Dr. Daxin Liu (刘达欣)

E-mail: daxin[dot]liu[at]nju.edu.cn

Office Hour:  No fixed, by appointment

Currently, I am an assistant professor at Nanjing University. Previously, I was a postdoctoral researcher (2023-2024) at the University of Edinburgh (supervised by Dr. Vaishak Belle) in AIAI. I was a doctoral researcher (2018-2022) at the RWTH-Aachen university (2018-2022) supervised by Prof. Gerhard Lakemeyer. During that time, I was affiliated with the Knowledge-Based Systems Group and the UnRAVeL Research Training Group. I finished my Master in Computer Science (2018) in Nanjing University and Bachelor in Software Engineering (2015) in Central South University.

Research Interest

Briefly, Artificial Intelligence, Knowledge Representation, Program Verification
Specifically, I am interested in formalisms of concepts such as knowledge and beliefs, actions and causality, probability, and uncertainty, etc., and reasoning mechanisms with regard to these notions. This covers research areas in AI such as reasoning about knowledge and beliefs, reasoning about actions and causes, and planning and verification for autonomous agents. This also means that most research is theoretical and based on logic and/or formal methods. Please also refer to the latest publication for my current focus.

Message for propsective students

I am looking for prospective students or research collaberators who are interested in the above research area. If you are interested, why no dropping me an email.

Community Service

Served as PC members for conference: AAAI2023, ECAI2023, AAAI2024, ECAI2024, KR2024, AAAI2025, AAMAS2025
Served as reviewers for journal: Tran. on Computational Logic

Publications

          Vaishak Belle, Daxin Liu
          Reasoning about Neural Network Perception in the Situation Calculus. (Extended Abstract)
          IJCLR, 2024.

          Daxin Liu, Jens Claβen
          First-Order Progression beyond Local-Effect and Normal Actions
          In proceeding of the 33rd International Joint Conference on Artificial Intelligence (IJCAI), 2024.

          Daxin Liu, Vaishak Belle.
          Progression with Probabilities in the Situation Calculus: Representation and Succinctness.
          In proceeding of the 23nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024.

          Daxin Liu, Qinfei Huang, Vaishak Belle, Gerhard Lakemeyer.
          Verifying Belief-based Programs via Symbolic Dynamic Programming.
          In proceeding of 26th European Conference on Artificial Intelligence (ECAI) 2023

          Daxin Liu, Qihui Feng, Vaishak Belle, Gerhard Lakemeyer.
          Concerning Measures in a First-order Logic with Actions and Meta-beliefs.
          In proceeding of 20th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2023

          Daxin Liu, Qihui Feng.
          On the Progression of Belief.
          Artificial Intelligence Journal (AIJ), 2023

          Qihui Feng, Daxin Liu, Vaishak Belle, Gerhard Lakemeyer.
          A Logic of Only-Believing over Arbitrary Probability Distributions.
          In proceeding of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.

          Daxin Liu, Gerhard Lakemeyer.
          On the Verification of Belief Programs.
          http://arxiv.org/abs/2204.12562,2022.

          Daxin Liu, Qihui Feng.
          On the Progression of Belief.
          In proceeding of 18th International Conference on Principles of Knowledge Representation and Reasoning (KR), (Best Student Paper), 2021.

          Daxin Liu, Gerhard Lakemeyer.
          Reasoning about Beliefs and Meta-Beliefs by Regression in an Expressive Probabilistic Action Logic.
          In proceeding of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021.

          Daxin Liu, Gong Cheng, Qingxia Liu, Yuzhong Qu.
          Fast and Practical Snippet Generation for RDF Datasets.
          ACM Transactions on the Web (TWEB), 13(4):19, 2019.

          Gong Cheng, Daxin Liu, Yuzhong Qu.
          Fast Algorithms for Semantic Association Search and Pattern Mining.
          IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.

          Yu Gu, Yue Liang, Gong Cheng, Daxin Liu, Ruidi Wei, Yuzhong Qu.
          Diversified and Verbalized Result Summarization for Semantic Association Search.
          International Conference on Web Information Systems Engineering (WISE), 381-390, 2018

          Gong Cheng, Daxin Liu, Yuzhong Qu.
          Efficient algorithms for association finding and frequent association pattern mining.
          International Semantic Web Conference (ISWC), 119-134, 2016

Teaching

   RWTH Aachen University

Introduction to Artificial Intelligence [WS 2021/2022]
Introduction to Artificial Intelligence [WS 2020/2021]
Introduction to Knowledge Representation [SS 2020]
Introduction to Artificial Intelligence [WS 2019/2020]
Seminar: Selected Topics in Agent Behavior Modeling [SS 2019]
Lab Course: Flexible Task-Level Reasoning and Execution for Logistics Robots [WS 2018/2019]

   Nanjing University

Graph Theory [WS 2017/2018]

Recommemded Books and Papers

For reasoning about knowledge and belief:

[1] Levesque, Hector J., and Gerhard Lakemeyer. The logic of knowledge bases (2ed). MIT Press, 2022.
[2] Fagin, Ronald, Joseph Y. Halpern, Yoram Moses, and Moshe Vardi. Reasoning about knowledge. MIT Press, 2004.
The book [1] is a modal treatment of knowledge and more up to date while the book [2] is more classical.

For reasoning about actions and causality:

[3] Reiter, Raymond. Knowledge in action: logical foundations for specifying and implementing dynamical systems. MIT Press, 2001.
[4] Halpern, Joseph Y. Actual causality. MIT Press, 2016.
[3] is the Reiter's reconsideration of the situation calculus and [4] is the latest book about HP account of actual causality.

For reasoning about probability and uncertainty:

[5] Pearl, Judea. Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier, 2014.
[6] Bacchus, Fahiem, Joseph Y. Halpern, and Hector J. Levesque. Reasoning about noisy sensors and effectors in the situation calculus. Artificial Intelligence (1999).
[5] is the famous book on reasoning about probability and [6] provides a logical treatment of probability.

For verification and model-checking:

[7] Baier, Christel, and Joost-Pieter Katoen. Principles of model checking. MIT Press, 2008.

The above books and papers shaped my knowledge of KR, but, of course this is purely subjective and non-exhaustive.