![Hiroyuki Kido PhD](https://profiles-cardiff.imgix.net/attachments/1586?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Overview
I am a lecturer in School of Computer Science and Informatics at Cardiff University. I have been studying artificial intelligence since around 2002 when I encountered the famous book, Artificial Intelligence: A Modern Approach, in my university's library. I am currently working across logic, machine learning and neuroscience. I have educational backgrounds in three universities and research experiences in industry, a national institute and three universities across Japan, China and the UK.
Publication
2025
- Kido, H. 2025. Inference of abstraction for human-like probabilistic reasoning. Presented at: 4th Advanced Course and Symposium on Artificial Intelligence & Neuroscience (ACAIN), Tuscany, Italy, 22-25 September 2024 Presented at Nicosia, G. et al. eds.Machine Learning, Optimization, and Data Science. Lecture Notes in Computer Science Vol. 15510. Springer Cham pp. 191-206., (10.1007/978-3-031-82487-6_9)
- Kido, H. 2025. Inference of abstraction for human-like logical reasoning. Presented at: 4th Advanced Course and Symposium on Artificial Intelligence & Neuroscience (ACAIN), Tuscany, Italy, 22-25 September 2024 Presented at Nicosia, G. et al. eds.Machine Learning, Optimization, and Data Science, Vol. 15510. Lecture Notes in Computer Science Springer pp. 191-206., (10.1007/978-3-031-82487-6_14)
2022
- Kido, H. and Liao, B. 2022. A Bayesian approach to forward and inverse abstract argumentation problems. Journal of Applied Non-Classical Logics Volume, pp. 273-304. (10.1080/11663081.2022.2144830)
- Baroni, P., Cerutti, F. and Giacomin, M. 2022. A generalized notion of consistency with applications to formal argumentation. In: Toni, . et al. eds. Computational Models of Argument., Vol. 353. Frontiers in Artificial Intelligence and Applications IOS Press, pp. 56-67., (10.3233/FAIA220141)
- Kido, H. 2022. Generative logic models for data-based symbolic reasoning. Presented at: 8th International Workshop on Artificial Intelligence and Cognition (AIC 2022), Örebro University, 15-17 June 2022.
2018
- Kido, H. 2018. Bayesian model selection in statistical construction of justification. Presented at: Sixteenth International Conference on Principles of Knowledge Representation and Reasoning, Tempe, Arizona (USA), 27 October - 2 November 2018. pp. -.
2017
- Kido, H. and Okamoto, K. 2017. A Bayesian approach to argument-based reasoning for attack estimation. Presented at: 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19-25 August 2017 Presented at Sierra, C. ed.IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI pp. 249-255.
- Kido, H. and Okamoto, K. 2017. Balancing between cognitive and semantic acceptability of arguments. Presented at: 10th International Conference on Knowledge Science, Engineering and Management, Melbourne, Australia, 19-20 Aug 2017 Presented at Li, G. et al. eds.Knowledge Science, Engineering and Management, Vol. 10412. Springer Verlag pp. 160-173., (10.1007/978-3-319-63558-3_14)
- Kido, H., Ohsawa, Y. and Nitta, K. 2017. Paretian argumentation frameworks for Pareto optimal arguments. Journal of Logic and Computation 27(1), pp. 213-225. (10.1093/logcom/exv012)
2016
- Kido, H. and Cerutti, F. 2016. Formal reconciliatory dialogue based on shift from forward to backward deliberation. Argument and Computation 6(3), pp. 292-309. (10.1080/19462166.2016.1145139)
Adrannau llyfrau
- Baroni, P., Cerutti, F. and Giacomin, M. 2022. A generalized notion of consistency with applications to formal argumentation. In: Toni, . et al. eds. Computational Models of Argument., Vol. 353. Frontiers in Artificial Intelligence and Applications IOS Press, pp. 56-67., (10.3233/FAIA220141)
Cynadleddau
- Kido, H. 2025. Inference of abstraction for human-like probabilistic reasoning. Presented at: 4th Advanced Course and Symposium on Artificial Intelligence & Neuroscience (ACAIN), Tuscany, Italy, 22-25 September 2024 Presented at Nicosia, G. et al. eds.Machine Learning, Optimization, and Data Science. Lecture Notes in Computer Science Vol. 15510. Springer Cham pp. 191-206., (10.1007/978-3-031-82487-6_9)
- Kido, H. 2025. Inference of abstraction for human-like logical reasoning. Presented at: 4th Advanced Course and Symposium on Artificial Intelligence & Neuroscience (ACAIN), Tuscany, Italy, 22-25 September 2024 Presented at Nicosia, G. et al. eds.Machine Learning, Optimization, and Data Science, Vol. 15510. Lecture Notes in Computer Science Springer pp. 191-206., (10.1007/978-3-031-82487-6_14)
- Kido, H. 2022. Generative logic models for data-based symbolic reasoning. Presented at: 8th International Workshop on Artificial Intelligence and Cognition (AIC 2022), Örebro University, 15-17 June 2022.
- Kido, H. 2018. Bayesian model selection in statistical construction of justification. Presented at: Sixteenth International Conference on Principles of Knowledge Representation and Reasoning, Tempe, Arizona (USA), 27 October - 2 November 2018. pp. -.
- Kido, H. and Okamoto, K. 2017. A Bayesian approach to argument-based reasoning for attack estimation. Presented at: 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19-25 August 2017 Presented at Sierra, C. ed.IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI pp. 249-255.
- Kido, H. and Okamoto, K. 2017. Balancing between cognitive and semantic acceptability of arguments. Presented at: 10th International Conference on Knowledge Science, Engineering and Management, Melbourne, Australia, 19-20 Aug 2017 Presented at Li, G. et al. eds.Knowledge Science, Engineering and Management, Vol. 10412. Springer Verlag pp. 160-173., (10.1007/978-3-319-63558-3_14)
Erthyglau
- Kido, H. and Liao, B. 2022. A Bayesian approach to forward and inverse abstract argumentation problems. Journal of Applied Non-Classical Logics Volume, pp. 273-304. (10.1080/11663081.2022.2144830)
- Kido, H., Ohsawa, Y. and Nitta, K. 2017. Paretian argumentation frameworks for Pareto optimal arguments. Journal of Logic and Computation 27(1), pp. 213-225. (10.1093/logcom/exv012)
- Kido, H. and Cerutti, F. 2016. Formal reconciliatory dialogue based on shift from forward to backward deliberation. Argument and Computation 6(3), pp. 292-309. (10.1080/19462166.2016.1145139)
- Baroni, P., Cerutti, F. and Giacomin, M. 2022. A generalized notion of consistency with applications to formal argumentation. In: Toni, . et al. eds. Computational Models of Argument., Vol. 353. Frontiers in Artificial Intelligence and Applications IOS Press, pp. 56-67., (10.3233/FAIA220141)
- Kido, H. 2018. Bayesian model selection in statistical construction of justification. Presented at: Sixteenth International Conference on Principles of Knowledge Representation and Reasoning, Tempe, Arizona (USA), 27 October - 2 November 2018. pp. -.
- Kido, H. and Okamoto, K. 2017. A Bayesian approach to argument-based reasoning for attack estimation. Presented at: 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19-25 August 2017 Presented at Sierra, C. ed.IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI pp. 249-255.
- Kido, H. and Okamoto, K. 2017. Balancing between cognitive and semantic acceptability of arguments. Presented at: 10th International Conference on Knowledge Science, Engineering and Management, Melbourne, Australia, 19-20 Aug 2017 Presented at Li, G. et al. eds.Knowledge Science, Engineering and Management, Vol. 10412. Springer Verlag pp. 160-173., (10.1007/978-3-319-63558-3_14)
- Kido, H., Ohsawa, Y. and Nitta, K. 2017. Paretian argumentation frameworks for Pareto optimal arguments. Journal of Logic and Computation 27(1), pp. 213-225. (10.1093/logcom/exv012)
- Kido, H. and Cerutti, F. 2016. Formal reconciliatory dialogue based on shift from forward to backward deliberation. Argument and Computation 6(3), pp. 292-309. (10.1080/19462166.2016.1145139)
Research
I am interested in how the brain processes reasoning. I am currenly studying biologically plausible computational models of reasoning. Mumford suggests the existence of such a theory:
In summary, my belief is that pattern theory contains the germs of a universal theory of thought itself, one which stands in opposition to the accepted analysis of thought in terms of logic. ... The extraordinary similarity of the structure of all parts of the human cortex to each other and of human cortex with the cortex of the most primitive mammals suggests that a relatively simple universal principal governs its operation, even in complex and deep thinking. (David Mumford, Pattern Theory: A Unifying Perspective, 1994)
No one still knows how reasoning operates in our brains. My work gets inspiration from methods and approaches used in formal and informal logic, machine learning and neuroscience. My work is different from logic and machine learning, since I am more interested in a biologically plausible theory. My work is also different from neurosicnece, since I seek a computational model.
Teaching
I am currently teaching and leading the following modules.
- CM3112 (Artificial Intelligence)
- CMT215 (Automated Reasoning)
- CM2501 (Year of Study Abroad)
I am a Fellow of Higher Education Academy (FHEA).
Biography
Projects (Principal Investigator)
- 2018 - 2022: Argument-Based Bayesian Generative Models for Argumentation Mining, Scientific Research C, Grants-in-Aid for Scientific Research <KAKENHI>
- 2015 - 2019: Argument-Based Data-Driven Conflict Studies, Scientific Research B (Generative Research Fields “Conflict Studies”), Grants-in-Aid for Scientific Research <KAKENHI>
Awards
- JSAI (the Japanese Society for Artificial Intelligence) best paper award 2012
Experience
- Lecturer, School of Computer Science and Informatics, Cardiff University, UK
- Distinguished associate researcher, Institute of Logic and Cognition, Sun Yat-sen University, China
- Jokyo (research associate/assistant professor) and project researcher, School of Engineering, The University of Tokyo, Japan
- Postdoctoral researcher, National Institute of Advanced Industrial Science and Technology, Japan
- Full-time employee, Information Technology R&D Center, Mitsubishi Electric Co., Ltd., Tokyo, Japan
Education
- PhD in computational intelligence and systems science, Tokyo Institute of Technology, Japan
- MSc in computer science, Hokkaido University, Japan
- BSc in information engineering, Niigata University, Japan
Contact Details
Research themes
Specialisms
- Artificial intelligence