2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
Machine Learning with Probabilistic Law Discovery: A Concise Introduction
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…
Interpretable Reinforcement Learning with Multilevel Subgoal Discovery
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, a…
On the Complexity of Semantic Integration of OWL Ontologies
Yevgeny Kazakov, Denis Ponomaryov
We propose a new mechanism for integration of OWL ontologies using semantic import relations. In contrast to the standard OWL importing, we do not require all axioms of the importe…
Progression of Decomposed Local-Effect Action Theories
Denis Ponomaryov, Mikhail Soutchanski
In many tasks related to reasoning about consequences of a logical theory, it is desirable to decompose the theory into a number of weakly-related or independent components. Howeve…