Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Maonian Wu

dblp:13/283 · DBLP profile ↗
← Back
9ranked-venue papers
3as first author
4since 2021 · last 2026
0000-0003-1014-8642ORCID · corroborated

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

Artificial intelligence and machine learning · 5 · 3 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 4 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorTheory of computation · 1

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
1 paper
Knowledge representation and reasoning · 100%
Theoretical computer science
1 paper
Logic in computer science · 50% Automated reasoning and model checking · 50%

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
belief change
0.112011
Language Splitting and Relevance-Based Belief Change in Horn Logic · AAAI 2011
Knowledge, reasoning and agents › Knowledge representation and reasoning
belief revision
0.112011
Language Splitting and Relevance-Based Belief Change in Horn Logic · AAAI 2011
Logic in computer science › logic programming
horn clauses
0.112011
Language Splitting and Relevance-Based Belief Change in Horn Logic · AAAI 2011
Automated reasoning and model checking › automated reasoning
interpolation
0.112011
Language Splitting and Relevance-Based Belief Change in Horn Logic · AAAI 2011

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

relevance criterion · 0.2partial meet contraction · 0.2
YearPublicationVenuePosition
2026 IEM-DETR: Integrating and Enhancing Multi-Level Features for Limited-Data Object Detection
Yehui Jin, Bo Zheng 0004, Maonian Wu, Shaojun Zhu
ICIC (9)3
2026 PR-TDMPC: Preference-Based Reinforcement Learning for Humanoid Control
Tianxiao Yao, Maonian Wu, Guoqing Lu, Shaojun Zhu, Bo Zheng 0004
ICIC (2)2
2024 Neuro-symbolic recommendation model based on logic query
Maonian Wu, Bang Chen, Shaojun Zhu, Bo Zheng 0004
Knowl. Based Syst.1
2023 Neural-Symbolic Recommendation with Graph-Enhanced Information
Bang Chen, Maonian Wu, Shaojun Zhu
ICONIP (3)3
2015 A sequential model of bargaining in logic programming
Dongmo Zhang, Maonian Wu
Frontiers Comput. Sci.3
2013 A Sequential Model for Reasoning about Bargaining in Logic Programs
Dongmo Zhang, Maonian Wu
LPNMR3
2011 Language Splitting and Relevance-Based Belief Change in Horn Logic
abstract
This paper presents a framework for relevance-based belief change in propositional Horn logic. We firstly establish a parallel interpolation theorem for Horn logic and show that Parikh's Finest Splitting Theorem holds with Horn formulae. By reformulating Parikh's relevance criterion in the setting of Horn belief change, we construct a relevance-based partial meet Horn contraction operator and provide a representation theorem for the operator. Interestingly, we find that this contraction operator can be fully characterised by Delgrande and Wassermann's postulates for partial meet Horn contraction as well as Parikh's relevance postulate without requiring any change on the postulates, which is qualitatively different from the case in classical propositional logic.
Maonian Wu, Dongmo Zhang
AAAI1
2010 Algorithms and application in decision-making for the finest splitting of a set of formulae
Maonian Wu
Knowl. Based Syst.1
2009 A Logic-Program-Based Negotiation Mechanism
Maonian Wu
J. Comput. Sci. Technol.3