Demo Data from EDBT 2026 / reviewers in the wild / expert
Maonian Wu
dblp:13/283
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
belief change |
0.1 | 1 | 2011 | Language Splitting and Relevance-Based Belief Change in Horn Logic · AAAI 2011 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
belief revision |
0.1 | 1 | 2011 | Language Splitting and Relevance-Based Belief Change in Horn Logic · AAAI 2011 |
Logic in computer science › logic programming
horn clauses |
0.1 | 1 | 2011 | Language Splitting and Relevance-Based Belief Change in Horn Logic · AAAI 2011 |
Automated reasoning and model checking › automated reasoning
interpolation |
0.1 | 1 | 2011 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 |
LPNMR | 3 |
| 2011 | Language Splitting and Relevance-Based Belief Change in Horn LogicabstractThis 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 |
AAAI | 1 |
| 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 |