Lucas Rizzo

Lucas Rizzo
Technological University Dublin - City Campus | TU Dublin · School of Computing

BSc, MS, PhD

About

24
Publications
3,077
Reads
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174
Citations
Additional affiliations
September 2015 - present
Technological University Dublin - City Campus
Position
  • Research Assistant
August 2011 - December 2011
Federal University of Minas Gerais
Position
  • Research Assistant
Education
September 2015 - August 2019
Technological University Dublin - City Campus
Field of study
  • Defeasible reasoning, artificial intelligence
March 2011 - March 2013
February 2007 - December 2010

Publications

Publications (24)
Article
Full-text available
Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new eviden...
Preprint
Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity, with only a limited number of works and researchers performing any sort of comparison among them. A non-monotonic formalism is one t...
Conference Paper
Full-text available
This paper proposes a novel machine learning procedure for genome-wide association study (GWAS), named LightGWAS. It is based on the LightGBM framework, in addition to being a single, resilient, autonomous and scalable solution to address common limitations of GWAS implementations found in the literature. These include reliance on massive manual qu...
Conference Paper
Full-text available
Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of...
Conference Paper
Full-text available
The ultimate goal of Explainable Artificial Intelligence is to build models that possess both high accuracy and degree of explainability. Understanding the inferences of such models can be seen as a process that discloses the relationships between their input and output. These relationships can be represented as a set of inference rules which are u...
Article
Full-text available
Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its...
Article
Full-text available
Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the exa...
Thesis
Full-text available
Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar...
Article
Full-text available
Mental workload (MWL) is an imprecise construct, with distinct definitions and no predominant measurement technique. It can be intuitively seen as the amount of mental activity devoted to a certain task over time. Several approaches have been proposed in the literature for the modelling and assessment of MWL. In this paper, data related to two sets...
Conference Paper
Full-text available
Inferences through knowledge driven approaches have been researched extensively in the field of Artificial Intelligence. Among such approaches argumentation theory has recently shown appealing properties for inference under uncertainty and conflicting evidence. Nonetheless , there is a lack of studies which examine its inferential capacity over oth...
Conference Paper
Full-text available
Defeasible argumentation has advanced as a solid theoretical research discipline for inference under uncertainty. Scholars have predominantly focused on the construction of argument-based models for demonstrating non-monotonic reasoning adopting the notions of arguments and conflicts. However, they have marginally attempted to examine the degree of...
Conference Paper
Full-text available
Computational argumentation has been gaining momentum as a solid theoretical research discipline for inference under uncertainty with incomplete and contradicting knowledge. However, its practical counterpart is underdeveloped, with a lack of studies focused on the investigation of its impact in real-world settings and with real knowledge. In this...
Conference Paper
Full-text available
Research on the discovery, classification and validation of biological markers, or biomarkers, have grown extensively in the last decades. Newfound and correctly validated biomarkers have great potential as prognostic and diagnostic indicators, but present a complex relationship with pertinent endpoints such as survival or other diseases manifestat...
Data
Data on 51 biomarkers collected in a time span of 5 years (2004 - 2009) for 93 patients in an European hospital. For a description of the biomarkers see http://dx.doi.org/10.13140/RG.2.2.20905.49764
Conference Paper
Full-text available
The NASA Task Load Index (NASA − TLX) and the Workload Profile (WP) are likely the most employed instruments for subjective mental workload (MWL) measurement. Numerous areas have made use of these methods for assessing human performance and thusly improving the design of systems and tasks. Unfortunately, MWL is still a vague concept, with different...
Conference Paper
Full-text available
Promising results have indicated Argumentation Theory as a solid research area for implementing defeasible reasoning in practice. However, applications are usually domain dependent, not incorporating all the layers and steps required in an argumentation process, thus limiting their applicability in different areas. This PhD project is focused on th...
Conference Paper
Argumentation theory (AT) is an important area of logic-based artificial intelligence, which provides the basis for computational models of defeasible reasoning. Promising results have indicated AT as a solid research area for implementing defeasible reasoning in practice. However, applications are usually ad-hoc frameworks, not incorporating all t...
Conference Paper
Full-text available
In the last few decades several fields have made use of the construct of human mental workload (MWL) for system and task design as well as for assessing human performance. Despite this interest, MWL remains a nebulous concept with multiple definitions and measurement techniques. State-of-the-art models of MWL are usually ad-hoc, considering differe...
Conference Paper
This work proposes a heuristic approach for the role assignment problem in wireless sensor networks based on the classical problem of vertex coloring in graph theory. Functions or roles define activities to be performed by sensor nodes. In the literature, there are several algorithms for the role assignment in sensor networks and in this work we pr...
Conference Paper
Full-text available
This paper describes a GRASP heuristic to the recently introduced Extended Car Sequencing Problem. A constructive heuristic is developed based on an heuristic for the classical Car Sequencing Problem. The local search procedure uses a very simple neighbourhood easy to be evaluated. Computational results on instances from the CSPLib's library verify...

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