Computational Short Cuts in Infinite Domain Constraint Satisfaction
Peter Jonsson, Victor Lagerkvist, Sebastian Ordyniak

TL;DR
This paper extends the concept of backdoors to infinite-domain CSPs with higher-arity constraints, analyzing their computational properties and introducing sidedoors as an alternative to improve AI applications.
Contribution
It generalizes the backdoor concept to broader CSP classes, analyzes their complexity, and proposes sidedoors as a more effective alternative in certain scenarios.
Findings
Finite constraint languages lead to fixed-parameter tractable problems.
Infinite languages make backdoor detection W[2]-hard, losing fixed-parameter tractability.
Sidedoors can outperform backdoors in some cases.
Abstract
A backdoor in a finite-domain CSP instance is a set of variables where each possible instantiation moves the instance into a polynomial-time solvable class. Backdoors have found many applications in artificial intelligence and elsewhere, and the algorithmic problem of finding such backdoors has consequently been intensively studied. Sioutis and Janhunen (Proc. 42nd German Conference on AI (KI-2019)) have proposed a generalised backdoor concept suitable for infinite-domain CSP instances over binary constraints. We generalise their concept into a large class of CSPs that allow for higher-arity constraints. We show that this kind of infinite-domain backdoors have many of the positive computational properties that finite-domain backdoors have: the associated computational problems are fixed-parameter tractable whenever the underlying constraint language is finite. On the other hand, we show…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
