Learning Probabilistic Sentential Decision Diagrams by Sampling

  • Renato Geh University of São Paulo
  • Denis Mauá University of São Paulo
  • Alessandro Antonucci Istituto Dalle Molle di Studi sull'Intelligenza Artificiale

Resumo


Probabilistic circuits are deep probabilistic models with neural-network-like semantics capable of accurately and efficiently answering probabilistic queries without sacrificing expressiveness. Probabilistic Sentential Decision Diagrams (PSDDs) are a subclass of probabilistic circuits able to embed logical constraints to the circuit’s structure. In doing so, they obtain extra expressiveness with empirical optimal performance. Despite achieving competitive performance compared to other state-of-the-art competitors, there have been very few attempts at learning PSDDs from a combination of both data and knowledge in the form of logical formulae. Our work investigates sampling random PSDDs consistent with domain knowledge and evaluating against state-of-the-art probabilistic models. We propose a method of sampling that retains important structural constraints on the circuit’s graph that guarantee query tractability. Finally, we show that these samples are able to achieve competitive performance even on larger domains.

Palavras-chave: artificial intelligence, machine learning, probabilistic circuits, probabilistic sentential decision diagrams, propositional logic

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Publicado
20/10/2020
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GEH, Renato; MAUÁ, Denis; ANTONUCCI, Alessandro. Learning Probabilistic Sentential Decision Diagrams by Sampling. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 8. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 129-136. DOI: https://doi.org/10.5753/kdmile.2020.11968.