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Details

  • Name

    Vítor Santos Costa
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2009
006
Publications

2020

Overcoming reinforcement learning limits with inductive logic programming

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

Publication
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

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

Publication
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.

2019

Contrasting logical sequences in multi-relational learning

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

Publication
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.

2019

Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting

Authors
Oliveira, M; Moniz, N; Torgo, L; Costa, VS;

Publication
2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

Abstract

2019

A Three-Valued Semantics for Typed Logic Programming

Authors
Barbosa, J; Florido, M; Costa, VS;

Publication
Proceedings 35th International Conference on Logic Programming (Technical Communications), ICLP 2019 Technical Communications, Las Cruces, NM, USA, September 20-25, 2019.

Abstract
Types in logic programming have focused on conservative approximations of program semantics by regular types, on one hand, and on type systems based on a prescriptive semantics defined for typed programs, on the other. In this paper, we define a new semantics for logic programming, where programs evaluate to true, false, and to a new semantic value called wrong, corresponding to a run-time type error. We then have a type language with a separated semantics of types. Finally, we define a type system for logic programming and prove that it is semantically sound with respect to a semantic relation between programs and types where, if a program has a type, then its semantics is not wrong. Our work follows Milner’s approach for typed functional languages where the semantics of programs is independent from the semantic of types, and the type system is proved to be sound with respect to a relation between both semantics.

Supervised
thesis

2019

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

Author
Christopher David Harrison

Institution
UP-FCUP

2019

Overcoming the current limitations of Reinforcement Learning towards Artificial General Intelligence

Author
Filipe Emanuel dos Santos Marinho da Rocha

Institution
UP-FCUP

2019

Probabilistic Logic-based Models For Gene Regulatory Networks

Author
António José Santos Freitas Gonçalves

Institution
UP-FCUP

2019

NeuralLog: a Neural Logic Language

Author
Victor Augusto Lopes Guimarães

Institution
UP-FCUP

2019

Predictive Analytics for Dependent Data

Author
Mariana Rafaela Figueiredo Ferreira de Oliveira

Institution
UP-FCUP