21 citations · 66 across the 14 of their papers we have counts for
3 papers · 1 filter
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Daniel Cunnington, Alessandra Russo, Mark Law +2
Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems re…
Induction and Exploitation of Subgoal Automata for Reinforcement Learning
Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2
In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induct…
The ILASP system for Inductive Learning of Answer Set Programs
Mark Law, Alessandra Russo, Krysia Broda
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. Until recently, most r…