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Recent approaches to causal discovery based on Boolean satisfiability solvers have opened new opportunities to consider search spaces for causal models with both feedback cycles and unmea-sured confounders. However, the available methods have so far not been able to provide a prin-cipled account of how to handle conflicting constraints that arise from(More)
etermining whether a given propositional logic formula is satisfiable is one of the most fundamental problems in computer science, known as the canonical NP-complete Boolean satisfiability (SAT) problem (Biere et al. 2009). In addition to its theoretical importance, major advances in the development of robust implementations of decision procedures for SAT,(More)
We present a very general approach to learning the structure of causal models based on d-separation constraints, obtained from any given set of overlapping passive observational or experimental data sets. The procedure allows for both directed cycles (feedback loops) and the presence of latent variables. Our approach is based on a logical representation of(More)
Constraints are a major factor shaping the conceptual space of many areas of creativity. We propose to use constraint programming techniques and off-the-shelf constraint solvers in the creative task of poetry writing. We show how many aspects essential in different poetical forms, and partially even in the level of language syntax and semantics can be(More)
This work presents a classification of weak models of distributed computing. We focus on deterministic distributed algorithms, and we study models of computing that are weaker versions of the widely-studied port-numbering model. In the port-numbering model, a node of degree <i>d</i> receives messages through <i>d</i> input ports and it sends messages(More)