2 citations · 3 across the 9 of their papers we have counts for
10 papers
Narcissus: Program Synthesis Using Context-Aware LLM Approximations
Tilman Hinnerichs, Sebastijan Dumancic, Neil Yorke-Smith
Large language models (LLMs) excel at programming, but not when the task fixes the target language: prompted with a grammar rare in their training data, their programs usually brea…
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Herb.jl: A Unifying Program Synthesis Library
Tilman Hinnerichs, Reuben Gardos Reid, Jaap de Jong +6
Program synthesis -- the automatic generation of code given a specification -- is one of the most fundamental tasks in artificial intelligence (AI) and the dream of many programmer…
Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances
Issa Hanou, Sebastijan Dumančić, Mathijs de Weerdt
We propose a new framework for discovering landmarks that automatically generalize across a domain. These generalized landmarks are learned from a set of solved instances and descr…
Learning Logical Rules using Minimum Message Length
Ruben Sharma, Sebastijan Dumančić, Ross D. King +1
Unifying probabilistic and logical learning is a key challenge in AI. We introduce a Bayesian inductive logic programming approach that learns minimum message length hypotheses fro…
Modelling Program Spaces in Program Synthesis with Constraints
Tilman Hinnerichs, Bart Swinkels, Jaap de Jong +4
A core challenge in program synthesis is taming the large space of possible programs. Since program synthesis is essentially a combinatorial search, the community has sought to lev…
Declarative Design of Neural Predicates in Neuro-Symbolic Systems
Tilman Hinnerichs, Robin Manhaeve, Giuseppe Marra +1
Neuro-symbolic systems (NeSy), which claim to combine the best of both learning and reasoning capabilities of artificial intelligence, are missing a core property of reasoning syst…
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey
Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve +1
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.…
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Luc De Raedt, Sebastijan Dumančić, Robin Manhaeve +1
Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across sev…
Turning 30: New Ideas in Inductive Logic Programming
Andrew Cropper, Sebastijan Dumančić, Stephen H. Muggleton
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work…