Generative Logic Model for Data-Driven Discovery of Symbolic Commonsense Knowledge [Self Funded Students Only]


   Cardiff School of Computer Science & Informatics

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  Dr Hiroyuki Kido, Dr MWA Caminada  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

This project aims to study and implement algorithms for generating symbolic commonsense knowledge from potentially inconsistent, incomplete, dynamic, subjective and distributed data. The project uses a generative logic model, which is a data-driven logical and statistical reasoning approach taking advantage of the expressive power of formal logic and the predictive power of Bayesian machine learning. Target applications of the project include argumentation mining, causal inference, counterfactuals and explainable artificial intelligence. The deliverables include the dissertation and algorithm source code. The project will expose the successful student to the interdisciplinary field across logic, machine learning and neuroscience, where important open questions remain unsolved. Under the research outline, the project will provide the successful student with the opportunity to follow their curiosity and to carry out research into something they are interested in. 

Keywords: Logic, reasoning, learning, Bayesian statistics, prediction, explanation, entailment, paraconsistency, parapossibility, counterfactuals, nonmonotonicity, action, argumentation, data mining 

Contact for more information on the project: [Email Address Removed]

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas. 

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component. 

This application is open to students worldwide. 

 How to apply

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below 

This project is accepting applications all year round, for self-funded candidates via https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics  

In order to be considered candidates must submit the following information:  

  • Supporting statement  
  • CV  
  • In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD 
  • Qualification certificates and Transcripts 
  • Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded) 
  • References x 2  
  • Proof of English language (if applicable) 

If you have any questions or need more information, please contact [Email Address Removed] 

Computer Science (8)

Funding Notes

This project is offered for self-funded students only, or those with their own sponsorship or scholarship award.
Please note that a PhD Scholarship may also available for this PhD project. If you are interested in applying for a PhD Scholarship, please search FindAPhD for this specific project title, supervisor or School within its Scholarships category.

References

Hiroyuki Kido: Generative Logic with Time: Beyond Logical Consistency and Statistical Possibility (To appear), CoRR in arXiv (2022)
Hiroyuki Kido: Generative Logic Models for Data-Based Symbolic Reasoning, AIC 2022, 8th International Workshop on Artificial Intelligence and Cognition (2022)
Hiroyuki Kido, Keishi Okamoto: Bayes Meets Entailment and Prediction: Commonsense Reasoning with Non-monotonicity, Paraconsistency and Predictive Accuracy, CoRR abs/2012.08479v3 (2020)

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