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Found 15 papers, 4 papers with code
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning
In this paper, we leverage the implicit knowledge within foundation models to enhance the performance in NeSy tasks, whilst reducing the amount of data labelling and manual engineering.
A Unifying Framework for Learning Argumentation Semantics
In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way.
Hierarchies of Reward Machines
Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the task using high-level events.
Neuro-Symbolic Learning of Answer Set Programs from Raw Data
A promising direction for achieving this goal is Neuro-Symbolic AI, which aims to combine the interpretability of symbolic techniques with the ability of deep learning to learn from raw data.
Efficient lifting of symmetry breaking constraints for complex combinatorial problems
Many industrial applications require finding solutions to challenging combinatorial problems.
FF-NSL: Feed-Forward Neural-Symbolic Learner
To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFNSL), that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data.
Conflict-driven Inductive Logic Programming
The fundamental idea of the approach, called Conflict-driven ILP (CDILP), is to iteratively interleave the search for a hypothesis with the generation of constraints which explain why the current hypothesis does not cover a particular example.
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Using the scoring function of FastLAS, NSL searches for short, interpretable rules that generalise over such noisy examples.
Induction and Exploitation of Subgoal Automata for Reinforcement Learning
In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks.
The ILASP system for Inductive Learning of Answer Set Programs
The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples in the context of some pre-existing background knowledge.
Induction of Subgoal Automata for Reinforcement Learning
In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL).
Inductive Learning of Answer Set Programs from Noisy Examples
In recent years, non-monotonic Inductive Logic Programming has received growing interest.
Iterative Learning of Answer Set Programs from Context Dependent Examples
In ILP, examples must all be explained by a hypothesis together with a given background knowledge.
Learning Weak Constraints in Answer Set Programming
This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP).
