Constraint programming and AI planning are powerful tools for solving assignment, optimization, and scheduling problems. They require, however, the rarely available combination of domain knowledge and mathematical modeling expertise. Learning constraints from exemplary solutions can close this gap and alleviate the effort of modeling. Existing approaches either require extensive user interaction, need exemplary invalid solutions that must be generated by experts at great expense, or show high noise-sensitivity. We aim to find constraints from potentially noisy solutions, without the need of user interaction. To this end, we formalize the problem in terms of the Minimum Description Length (MDL) principle, by which we select the model with the best lossless compression of the data. Solving the problem involves model counting, which is #P-hard to approximate. We therefore propose the greedy URPILS algorithm to find high-quality constraints in practice. Extensive experiments on constraint programming and AI planning benchmark data show URPILS not only finds more accurate and succinct constraints, but also is more robust to noise, and has lower sample complexity than the state of the art.

Queueing models explain waiting times, predict sojourn times and help to identify and avoid bottlenecks. Domain experts usually create these models by intensive handcrafting, often resulting in idealized models not fitting the actual process behavior well. Discovering queueing models from data can alleviate this effort, but existing methods do not suffice as they are unable to model complex queueing behaviors. We propose a novel approach to discover queueing models for interpretable waiting time prediction using a rich modeling language to fit complex processes. We formalize the problem in terms of the Minimum Description Length (MDL) principle, by which the best model gives the best lossless compression. The resulting optimization problem is computationally hard, and hence we propose the greedy CueMin algorithm to efficiently find good queueing models from data. Through an extensive set of experiments including a case study on call center data, we show it discovers inherently interpretable models, which explain and predict behavior of waiting lines better than the state of the art.

We study the problem of predicting an event sequence given some meta data. In particular, we are interested in learning easily interpretable models that can accurately generate a sequence based on an attribute vector. To this end, we propose to learn a sparse event-flow graph over the training sequences, and statistically robust rules that use meta data to determine which paths to follow. We formalize the problem in terms of the Minimum Description Length (MDL) principle, by which we identify the best model as the one that compresses the data best. As the resulting optimization problem is NP-hard, we propose the efficient ConSequence algorithm to discover good event-flow graphs from data. Through an extensive set of experiments including a case study, we show that it ably discovers compact, interpretable and accurate models for the generation and prediction of event sequences from data, has a low sample complexity, and is particularly robust against noise.