Alexander Demin

Alexander Demin, Denis Ponomaryov

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.

Vladimir Burlakov, Alena Rybakina, Sergey Kudashev et al.

Monte Carlo Tree Search (MCTS) has been proposed as a transformative approach to join-order optimization in database query processing, with recent frameworks such as AlphaJoin and HyperQO claiming to outperform traditional methods. However, the fact that these frameworks rely on learned cost models raises concerns related to generalizability and deployment readiness. This paper presents a comprehensive reproducibility study of these methods, revealing that they often fail to support the claimed performance gains when subjected to diverse workloads. Through an ablation study, we diagnose the root cause of this instability: while the MCTS search strategy is effective, the accompanying learned cost models suffer from severe out-of-distribution generalization errors. Addressing this, we propose a novel MCTS framework. Unlike prior methods that rely on unstable learned components, our approach utilizes the database standard internal cost model, augmented by a new Extreme UCT (Upper Confidence Bound applied to Trees) selection policy to navigate the search space more robustly. We benchmark our method against the original AlphaJoin and HyperQO, as well as industry-standard baselines including Dynamic Programming (DP) and Genetic Query Optimization (GEQO), using the well-known Join Order Benchmark (JOB) and the new JOB-Complex benchmark. The results demonstrate that our approach outperforms learned MCTS methods and achieves superiority over a SOTA query optimizer in complex join scenarios on real-world data. We release the full implementation and experimental artifacts to support further research.

Alexander Demin, Denis Ponomaryov

We propose a novel Reinforcement Learning model for discrete environments, which is inherently interpretable and supports the discovery of deep subgoal hierarchies. In the model, an agent learns information about environment in the form of probabilistic rules, while policies for (sub)goals are learned as combinations thereof. No reward function is required for learning; an agent only needs to be given a primary goal to achieve. Subgoals of a goal G from the hierarchy are computed as descriptions of states, which if previously achieved increase the total efficiency of the available policies for G. These state descriptions are introduced as new sensor predicates into the rule language of the agent, which allows for sensing important intermediate states and for updating environment rules and policies accordingly.