21 citations · 60 across the 9 of their papers we have counts for
6 papers · 1 filter
Reactive Answer Set Programming
Krysia Broda, Fariba Sadri, Stephen Butler
Logic Production System (LPS) is a logic-based framework for modelling reactive behaviour. Based on abductive logic programming, it combines reactive rules with logic programs, a d…
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…
A general framework for scientifically inspired explanations in AI
David Tuckey, Alessandra Russo, Krysia Broda
Explainability in AI is gaining attention in the computer science community in response to the increasing success of deep learning and the important need of justifying how such sys…
Inductive Learning of Answer Set Programs from Noisy Examples
Mark Law, Alessandra Russo, Krysia Broda
In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for lear…
Learning Weak Constraints in Answer Set Programming
Mark Law, Alessandra Russo, Krysia Broda
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…