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Artificial Intelligence and Decision Support

At LIAAD, we work on the very strategic area of Data Science, which has an increasing interest worldwide and is critical to all areas of human activity. The huge amounts of collected data (Big Data) and the ubiquity of devices with sensors and/or processing power offer opportunities and challenges to scientists and engineers. Moreover, the demand for complex models for objective decision support is spreading in business, health, science, e-government and e-learning, which encourages us to invest in different approaches to modelling.

Our overall strategy is to take advantage of the data flood and diversification, and to invest in research lines that will help reduce the gap between collected and useful data, while offering diverse modelling solutions.

At LIAAD, our fundamental scientific principals are machine learning, statistics, optimisation and mathematics.

Latest News
Computer Science

INESC TEC team wins another Best Paper Award

The paper entitled “A Text Feature Based Automatic Keyword Extraction Method for Single Documents” by Ricardo Campos, Vitor Mangaravite, Arian Pasquali and Alípio M. Jorge, researchers from INESC TEC’s Artificial Intelligence and Decision Support Laboratory (LIAAD) and by Célia Nunes from the University of Beira Interior (UBI), and by Adam Jatowt from Kyoto University, won the ECIR 2018 Best Short Paper Award, promoted by the 40th European Conference on Information Retrieval.

12th April 2018

FOOTURE 4.0 is INESCTEC’s roadmap of innovation in the footwear industry

On 9 March and under the “Roadmap of Innovation”, the Portuguese Prime Minister, António Costa, participated in the fourth roadmap, which was dedicated to the footwear industry and which was attended by the Procalçado group and by the CTCP (Portuguese Technological Footwear Centre).

10th April 2018

Computer Science

Two INESC TEC’s researchers were nominated for the Cor Baayen Young Researcher Award of the ERCIM

João Tiago Paulo and Hadi Tork, INESC TEC’s collaborators of the High Assurance Software Laboratory (HASLab) and Artificial Intelligence and Decision Support Laboratory (LIAAD), respectively, were two of the fifteen finalists present at the ERCIM 2017 Cor Baayen Young Researcher Award.

29th March 2018

Computer Science

INESC TEC organises a workshop in Japan

Ricardo Campos, researcher at INESC TEC’s Artificial Intelligence and Decision Support Laboratory (LIAAD) was one of the promoters of the first workshop entitled «User Interfaces for Spatial and Temporal Data Analysis - UISTDA2018» that took place in Japan on 11 march.

26th March 2018

Computer Science

INESC TEC researcher publishes book through Springer-Verlag

Alberto Adrego Pinto, researcher at the Laboratory of Artificial Intelligence and Decision Support (LIAAD) of INESC TEC and professor at the Faculty of Sciences of the University of Porto, has recently published the second volume of the series "Modeling, Dynamics, Optimization and Bioeconomics", together with David Zilberman, of the University of California, Berkeley, USA.

17th January 2018

Interest Topics
030

Featured Projects

PERSONA

PERSONALIZAÇÃO E GESTÃO DE INFORMAÇÃO BASEADA EM DADOS CLIENTE

2017-2019

RECAP

Research on European Children and Adults born Preterm

2017-2021

SmartFarming-1

Ferramenta avançada para operacionalização da agricultura de precisão

2016-2018

PANACea

Perfis para Anomalias Consumo

2016-2018

BI4UP2

Business Intelligence (BI) Tool

2016-2017

Dynamics2

Dynamics, optimization and modelling

2016-2019

CORAL-TOOLS-1

CORAL – Sustainable Ocean Exploitation: Tools and Sensors

2016-2018

MarineEye

MarinEye - A prototype for multitrophic oceanic monitoring

2015-2017

FOUREYES

TEC4Growth - RL FourEyes - Intelligence, Interaction, Immersion and Innovation for media industries

2015-2018

NanoStima-RL5

NanoSTIMA – Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2018

iMAN

iMAN - Intelligence for advanced Manufacturing systems

2015-2018

NanoStima-RL3

NanoSTIMA – Health data infrastructure

2015-2018

NanoStima-RL4

NanoSTIMA – Health Data Analysis & Decision

2015-2018

SMILES

SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2018

FOTOCATGRAF

Graphene-based semiconductor photocatalysis for a safe and sustainable water supply: an advanced technology for emerging pollutants removal

2015-2018

SEA

SEA-Sistema de ensino autoadaptativo

2015-2015

MAESTRA

Learning from Massive, Incompletely annotated, and Structured Data

2014-2017

BI4UP

Business Intelligence (BI) Tool

2014-2014

SIBILA

Towards Smart Interacting Blocks that Improve Learned Advice

2013-2015

SmartManufacturing

Smart Manufacturing and Logistics

2013-2015

SmartGrids

Smart Grids

2013-2015

Dynamics

Dynamics and Applications

2012-2015

e-Policy

Engineering for the Policy-making Life Cycle (ePolicy)

2011-2014

SIMULESP

Expert system to support network operator on real time decision

2011-2015

CRN

Trust-aware Automatic E-Contract Negotiation in Agent-based Adaptive Normative Environments

2010-2013

KDUS

Knowledge Discovery from Ubiquitous Data Streams

2010-2013

Palco3.0

Intelligent Web system to support the management of a social network on music

2008-2011

Argos-1

Wind power forecasting system

2008-2012

MOREWAQ

Monitoring and Forecasting of Water Quality Parameters

2008-2011

ORANKI

Resource-bounded outlier detection

2008-2011

Team
Publications

LIAAD Publications

View all Publications

2018

State estimation pre-filtering with overlapping tiling of autoencoders

Authors
Saran, MAM; Miranda, V;

Publication
Electric Power Systems Research

Abstract
This paper presents a new concept for an approach to deal with measurements contaminated with gross errors, prior to power system state estimation. Instead of a simple filtering operation, the new procedure develops a screen-and-repair process, going through the phases of detection, identification and correction of multiple gross errors. The method is based on the definition of the coverage of the measurement set by a tiling scheme of 3-overlapping autoencoders, trained with denoising techniques and correntropy, that produce an ensemble-like set of three proposals for each measurement. These proposals are then subject to a process of fusion to produce a vector of proposed/corrected measurements, and two fusion methods are compared, with advantage to the Parzen Windows method. The original measurement vector can then be recognized as clean or diagnosed with possible gross errors, together with corrections that remove these errors. The repaired vectors can then serve as input to classical state estimation procedures, as only a small noise remains. A test case illustrates the effectiveness of the technique, which could deal with four simultaneous gross errors and achieve a result close to full recognition and correction of the errors. © 2017 Elsevier B.V.

2018

Multi-label classification from high-speed data streams with adaptive model rules and random rules

Authors
Sousa, R; Gama, J;

Publication
Progress in Artificial Intelligence

Abstract

2018

Preference rules for label ranking: Mining patterns in multi-target relations

Authors
de Sa, CR; Azevedo, P; Soares, C; Jorge, AM; Knobbe, A;

Publication
INFORMATION FUSION

Abstract
In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

2018

Using metalearning for parameter tuning in neural networks

Authors
Félix, C; Soares, C; Jorge, A; Ferreira, H;

Publication
Lecture Notes in Computational Vision and Biomechanics

Abstract
Neural networks have been applied as a machine learning tool in many different areas. Recently, they have gained increased attention with what is now called deep learning. Neural networks algorithms have several parameters that need to be tuned in order to maximize performance. The definition of these parameters can be a difficult, extensive and time consuming task, even for expert users. One approach that has been successfully used for algorithm and parameter selection is metalearning. Metalearning consists in using machine learning algorithm on (meta)data from machine learning experiments to map the characteristics of the data with the performance of the algorithms. In this paper we study how a metalearning approach can be used to obtain a good set of parameters to learn a neural network for a given new dataset. Our results indicate that with metalearning we can successfully learn classifiers from past learning tasks that are able to define appropriate parameters. © 2018, Springer International Publishing AG.

2018

A Text Feature Based Automatic Keyword Extraction Method for Single Documents

Authors
Campos, R; Mangaravite, V; Pasquali, A; Jorge, AM; Nunes, C; Jatowt, A;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

Supervised Theses

2016

A Performance dos Agentes de Seguros e a Diversidade de Parcerias: Modelos de Equações Estruturais e Análise de Redes Sociais

Author
Mónica Monteiro Chibante Sequeira

Institution
UP-FEP

2016

A Aceitação do Aplicativo Móvel “Cartão Continente” por Parte dos Clientes Sénior

Author
Paula Sofia da Cruz Ribeiro

Institution
UP-FEP

2016

Impacto de uma Ação de Merchandising em Farmácias

Author
Teresa Filipa Ferreira da Cunha Cruz e Sousa

Institution
UP-FEP

2016

Impacto dos Incidentes Críticos Negativos na Propensão para a Repetição e Divulgação de um Destino de Férias

Author
Teresa Maria Moura Quintas

Institution
UP-FEP

2016

Perceção da responsabilidade social nos estudantes de mestrados em economia e gestão

Author
Tânia Margarida Soares Castro Silva

Institution
UP-FEP

Facts & Figures

329Turnover (k€)

2016

2Concluded PhD theses

2016

29Senior Researchers

2016