diversity for ATGPs. For this reason, ATGPs are modeled as MaxDiversekSet problems, which were studied extensively in [3]. More precisely, given a CSP P, its set of solutions sol (P), and a function δ to measure the distance between solutions, the MaxDiversekSet problem for P consists in finding a set of solutions S = {s 1 , .…”

“…This problem is in general quite difficult from a practical standpoint, since it requires to search for k solutions simultaneously and produces very large CSPs (See also [3] for the theoretical complexity which is F P N P [logn] -complete). Moreover, in this application domain, it is often desirable to produce the solutions incrementally, one at a time.…”

A Declarative Paradigm for Robust Cumulative Scheduling

“…All variables have the same status. It has been applied to jobshop benchmarks [8]. A low-performance technique for obtaining robust schedules is to augment the duration of each activity.…”