Artificial Intelligence
Mathematical and computational foundations for intelligent systems, automated reasoning, planning, and algorithmic decision processes.
Daniel Kikuti, PhD
Editorial Board
Daniel Kikuti, PhD
Editorial Board Member — Artificial Intelligence, Optimization, and Decision Sciences
Journal of Digital Health and Advanced Biomaterials (JDHAB)
State University of Maringá (UEM), Maringá, Paraná, Brazil
Artificial IntelligenceAlgorithmsOptimizationDecision-Making under UncertaintyComputational Models
Artificial Intelligence Automated Reasoning · Computational Models · Intelligent Systems
Decision Sciences Uncertainty · Imprecise Probabilities · Partially Ordered Preferences
Optimization Algorithms · Planning · Scheduling · Operational Research
Professional Profile
Daniel Kikuti is a computer scientist, researcher, and university professor whose academic work spans artificial intelligence, algorithms, optimization, decision sciences, operational research, and computational modeling. He is currently a faculty member in the Department of Informatics at the State University of Maringá (UEM), Brazil.
His research has a strong theoretical and computational foundation in decision-making under uncertainty, particularly in situations in which probability estimates or preferences cannot be represented by a single complete ordering. His work investigates mathematical and algorithmic approaches capable of representing and solving decision problems involving imprecise probabilities, partially ordered preferences, decision trees, and influence diagrams.
His broader academic activities also include algorithm development, combinatorial optimization, operational research, graph-based problems, automated planning, scheduling problems, computational thinking, and computing education.
Editorial Role at JDHAB
As an Editorial Board Member of JDHAB, Daniel Kikuti contributes expertise in artificial intelligence, computational methods, algorithms, optimization, and decision sciences.
His background supports the scientific assessment of manuscripts involving computational models, algorithmic methods, automated decision systems, optimization procedures, uncertainty modeling, and artificial intelligence. His role is particularly relevant to the journal's commitment to distinguishing technically sound computational methodology from unsupported or insufficiently validated claims involving artificial intelligence.
Research and Editorial Focus
Decision-Making under Uncertainty
Decision models involving incomplete information, imprecise probabilities, partially ordered preferences, and alternative criteria for strategy selection.
Algorithms and Optimization
Development and analysis of algorithms for optimization, operational research, combinatorial problems, graphs, planning, and computational problem solving.
Planning and Scheduling
Computational approaches to automated planning, scheduling problems, allocation, and algorithmic optimization.
Computational Systems
Research involving computer systems, heterogeneous computing environments, monitoring architectures, and practical computational infrastructure.
Computing Education
Algorithmic problem solving, computational thinking, programming education, and educational initiatives connecting computer science with academic and school communities.
Selected Scientific Contributions
Artificial Intelligence · Decision Sciences
Sequential decision making with partially ordered preferences
Research addressing sequential decision-making when available information does not permit a complete ranking of alternative strategies. The study investigates decision criteria including Γ-Maximin, Γ-Maximax, Γ-Maximix, Interval Dominance, Maximality, and E-admissibility, using decision trees and influence diagrams together with linear and multilinear programming approaches.
Decision Trees · Imprecise Probabilities
Partially Ordered Preferences in Decision Trees: Computing Strategies with Imprecision in Probabilities
Early work developing computational strategies for decision trees in which uncertainty is represented by sets or intervals of probability values. The study evaluates several decision criteria and introduces algorithms for identifying admissible strategies under partially ordered preferences.
Computational Systems
Using Portable Monitoring for Heterogeneous Cluster on Windows and Linux Operating Systems
Research addressing portable monitoring in heterogeneous computational environments involving different operating systems. The work reflects an additional dimension of his research trajectory in practical computing infrastructure and computer systems.
Current and Continuing Research Areas
His continuing research activities include automated planning and algorithms for scheduling problems, together with mathematical and computational approaches to optimization and decision-making.
These research directions extend his earlier investigations of incomplete preferences and uncertain decision environments toward broader problems in artificial intelligence, operational research, and algorithmic optimization.
Computational Thinking and Computing Education
In addition to theoretical and algorithmic research, Daniel Kikuti participates in educational and extension activities aimed at strengthening computational thinking, programming, and algorithmic problem-solving skills.
Computing Education · Extension
Desmistificando o Pensamento Computacional: relato de um workshop para Professores da Educação Básica
Educational initiative addressing computational thinking through structured activities for basic education teachers, contributing to the translation of computer science concepts into broader educational environments.
His academic activities also include training and mentoring related to algorithms, programming competitions, and problem-solving methodologies, reinforcing the connection between theoretical computer science and practical computational education.
Scientific Perspective within the Journal
Daniel Kikuti's contribution to JDHAB expands the journal's interdisciplinary expertise beyond biomedical application areas by providing a foundation in computer science and formal decision theory.
His expertise is particularly relevant when evaluating whether computational or artificial-intelligence methods are appropriately defined, whether algorithmic procedures are reproducible, whether uncertainty has been adequately represented, and whether the conclusions produced by computational systems are supported by the methods and evidence presented.
