1 citations · 2 across the 9 of their papers we have counts for
9 papers
Neurosymbolic Action Model Learning under Partial Observability
Adem Kikaj, Lennert De Smet, Giuseppe Marra +1
AI planning studies how an agent can reach a goal by executing a sequence of actions. To plan correctly, the agent needs an action model describing when each action can be executed…
Towards Zero-Shot Task Transfer with Neurosymbolic World Models
Isidoro Tamassia, Lennert De Smet, Giuseppe Marra
State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on the struct…
Parallel Noising in Neural Markov Logic Networks
Peter Jung, Giuseppe Marra, Ondrej Kuzelka
Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve strong performance as generative models for…
EM-NeSy: Expectation Maximization for Neurosymbolic Learning
Annegret Seibt, Luc De Raedt, Giuseppe Marra
Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is ex…
DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs
Ying Jiao, Rodrigo Castellano Ontiveros, Luc De Raedt +4
Neurosymbolic (NeSy) AI aims to combine the strengths of neural architectures and symbolic reasoning to improve the accuracy, interpretability, and generalization capability of AI…
DeepGraphLog for Layered Neurosymbolic AI
Adem Kikaj, Giuseppe Marra, Floris Geerts +2
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy framewor…
The DeepLog Neurosymbolic Machine
Vincent Derkinderen, Robin Manhaeve, Rik Adriaensen +4
We contribute a theoretical and operational framework for neurosymbolic AI called DeepLog. DeepLog introduces building blocks and primitives for neurosymbolic AI that make abstract…
Valid Text-to-SQL Generation with Unification-based DeepStochLog
Ying Jiao, Luc De Raedt, Giuseppe Marra
Large language models have been used to translate natural language questions to SQL queries. Without hard constraints on syntax and database schema, they occasionally produce inval…
Relational Neurosymbolic Markov Models
Lennert De Smet, Gabriele Venturato, Luc De Raedt +1
Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing. State-of-the-art deep sequential models, like transformers, excel in the…