  Probabilistic Law Discovery (PLD) is a logic based Machine Learning method,
which implements a variant of probabilistic rule learning. In several aspects,
PLD is close to Decision Tree/Random Forest methods, but it differs
significantly in how relevant rules are defined. The learning procedure of PLD
solves the optimization problem related to the search for rules (called
probabilistic laws), which have a minimal length and relatively high
probability. At inference, ensembles of these rules are used for prediction.
Probabilistic laws are human-readable and PLD based models are transparent and
inherently interpretable. Applications of PLD include
classification/clusterization/regression tasks, as well as time series
analysis/anomaly detection and adaptive (robotic) control. In this paper, we
outline the main principles of PLD, highlight its benefits and limitations and
provide some application guidelines.
