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New Relational Consistency Algorithms and a Flexible Solver Architecture for Integrating Them during Search
Abstract
Consistency algorithms, which perform inference, are at the heart of Constraint Programming. The strongest consistency level provided in most constraint solvers is Generalized Arc Consistency (GAC). In recent years, higher-level consistencies, especially relational consistencies, were shown to be critical for solving difficult Constraint Satisfaction Problems (CSPs). Implementing algorithms that enforce such consistencies in existing solvers cannot be done in a flexible and transparent manner and may require significantly modifying the constraint model of the CSP. In this thesis, we address the practical mechanics for making higher-level consistencies a pragmatic choice for solving CSPs. To this end, we present three main contributions. First, we design and implement a new generation of constraint solvers with an architecture open to development and integration of new domain and relation-filtering consistency algorithms where one or more consistency algorithms can independently operate on specific, possibly overlapping subproblems, and where the implementation of the relational consistency algorithms does not require the modification of the constraint model of the CSP. Second, we propose new algorithms for two different kinds of relational consistencies: pairwise and m-wise consistency. Finally, we describe how to dynamically identify and exploit tractable substructures during search using one of the novel pairwise consistencies developed in this dissertation.
Subject Area
Artificial intelligence|Computer science
Recommended Citation
Schneider, Anthony R, "New Relational Consistency Algorithms and a Flexible Solver Architecture for Integrating Them during Search" (2022). ETD collection for University of Nebraska-Lincoln. AAI30001033.
https://digitalcommons.unl.edu/dissertations/AAI30001033