1 code implementation • 31 May 2022 • Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo
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.
no code implementations • 22 Sep 2021 • Krysia Broda, Fariba Sadri, Stephen Butler
Based on abductive logic programming, it combines reactive rules with logic programs, a database and a causal theory that specifies transitions between the states of the database.
no code implementations • 8 Sep 2020 • Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo
In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks.
no code implementations • 2 May 2020 • 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.
no code implementations • 2 Mar 2020 • 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 systems make predictions in life-critical applications.
no code implementations • 29 Nov 2019 • Daniel Furelos-Blanco, Mark Law, Alessandra Russo, Krysia Broda, Anders Jonsson
In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL).
no code implementations • 12 Jul 2019 • David Tuckey, Krysia Broda, Alessandra Russo
Saliency map generation techniques are at the forefront of explainable AI literature for a broad range of machine learning applications.
no code implementations • 25 Aug 2018 • Mark Law, Alessandra Russo, Krysia Broda
In recent years, non-monotonic Inductive Logic Programming has received growing interest.
no code implementations • 5 Aug 2016 • Mark Law, Alessandra Russo, Krysia Broda
In ILP, examples must all be explained by a hypothesis together with a given background knowledge.
no code implementations • 23 Jul 2015 • 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).