EDBT 2026 Demo / reviewers in the wild / expert
Fuan Pu
dblp:131/9508
· DBLP profile ↗
7ranked-venue papers
5as first author
0since 2021 · last 2016
0000-0002-2378-427XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 7 · 5 first-authorDatabases, data management, data science and information retrieval · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 2 · 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.
| Artificial intelligence
2 papers |
Knowledge representation and reasoning · 100% | |
| Theoretical computer science
1 paper |
Logic in computer science · 50% Algorithms and data structures · 50% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
argumentation |
0.4 | 2 | 2016 | Counter-Transitivity in Argument Ranking Semantics · AAAI 2016 Computing Preferences Based on Agents' Beliefs · AAAI 2014 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › argumentation
ranking-based semantics |
0.2 | 1 | 2016 | Counter-Transitivity in Argument Ranking Semantics · AAAI 2016 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
uncertainty reasoning |
0.2 | 1 | 2014 | Computing Preferences Based on Agents' Beliefs · AAAI 2014 |
Algorithms and data structures › numerical linear algebra › linear system solving
iterative refinement |
0.1 | 1 | 2016 | Counter-Transitivity in Argument Ranking Semantics · AAAI 2016 |
Logic in computer science
semantics |
0.1 | 1 | 2016 | Counter-Transitivity in Argument Ranking Semantics · AAAI 2016 |
Methods — techniques the papers use, named apart from their topics
quantity-based ranking · 0.5quality-based ranking · 0.5iterative refinement algorithm · 0.5weighted satisfiability degree · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Counter-Transitivity in Argument Ranking SemanticsabstractThe principle of counter-transitivity plays a vital role in argumentation. It states that an argument is strong when its attackers are weak, and is weak when its attackers are strong. In this work, we develop a formal theory about the argument ranking semantics based on this principle. Three approaches, quantity-based, quality-based and the unity of them, are defined to implement the principle. Then, we show an iterative refinement algorithm for capturing the ranking on arguments based on the recursive nature of the principle. Fuan Pu, Jian Luo 0003, Guiming Luo |
AAAI | 1 |
| 2015 | Argument Strength Computation Based on Satisfiability Degree and Agents' Beliefs
Jian Luo 0003, Fuan Pu, Guiming Luo |
CogSci | 2 |
| 2015 | Attacker and Defender Counting Approach for Abstract Argumentation
Fuan Pu, Jian Luo 0003, Yulai Zhang, Guiming Luo |
CogSci | 1 |
| 2015 | Some Supplementaries to the Counting Semantics for Abstract ArgumentationabstractDung's abstract argumentation framework consists of a set of interacting arguments and a series of semantics for evaluating them. Those semantics partition the powerset of the set of arguments into two classes: extensions and non-extensions. In order to reason with a specific semantics, one needs to take a credulous or skeptical approach, i.e. an argument is eventually accepted, if it is accepted in one or all extensions, respectively. In our previous work [1], we have proposed a novel semantics, called counting semantics, which allows for a more fine-grained assessment to arguments by counting the number of their respective attackers and defenders based on argument graph and argument game. In this paper, we continue our previous work by presenting some supplementaries about how to choose the damaging factor for the counting semantics, and what relationships with some existing approaches, such as Dung's classical semantics, generic gradual valuations. Lastly, an axiomatic perspective on the ranking semantics induced by our counting semantics are presented. Fuan Pu, Jian Luo 0003, Guiming Luo |
ICTAI | 1 |
| 2014 | Computing Preferences Based on Agents' BeliefsabstractThe knowledgebase uncertainty and the argument preferences are considered in this paper. The uncertainty is captured by weighted satisfiability degree, while a preference relation over arguments is derived by the beliefs of an agent. Jian Luo 0003, Fuan Pu, Yulai Zhang, Guiming Luo |
AAAI | 2 |
| 2014 | Argument Ranking with Categoriser Function
Fuan Pu, Jian Luo 0003, Yulai Zhang, Guiming Luo |
KSEM | 1 |
| 2013 | Social Welfare Semantics for Value-Based Argumentation Framework
Fuan Pu, Jian Luo 0003, Yulai Zhang, Guiming Luo |
KSEM | 1 |