Research output per year
Research output per year
Project Details
Description
As artificial intelligence (AI) tools employ more advanced reasoning
mechanisms and computation, it becomes increasingly difficult to
understand why certain decisions are made. Explainable AI research
aims to fulfill the need for trustworthy AI systems that can explain
their reasoning in a human-understandable way. Our proposed
contribution to explainable AI is situated in the domain of constraint
solving and optimization, where we aim to augment constraint
solvers with explainable agency .
Based on research questions that came out of a preliminary study
performed by the two PIs, the high-level objective of this research
project is to design an integrated framework for explainable
constraint satisfaction and optimization. Developing such a
framework comes with several questions, related to scalability (the
ability to explain large instances), generality (the ability to answer
different types of questions) and interactability (the ability to interact
in a natural and fluent way with a user).
mechanisms and computation, it becomes increasingly difficult to
understand why certain decisions are made. Explainable AI research
aims to fulfill the need for trustworthy AI systems that can explain
their reasoning in a human-understandable way. Our proposed
contribution to explainable AI is situated in the domain of constraint
solving and optimization, where we aim to augment constraint
solvers with explainable agency .
Based on research questions that came out of a preliminary study
performed by the two PIs, the high-level objective of this research
project is to design an integrated framework for explainable
constraint satisfaction and optimization. Developing such a
framework comes with several questions, related to scalability (the
ability to explain large instances), generality (the ability to answer
different types of questions) and interactability (the ability to interact
in a natural and fluent way with a user).
| Acronym | FWOAL1002 |
|---|---|
| Status | Finished |
| Effective start/end date | 1/01/21 → 31/12/24 |
Keywords
- constraint programming
- explainability
Flemish discipline codes in use since 2023
- Operations research and mathematical programming
- Artificial intelligence not elsewhere classified
- Knowledge representation and reasoning
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Exploiting Symmetries in MUS Computation
Bleukx, I., Verhaege, H., Bogaerts, B. & Guns, T., 2025, Proceedings of the Thirty-Ninth Conference on Artificial Intelligence (AAAI). 11 ed. Association for the Advancement of Artificial Intelligence, Vol. 39. p. 11122-11130 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence).Research output: Chapter in Book/Report/Conference proceeding › Conference paper
Open Access2 Citations (Scopus) -
Incremental SAT-Based Enumeration of Solutions to the Yang-Baxter Equation
Van Caudenberg, D. S., Bogaerts, B. & Vendramin, L., 2025, Tools and Algorithms for the Construction and Analysis of Systems - 31st International Conference, TACAS 2025, Held as Part of the International Joint Conferences on Theory and Practice of Software, ETAPS 2025, Proceedings. Gurfinkel, A. & Heule, M. (eds.). p. 3-22 20 p. (Lecture Notes in Computer Science; vol. 15697 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference paper
Open Access1 Citation (Scopus) -
Using Symmetries to Lift Satisfiability Checking
Carbonnelle, P., Schenner, G., Bruynooghe, M., Bogaerts, B. & Denecker, M., 25 Mar 2024, Thirty-Eighth {AAAI} Conference on Artificial Intelligence, AAAI 2024. Woolridge, M., Dy, J. & Natarajan, S. (eds.). 8 ed. AAAI Press, Vol. 38. p. 7961-7968 8 p. (Proceedings of the AAAI Conference on Artificial Intelligence).Research output: Chapter in Book/Report/Conference proceeding › Conference paper
Open Access