Daniel J. Geschwender

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Computing the minimal network of a Constraint Satisfaction Problem (CSP) is a useful and difficult task. Two algorithms, PerTuple and AllSol, were proposed to this end. The performances of these algorithms vary with the problem instance. We use Machine Learning techniques to build a classifier that predicts which of the two algorithms is likely to be more(More)
In Constraint Processing, no single consistency algorithm always outperforms all others, but the problem type and characteristics often determine the most appropriate algorithm. Our goal is to understand and determine what problem features lead to better performance of one algorithm over another. As a first step in that direction, we utilize an algorithm(More)
We are developing software tools to compare the behavior of two executable procedures, written in the same or different languages. These tools are useful in situations such as verifying that an automated procedure does the same thing as its manual backup, ensuring that a change to a procedure impacts only the intended behavior, and verifying that a(More)
In Constraint Processing, many algorithms for enforcing the same level of local consistency may exist. The performance of those algorithms varies widely. In order to understand what problem features lead to better performance of one algorithm over another, we utilize an algorithm configurator to tune the parameters of a random problem generator and maximize(More)
Minimality, a highly desirable consistency property of Constraint Satisfaction Problems (CSPs), is in general too expensive to enforce. Previous work has shown the practical benefits of restricting minimality to the clusters of a tree decomposition, allowing us to solve many difficult problems in a backtrack-free manner. We explore two alternative(More)
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