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. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Hao Zhan

dblp:53/7794 · DBLP profile ↗
← Back
3ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 since 2021

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 · 67% Multi-agent systems · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems
multi-agent collaboration
1.012026
Leibniz: Theory-of-Mind Driven Neuro-Symbolic Logical Reasoning via Multi-Agent Collaboration · ACL (1) 2026
Knowledge, reasoning and agents › Knowledge representation and reasoning
neuro-symbolic reasoning
1.012026
Leibniz: Theory-of-Mind Driven Neuro-Symbolic Logical Reasoning via Multi-Agent Collaboration · ACL (1) 2026
Knowledge, reasoning and agents › Knowledge representation and reasoning
theory of mind
1.012026
Leibniz: Theory-of-Mind Driven Neuro-Symbolic Logical Reasoning via Multi-Agent Collaboration · ACL (1) 2026

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

neuro-symbolic reasoning · 1.0multi-agent collaboration · 1.0
YearPublicationVenuePosition
2026 Leibniz: Theory-of-Mind Driven Neuro-Symbolic Logical Reasoning via Multi-Agent Collaboration
abstract
Yue Fan, Hu Zhang, Yunxiao Zhao, Guangjun Zhang, Hao Zhan, Ru Li, Hongye Tan, Yuanlong Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Yunxiao Zhao, Hao Zhan, Hongye Tan
ACL (1)5
2026 AirfoilAgent: Airfoil aerodynamics optimization design via large language model multi-agent collaborations
Hao Zhan, Baigang Mi
Adv. Eng. Informatics2
2020 On the Responsible Subjects of Self-Driving Cars under the SAE System: An Improvement Scheme
abstract
The issue of how to identify the liability of subjects after a traffic accident takes place remains a puzzle regarding the SAE classification system. The SAE system is not good at dealing with the problem of responsibility evaluation; therefore, building a new classification system for self-driving cars from the perspective of the subject's liability is a possible way to solve this problem. This new system divides automated driving into three levels: i) assisted driving based on the will of drivers, ii) automated driving based on the will of the manufacturers, and iii) fully automated driving based on social will. The corresponding responsible subjects for level one, level two, and level three are the drivers, the manufacturers, and society, respectively.
Hao Zhan, Dan Wan
ISCAS1