Search Results for author: Krysia Broda

Found 10 papers, 1 papers with code

Hierarchies of Reward Machines

1 code implementation31 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.

Reactive Answer Set Programming

no code implementations22 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.

The ILASP system for Inductive Learning of Answer Set Programs

no code implementations2 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.

Common Sense Reasoning Inductive logic programming

A general framework for scientifically inspired explanations in AI

no code implementations2 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.

Philosophy

Saliency Maps Generation for Automatic Text Summarization

no code implementations12 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.

counterfactual Text Summarization

Iterative Learning of Answer Set Programs from Context Dependent Examples

no code implementations5 Aug 2016 Mark Law, Alessandra Russo, Krysia Broda

In ILP, examples must all be explained by a hypothesis together with a given background knowledge.

Inductive logic programming

Learning Weak Constraints in Answer Set Programming

no code implementations23 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).

Inductive logic programming Scheduling

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