Complexity Classification in Infinite-Domain Constraint Satisfaction
Manuel Bodirsky

TL;DR
This paper extends the complexity classification of constraint satisfaction problems (CSPs) to infinite domains, applying universal algebra and Ramsey theory to achieve new results in temporal reasoning and graph logic.
Contribution
It introduces a universal-algebraic approach combined with Ramsey theory for classifying infinite-domain CSPs, providing complete results for temporal reasoning and graph logic.
Findings
Complete complexity classifications for infinite-domain CSPs in temporal reasoning.
A generalized Schaefer's theorem for logic over graphs.
Identification of classes without a clear complexity dichotomy.
Abstract
A constraint satisfaction problem (CSP) is a computational problem where the input consists of a finite set of variables and a finite set of constraints, and where the task is to decide whether there exists a satisfying assignment of values to the variables. Depending on the type of constraints that we allow in the input, a CSP might be tractable, or computationally hard. In recent years, general criteria have been discovered that imply that a CSP is polynomial-time tractable, or that it is NP-hard. Finite-domain CSPs have become a major common research focus of graph theory, artificial intelligence, and finite model theory. It turned out that the key questions for complexity classification of CSPs are closely linked to central questions in universal algebra. This thesis studies CSPs where the variables can take values from an infinite domain. This generalization enhances dramatically…
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