6 papers
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…
Learning Logical Rules using Minimum Message Length
Ruben Sharma, Sebastijan DumanÄiÄ, 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…
Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances
Issa Hanou, Sebastijan DumanÄiÄ, 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…
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…