Figure 16: Screenshot of the dashboard used for data exploration. Since experiments usually produce large amounts of data that can be hard to get a sense of, we created a data visualization dashboard. Given a folder containing the JSON logs of a job, it offers a user interface to explore the influence of the controls. For each parameter of each control, we can pick one out three mode: Heat map axis: This control will be used as the x or y axis of the heat map. Exactly two controls should be assigned to this mode to enable the visualization.

Characterising tractable fragments of the constraint satisfaction problem (CSP) is an important challenge in theoretical computer science and artificial intelligence. Forbidding patterns (generic sub-instances) provides a means of defining CSP fragments which are neither exclusively language-based nor exclusively structure-based. It is known that the class of binary CSP instances in which the broken-triangle pattern (BTP) does not occur, a class which includes all tree-structured instances, are decided by arc consistency (AC), a ubiquitous reduction operation in constraint solvers. We provide a characterisation of simple partially-ordered forbidden patterns which have this AC-solvability property. It turns out that BTP is just one of five such AC-solvable patterns. The four other patterns allow us to exhibit new tractable classes.