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Alexander Greßler

dblp:202/0328 · DBLP profile ↗
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4ranked-venue papers
2as first author
3since 2021 · last 2024
0000-0001-7470-9352ORCID · corroborated

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

Artificial intelligence and machine learning · 4 · 2 first-author · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Theory of computation · 1 · 1 first-author

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
2 papers
Computational complexity · 58% Automated reasoning and model checking · 42%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
argumentation
0.712023
The complexity landscape of claim-augmented argumentation frameworks · Artif. Intell. 2023
Automated reasoning and model checking › argumentation
abstract argumentation
0.512021
The Complexity Landscape of Claim-Augmented Argumentation Frameworks · AAAI 2021
Computational complexity › complexity classes
polynomial hierarchy
0.512021
The Complexity Landscape of Claim-Augmented Argumentation Frameworks · AAAI 2021
Computational complexity
parameterized complexity
0.212023
The complexity landscape of claim-augmented argumentation frameworks · Artif. Intell. 2023

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

complexity analysis · 0.5
YearPublicationVenuePosition
2024 The GSAF Solver and Verifier
abstract
In this system description we briefly describe the GSAF solver and verifier for SETAFs. The solver is a genuine CDCL-based solver and facilitates the creation of certificates to prove the correctness of negative results if a given instance is deemed to have no extensions. Furthermore, the accompanying verifier can be used to verify such certificates to ensure the correctness thereof.
Alexander Greßler, Wolfgang Dvorák, Stefan Woltran
COMMA1
2023 The complexity landscape of claim-augmented argumentation frameworks
abstract
Claim-augmented argumentation frameworks (CAFs) provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective; they extend Dung AFs by associating a claim to each argument representing its conclusion. This additional layer offers various possibilities to generalize abstract argumentation semantics, i.e. the re-interpretation of arguments in terms of their claims can be performed at different stages in the evaluation of the framework: One approach is to perform the evaluation entirely at argument-level before interpreting arguments by their claims (inherited semantics); alternatively, one can perform certain steps in the process (e.g., maximization) already in terms of the arguments' claims (claim-level semantics). The inherent difference of these approaches not only potentially results in different outcomes but, as we will show in this paper, is also mirrored in terms of computational complexity. To this end, we provide a comprehensive complexity analysis of the four main reasoning problems with respect to claim-level variants of preferred, naive, stable, semi-stable and stage semantics and complete the complexity results of inherited semantics by providing corresponding results for semi-stable and stage semantics. Furthermore, we provide complexity results for these types of frameworks when restricted to specific graph classes and when parameterized by the number of claims within the framework. Moreover, we show that deciding, whether for a given framework the two approaches of a semantics coincide (concurrence) can be surprisingly hard, ranging up to the third level of the polynomial hierarchy.
Wolfgang Dvorák, Alexander Greßler, Anna Rapberger, Stefan Woltran
Artif. Intell.2
2021 The Complexity Landscape of Claim-Augmented Argumentation Frameworks
abstract
Claim-augmented argumentation frameworks (CAFs) provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective; they extend Dung AFs by associating a claim to each argument representing its conclusion. This additional layer offers various possibilities to generalize abstract argumentation semantics as the re-interpretation of arguments in terms of their claims can be performed at different stages in the evaluation of the framework: One approach is to perform the evaluation entirely at argument-level before interpreting arguments by their claims (inherited semantics); alternatively, one can perform certain steps in the process (e.g., maximization) already in terms of the arguments’ claims (claim-level semantics). The inherent difference of these approaches not only potentially results in different outcomes but, as we will show in this paper, is also mirrored in terms of computational complexity. To this end, we provide a comprehensive complexity analysis of the four main reasoning problems with respect to claim-level variants of preferred, naive, stable, semi-stable and stage semantics and complete the complexity results of inherited semantics by providing corresponding results for semi-stable and stage semantics. Moreover, we show that deciding, whether for a given framework the two approaches of a semantics coincide (concurrence) can be surprisingly hard, ranging up to the third level of the polynomial hierarchy.
Wolfgang Dvorák, Alexander Greßler, Anna Rapberger, Stefan Woltran
AAAI2
2017 \mathsf Harvey : A System for Random Testing in ASP
Alexander Greßler, Johannes Oetsch, Hans Tompits
LPNMR1