Constraint Programming is an attractive approach for solving AI planning problems by modelling them as Constraint Satisfaction Problems (CSPs). However, formulating effective constraint models of complex planning problems is challenging, and CSPs resulting from standard approaches often require further enhancement to perform well. Common subexpression elimination is a computationally cheap and ...

Variable or value elimination in a constraint satisfaction problem (CSP) can be used in preprocessing or during search to reduce search space size. A variable elimination rule (value elimination rule) allows the polynomial-time identification of certain variables (domain elements) whose elimination, without the introduction of extra compensatory constraints, does not affect the satisfiability o...

This paper presents new look-ahead schemes for backtracking search when solving constraint satisfaction problems. The look-ahead schemes compute a heuristic for value ordering and domain pruning, which influences variable orderings at each node in the search space. As a basis for a heuristic, we investigate two tasks, both harder than the CSP task. The first is finding the solution with min-num...

Bucket elimination (BE) is an exact technique based on variable elimination, commonly used for solving constraint satisfaction problems. We consider the hybridization of BE with evolutionary algorithms endowed with tabu search. The resulting memetic algorithm (MA) uses BE as a mechanism for recombining solutions, providing the best possible child from the parental set. This MA is applied to the...

Bucket elimination is an algorithmic framework that generalizes dynamic programming to accommodate many complex problem-solving and reasoning tasks. Algorithms such as directional-resolution for proposi-tional satissability, adaptive-consistency for constraint satisfaction, Fourier and Gaussian elimination, for solving linear equalities and inequalities and dynamic programming for combinatorial...

Typically, there are many alternative models of a given problem as a constraint satisfaction problem, and formulating an effective model requires a great deal of expertise. To reduce this bottleneck, automated constraint modelling systems allow the abstract specification of a problem, which can then be refined automatically to a solver-independent modelling language. The final step is to tailor...

In this survey paper on our work in the field of constraint solving techniques, we discuss generalizations of constraint solving over various domains. These generalizations comprise paradigms from both computational algebra (e.g. parameterization) as well as from logic (e.g. expressiveness of full first-order logic). The domains include real numbers, p-adic numbers, integers, differential field...

Many program analyses are naturally formulated and implemented using inclusion constraints. We present new results on the scalable implementation of such analyses based on two insights: rst, that online elimination of cyclic constraints yields orders-of-magnitude improvements in analysis time for large problems; second, that the choice of constraint representation aaects the quality and eecienc...

We present an extension of constraint logic programming, where the admissible constraints are arbitrary first-order formulas over some domain. Constraint solving is realized by effective quantifier elimination. The current realization in our CLP(RL) system provides as possible domains R, C, and Qp for primes p. In R and C we admit arbitrary degrees in our constraints. In Qp we restrict to linea...

Whenever we have data represented by constraints (such as order, linear, polynomial, etc.), running time for many constraint processing algorithms can be considerably lowered if it is known that certain variables in those constraints are independent of each other. For example, when one deals with spatial and temporal databases given by constraints, the projection operation, which corresponds to...