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Mohamed Maizia

dblp:407/9694 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Computational complexity · 67% Logic in computer science · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Logic in computer science › nonmonotonic reasoning
abduction
0.912025
A Fine-Grained Complexity View on Propositional Abduction - Algorithms and Lower Bounds · IJCAI 2025
Computational complexity › fine-grained complexity
exponential time hypothesis
0.912025
A Fine-Grained Complexity View on Propositional Abduction - Algorithms and Lower Bounds · IJCAI 2025
Computational complexity
fine-grained complexity
0.912025
A Fine-Grained Complexity View on Propositional Abduction - Algorithms and Lower Bounds · IJCAI 2025

Methods — techniques the papers use, named apart from their topics

fine-grained complexity · 0.9
YearPublicationVenuePosition
2025 A Fine-Grained Complexity View on Propositional Abduction - Algorithms and Lower Bounds
abstract
The Boolean satisfiability problem (SAT) is a well-known example of monotonic reasoning, of intense practical interest due to fast solvers, complemented by rigorous fine-grained complexity results. However, for non-monotonic reasoning, e.g., abductive reasoning, comparably little is known outside classic complexity theory. In this paper we take a first step of bridging the gap between monotonic and non-monotonic reasoning by analyzing the complexity of intractable abduction problems under the seemingly overlooked but natural parameter n: the number of variables in the knowledge base. We obtain several positive results for SigmaP2- as well as NP- and coNP-complete fragments, which implies the first example of beating exhaustive search for a SigmaP2-complete problem (to the best of our knowledge). We complement this with lower bounds and for many fragments rule out improvements under the (strong) exponential-time hypothesis.
Victor Lagerkvist, Mohamed Maizia, Johannes Schmidt 0001
IJCAI2
2025 Complexity of Faceted Explanations in Propositional Abduction
abstract
Abstract Abductive reasoning is a popular non-monotonic paradigm that aims to explain observed symptoms and manifestations. It has many applications, such as diagnosis and planning in artificial intelligence and database updates. In propositional abduction, we focus on specifying knowledge by a propositional formula. The computational complexity of tasks in propositional abduction has been systematically characterized – even with detailed classifications for Boolean fragments. Unsurprisingly, the most insightful reasoning problems (counting and enumeration) are computationally highly challenging. Therefore, we consider reasoning between decisions and counting, allowing us to understand explanations better while maintaining favorable complexity. We introduce facets to propositional abductions, which are literals that occur in some explanation (relevant) but not all explanations (dispensable). Reasoning with facets provides a more fine-grained understanding of variability in explanations (heterogeneous). In addition, we consider the distance between two explanations, enabling a better understanding of heterogeneity/homogeneity. We comprehensively analyze facets of propositional abduction in various settings, including an almost complete characterization in Post’s framework.
Johannes Schmidt 0001, Mohamed Maizia, Victor Lagerkvist, Johannes Klaus Fichte
Theory Pract. Log. Program.2