Key research themes
1. How does fuzzy logic formalize and manage uncertainty and imprecision in real-world and engineering systems?
This theme explores the foundational role of fuzzy logic as a precise mathematical framework for representing vagueness and approximate reasoning in diverse application domains including engineering processes, biomedical image retrieval, and material manufacturing. Research focuses on modeling uncertainty, optimizing system behavior under complex conditions, and enhancing decision-making where classical binary logic proves inadequate.
2. What are the logical and semantic frameworks developed to handle paradoxes, hyperintensionality, and completeness in non-classical logics?
This research area investigates novel logics addressing foundational issues in semantics and paradox resolution, including hyperintensional contexts that distinguish logically equivalent but intensionally distinct formulae, semantic paradoxes like the Liar, and the notion of completeness in truthmaker semantics. These frameworks extend classical logic by embracing partiality, inconsistency, and enriched semantic notions to refine entailment, conditionality, and truth conditions beyond extensional equivalence.
3. How can alternative logical frameworks expand or generalize classical logic to incorporate intuitionistic, fuzzy, and deontic reasoning?
This theme examines extensions and generalizations of classical bivalent logic by integrating fuzzy, intuitionistic, and deontic principles to better capture graded truth, normative reasoning, and term-based inference. Research focuses on identifying correspondences between classical logic and its fuzzy extensions, axiomatizing term logic as a natural language-based reasoning system, and developing defeasible deontic logics that address contrary-to-duty obligations and pragmatic paradoxes with computationally feasible semantics.