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Detalhes

Detalhes

  • Nome

    Vítor Santos Costa
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2009
006
Publicações

2020

Overcoming reinforcement learning limits with inductive logic programming

Autores
Rocha, FM; Costa, VS; Reis, LP;

Publicação
Advances in Intelligent Systems and Computing

Abstract
This work presents some approaches to overcome current Reinforcement Learning limits. We implement a simple virtual environment and some state-of-the-art Reinforcement Learning algorithms for testing and producing a baseline for comparison. Then we implement a Relational Reinforcement Learning algorithm that shows superior performance to the baseline but requires introducing human knowledge. We also propose that Model-based Reinforcement Learning can help us overcome some of the barriers. For better World models, we explore Inductive Logic Programming methods, such as First-Order Inductive Learner, and develop an improved version of it, more adequate to Reinforcement Learning environments. Finally we develop a novel Neural Network architecture, the Inductive Logic Neural Network, to fill the gaps of the previous implementations, that shows great promise. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

2020

From reinforcement learning towards artificial general intelligence

Autores
Rocha, FM; Costa, VS; Reis, LP;

Publicação
Advances in Intelligent Systems and Computing

Abstract
The present work surveys research that integrates successfully a number of complementary fields in Artificial Intelligence. Starting from integrations in Reinforcement Learning: Deep Reinforcement Learning and Relational Reinforcement Learning, we then present Neural-Symbolic Learning and Reasoning since it is applied to Deep Reinforcement Learning. Finally, we present integrations in Deep Reinforcement Learning, such as, Relational Deep Reinforcement Learning. We propose that this road is breaking through barriers in Reinforcement Learning and making us closer to Artificial General Intelligence, and we share views about the current challenges to get us further towards this goal. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

2020

Diabetes Management Guidance by a Logical Unit Supported by Data-Mining in a Mobile Application

Autores
Machado, D; Costa, VS; Dutra, I; Brandao, P;

Publicação
XV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING - MEDICON 2019

Abstract
Diabetes type I is a chronic disease that requires strict supervision. MyDiabetes is a utility application for diabetic users. This application served as basis to develop a logical unit, composed of logical rules, translated from medical protocols and guidelines, to advise the user. The data in the application is a source of knowledge about the user's health state and diabetes intrinsic characteristics. An existing concern is the weak user adherence and consequential data absence. The implemented solutions were gamification and an interface rework. As later confirmed through a survey, users feel captivated by appealing interfaces, achievements and medals. In a near future, we will resume our work with the S. Joao's hospital, with a new trial and volunteers. User testing will be used to validate the gamification techniques.

2019

Evaluation Procedures for Forecasting with Spatio-Temporal Data

Autores
Oliveira, M; Torgo, L; Costa, VS;

Publicação
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I

Abstract

2019

Contrasting logical sequences in multi-relational learning

Autores
Ferreira, CA; Gama, J; Costa, VS;

Publicação
Progress in Artificial Intelligence

Abstract
In this paper, we present the BeamSouL sequence miner that finds sequences of logical atoms. This algorithm uses a levelwise hybrid search strategy to find a subset of contrasting logical sequences available in a SeqLog database. The hybrid search strategy runs an exhaustive search, in the first phase, followed by a beam search strategy. In the beam search phase, the algorithm uses the confidence metric to select the top k sequential patterns that will be specialized in the next level. Moreover, we develop a first-order logic classification framework that uses predicate invention technique to include the BeamSouL findings in the learning process. We evaluate the performance of our proposals using four multi-relational databases. The results are promising, and the BeamSouL algorithm can be more than one order of magnitude faster than the baseline and can find long and highly discriminative contrasting sequences. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Teses
supervisionadas

2020

NeuralLog: a Neural Logic Language

Autor
Victor Augusto Lopes Guimarães

Instituição
UP-FCUP

2020

Predictive Analytics for Dependent Data

Autor
Mariana Rafaela Figueiredo Ferreira de Oliveira

Instituição
UP-FCUP

2020

Advising Diabetes’ self-management supported by user data in a mobile platform

Autor
Diogo Roberto de Melo e Diogo Machado

Instituição
UP-FCUP

2020

Probabilistic Logic-based Models For Gene Regulatory Networks

Autor
António José Santos Freitas Gonçalves

Instituição
UP-FCUP

2020

Exascale computing with custom Linear Mixed Model kernels and GPU accelerators for Genomic Wide Association Studies and personalized medicine

Autor
Christopher David Harrison

Instituição
UP-FCUP