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Fuan Pu

dblp:131/9508 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
argumentation
0.422016
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
0.212016
Counter-Transitivity in Argument Ranking Semantics · AAAI 2016
Knowledge, reasoning and agents › Knowledge representation and reasoning
uncertainty reasoning
0.212014
Computing Preferences Based on Agents' Beliefs · AAAI 2014
Algorithms and data structures › numerical linear algebra › linear system solving
iterative refinement
0.112016
Counter-Transitivity in Argument Ranking Semantics · AAAI 2016
Logic in computer science
semantics
0.112016
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
YearPublicationVenuePosition
2016 Counter-Transitivity in Argument Ranking Semantics
abstract
The 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
AAAI1
2015 Argument Strength Computation Based on Satisfiability Degree and Agents' Beliefs
Jian Luo 0003, Fuan Pu, Guiming Luo
CogSci2
2015 Attacker and Defender Counting Approach for Abstract Argumentation
Fuan Pu, Jian Luo 0003, Yulai Zhang, Guiming Luo
CogSci1
2015 Some Supplementaries to the Counting Semantics for Abstract Argumentation
abstract
Dung'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
ICTAI1
2014 Computing Preferences Based on Agents' Beliefs
abstract
The 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
AAAI2
2014 Argument Ranking with Categoriser Function
Fuan Pu, Jian Luo 0003, Yulai Zhang, Guiming Luo
KSEM1
2013 Social Welfare Semantics for Value-Based Argumentation Framework
Fuan Pu, Jian Luo 0003, Yulai Zhang, Guiming Luo
KSEM1