What is he best known for?
The fields of study he is best known for:
- Programming language
- Artificial intelligence
- First-order logic
His primary scientific interests are in Artificial intelligence, Logic programming, Programming language, Theoretical computer science and Non-monotonic logic.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Semantics and Calculus.
His work carried out in the field of Logic programming brings together such families of science as Autoepistemic logic and Knowledge representation and reasoning.
His study in Theoretical computer science focuses on Predicate logic in particular.
His research in Stable model semantics intersects with topics in Well-founded semantics and Axiomatic semantics.
His studies deal with areas such as Multimodal logic, Description logic, Higher-order logic, Logical programming and Semantics as well as Well-founded semantics.
His most cited work include:
- Abduction in Logic Programming (145 citations)
- Well-founded and stable semantics of logic programs with aggregates (136 citations)
- The Second Answer Set Programming Competition (119 citations)
What are the main themes of his work throughout his whole career to date?
Marc Denecker mainly focuses on Theoretical computer science, Logic programming, Programming language, Knowledge representation and reasoning and Artificial intelligence.
He works mostly in the field of Theoretical computer science, limiting it down to topics relating to Inference and, in certain cases, Knowledge base, as a part of the same area of interest.
His Logic programming research includes themes of Autoepistemic logic, Semantics, Classical logic and Description logic.
The Autoepistemic logic study combines topics in areas such as Non-monotonic logic and Higher-order logic.
His work deals with themes such as Algorithm and Extension, which intersect with Knowledge representation and reasoning.
His Artificial intelligence research incorporates elements of Machine learning, Representation and Natural language processing.
He most often published in these fields:
- Theoretical computer science (31.84%)
- Logic programming (29.21%)
- Programming language (27.72%)
What were the highlights of his more recent work (between 2014-2021)?
- Semantics (12.36%)
- Programming language (27.72%)
- Theoretical computer science (31.84%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Semantics, Programming language, Theoretical computer science, Logic programming and Knowledge representation and reasoning.
The various areas that he examines in his Programming language study include Basis and Principle of compositionality.
His Theoretical computer science research incorporates themes from Context, Human–computer interaction, Knowledge base, Graph and Solver.
Marc Denecker interconnects Domain, Fixed point, Formal language, Type and Autoepistemic logic in the investigation of issues within Logic programming.
His work in Autoepistemic logic addresses issues such as Well-founded semantics, which are connected to fields such as Calculus.
His Knowledge representation and reasoning study is concerned with the field of Artificial intelligence as a whole.
Between 2014 and 2021, his most popular works were:
- Predicate logic as a modeling language: modeling and solving some machine learning and data mining problems with IDP3 (34 citations)
- Improved Static Symmetry Breaking for SAT (28 citations)
- Lazy model expansion: interleaving grounding with search (26 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Artificial intelligence
- Algebra
His scientific interests lie mostly in Knowledge representation and reasoning, Programming language, Logic programming, Modeling language and Theoretical computer science.
His Knowledge representation and reasoning research incorporates elements of Mathematical logic, Declarative programming and Calculus.
The Programming language study combines topics in areas such as Basis, Algorithm and TRACE.
His Logic programming study contributes to a more complete understanding of Artificial intelligence.
His studies in Artificial intelligence integrate themes in fields like Well-founded semantics and Argumentation theory.
He studies Answer set programming, a branch of Theoretical computer science.
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