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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 researcher reaches third place in the Computer Sciences ranking in Portugal

João Gama, researcher at INESC TEC’s Artificial Intelligence and Decision Support Laboratory (LIAAD), reached the third place in the Computer Sciences ranking in Portugal, part of sixth edition of the world ranking of best scientists in the field of Computer Sciences in 2020, promoted by Guide2Research.

15th June 2020

Industrial and Systems Engineering

INESC TEC presents recommendations for supply chains in 2030

During the final event of the NEXT-NET project, held in Brussels on December 17, INESC TEC presented the recommendations for supply chains in 2030. The recommendations are some of the outcomes of the project, which began in October 2017 and whose focus was increasing the production/distribution integration of supply chains, through a multisector and technological initiative aimed at defining research and innovation priorities for the future.

17th January 2020

Computer Science

Apps of YAKE! and Conta-me Histórias are already available on Google Play

The scientific research projects YAKE! and Conta-me Histórias are already available in app format on Google Play.

14th October 2019

Computer Science

INESC TEC organises international workshop on recommendation systems and user modelling

A team of INESC TEC’s researchers was responsible for the organisation of the international workshop ORSUM 2019 – 2nd Workshop on Online Recommender Systems and User Modelling, focused on incremental models for user recommendation and modelling. 

07th October 2019

Computer Science

INESC TEC's researcher was honoured by the Artificial Intelligence Society

On 12 September, Pavel Brazdil, researcher and founder of INESC TEC’s Laboratory of Artificial Intelligence and Decision Support (LIAAD) was honoured by the Portuguese Association for Artificial Intelligence (APPIA) in Guimarães, in the closing session of the Gulbenkian Workshop “New Talents in Artificial Intelligence”. 

25th September 2019

Interest Topics
055

Featured Projects

TRF4p0

TRANSFORMER4.0: DIGITAL REVOLUTION OF POWER TRANSFORMERS

2020-2023

AIDA

Adaptive, Intelligent and Distributed Assurance Platform

2020-2022

PAFML

Investigação e desenvolvimento para aplicação de Machine Learning a dados de pacientes com Paramiloidose

2020-2023

SLSNA

Prestação de Serviços no ambito do projeto SKORR

2020-2021

MINE4HEALTH

Text mining e clinical decision-making

2020-2021

Text2Story

Text2Story: extracting journalistic narratives from text and representing them in a narrative modeling language

2019-2022

T4CDTKC

Training 4 Cotec, Digital Transformation Knowledge Challenge - Elaboração de Programa de Formação “CONHECER E COMPREENDER O DESAFIO DAS TECNOLOGIAS DE TRANSFORMAÇÃO DIGITAL”

2019-2020

PROMESSA

PROject ManagEment intellingent aSSistAnt

2019-2022

RISKSENS

Market Risk Sensitivities

2019-2020

NDTECH

NDtech 4.0 - Smart and Connected - Estudo e Caderno de Encargos

2019-2019

RAMnet

Risk Assessment for Microfinance

2019-2020

Humane_AI

Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us

2019-2020

MLABA

Machine Learn Based Adaptive Business Assurance

2019-2020

Moveo

Prestação de serviços de investigação e desenvolvimento relativos ao sistema MOVEO

2019-2019

FIN-TECH

A FINancial supervision and TECHnology compliance training programme

2019-2020

FailStopper

Deteção precoce de avarias de veículos de transporte público em ambiente operacional

2018-2021

TerraAlva

Terr@Alva

2018-2019

MaLPIS

Aprendizagem Automática para Deteção de Ataques e Identificação de Perfis Segurança na Internet

2018-2021

MDG

Modelling, dynamics and games

2018-2021

NITROLIMIT

Life at the edge: define the boundaries of the nitrogen cycle in the extreme Antarctic environments

2018-2021

RUTE

Randtech Update and Test Environment

2018-2020

SKORR

Advancing the Frontier of Social Media Management Tools

2018-2020

FAST-manufacturing

Flexible And sustainable manufacturing

2018-2021

FLOWTEE

Desenvolvimento de um programa que monitorize automaticamente os níveis de bem-estar (ou felicidade) dos funcionários, a partir de dados disponíveis online

2018-2019

MDIGIREC

Context Recommendation in Digital Marketing

2017-2018

NEXT-NET

Next generation Technologies for networked Europe

2017-2019

RECAP

Research on European Children and Adults born Preterm

2017-2021

SmartFarming

Ferramenta avançada para operacionalização da agricultura de precisão

2016-2018

PANACea

Perfis para Anomalias Consumo

2016-2019

BI4UP2

Business Intelligence (BI) Tool

2016-2017

Dynamics2

Dynamics, optimization and modelling

2016-2019

CORAL-TOOLS

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

2015-2019

NanoStima-RL5

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2019

iMAN

iMAN - Intelligence for advanced Manufacturing systems

2015-2019

NanoStima-RL3

NanoSTIMA - Health data infrastructure

2015-2019

NanoStima-RL4

NanoSTIMA - Health Data Analysis & Decision

2015-2019

SMILES

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

2015-2019

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

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

2020

BRIGHT - Drift-Aware Demand Predictions for Taxi Networks

Authors
Saadallah, A; Moreira Matias, L; Sousa, R; Khiari, J; Jenelius, E; Gama, J;

Publication
IEEE Transactions on Knowledge and Data Engineering

Abstract
Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China) and Stockholm (Sweden), as well as controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task. IEEE

2020

Transfer Learning in urban object classification: Online images to recognize point clouds

Authors
Balado, J; Sousa, R; Diaz Vilarino, L; Arias, P;

Publication
AUTOMATION IN CONSTRUCTION

Abstract
The application of Deep Learning techniques to point clouds for urban object classification is limited by the large number of samples needed. Acquiring and tagging point clouds is more expensive and tedious labour than its image equivalent process. Point cloud online datasets contain few samples for Deep Learning or not always the desired classes This work focuses on minimizing the use of point cloud samples for neural network training in urban object classification. The method proposed is based on the conversion of point clouds to images (pc-images) because it enables: the use of Convolutional Neural Networks, the generation of several samples (images) per object (point clouds) by means of multi-view, and the combination of pc-images with images from online datasets (ImageNet and Google Images). The study is conducted with ten classes of objects extracted from two street point clouds from two different cities. The network selected for the job is the InceptionV3. The training set consists of 5000 online images with a variable percentage (0% to 10%) of pc-images. The validation and testing sets are composed exclusively of pc-images. Although the network trained only with online images reached 47% accuracy, the inclusion of a small percentage of pc-images in the training set improves the classification to 99.5% accuracy with 6% pc-images. The network is also applied at IQmulus & TerraMobilita Contest dataset and it allows the correct classification of elements with few samples.

2020

YAKE! Keyword extraction from single documents using multiple local features

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

Publication
Information Sciences

Abstract
As the amount of generated information grows, reading and summarizing texts of large collections turns into a challenging task. Many documents do not come with descriptive terms, thus requiring humans to generate keywords on-the-fly. The need to automate this kind of task demands the development of keyword extraction systems with the ability to automatically identify keywords within the text. One approach is to resort to machine-learning algorithms. These, however, depend on large annotated text corpora, which are not always available. An alternative solution is to consider an unsupervised approach. In this article, we describe YAKE!, a light-weight unsupervised automatic keyword extraction method which rests on statistical text features extracted from single documents to select the most relevant keywords of a text. Our system does not need to be trained on a particular set of documents, nor does it depend on dictionaries, external corpora, text size, language, or domain. To demonstrate the merits and significance of YAKE!, we compare it against ten state-of-the-art unsupervised approaches and one supervised method. Experimental results carried out on top of twenty datasets show that YAKE! significantly outperforms other unsupervised methods on texts of different sizes, languages, and domains. © 2019 Elsevier Inc.

2020

The 3$$^{\mathrm {rd}}$$ International Workshop on Narrative Extraction from Texts: Text2Story 2020

Authors
Campos, R; Jorge, A­; Jatowt, A; Bhatia, S;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2020

Proceedings of Text2Story - Third Workshop on Narrative Extraction From Texts co-located with 42nd European Conference on Information Retrieval, Text2Story@ECIR 2020, Lisbon, Portugal, April 14th, 2020 [online only]

Authors
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S;

Publication
Text2Story@ECIR

Abstract

Supervised Theses

2019

Simulação Multi-agentes de Leilões de Posições Online: GPS vs. VCG

Author
Maria Margarida Versos Baptista dos Santos

Institution
UP-FEP

2019

Valor preditivo dos marcadores nutricionais na mortalidade e morbilidade dos doentes em hemodiálise

Author
Rosária Maria Afonso Rodrigues de Melo

Institution
UP-FCNAUP

2019

Corporate Social Responsibility Communication in The Logistics Industry: The Home Country Role

Author
Ester Santos da Costa

Institution
UP-FEP

2019

Evolução da densidade mineral óssea em doentes submetidos a cirurgia bariátrica

Author
Beatriz Isabel Guimarães Pereira

Institution
UP-FCNAUP

2019

O Impacto dos Chabots no Comportamento do Consumidor Online

Author
Carolina Pereira Mustur

Institution
UP-FEP

Facts & Figures

2Book Chapters

2019

33Proceedings in indexed conferences

2019

46Papers in indexed journals

2019