Masood Feyzbakhsh Rankooh

I am a Postdoctoral Research Fellow at Tampere University, specializing in Answer Set Programming (ASP), Automated Reasoning, and Constraint Programming. My research primarily focuses on advancing ASP techniques to enhance AI problem-solving, particularly in optimization and knowledge representation. With a strong background from Aalto University and Sharif University of Technology, I am also involved in projects related to Explainable AI, where I strive to make AI systems more transparent and interpretable.
 

As a Postdoctoral Research Fellow, I develop cutting-edge algorithms for Answer Set Programming (ASP) and its applications in AI planning and optimization. I also mentor graduate students, involve in research collaborations, and actively participate in publishing and presenting research findings at major AI conferences.

My expertise centers on Answer Set Programming (ASP), with additional strengths in Automated Reasoning, SAT-based Planning, Constraint Programming, and Knowledge Representation. I have developed innovative ASP-based techniques that are crucial for solving complex AI problems, including applications in Explainable AI.

Best Paper Award at the 39th International Conference on Logic Programming (ICLP 2023) for work on ASP.
Development of efficient translations of ASP into SAT and Integer Programming.
Significant contributions to Explainable AI, recognized through multiple publications.

Program Committee Member: European Conference on Artificial Intelligence (ECAI), AAAI Conference on Artificial Intelligence, and International Conference on Automated Planning and Scheduling (ICAPS).

Invited Lecturer: Sharif University of Technology, teaching courses on Artificial Intelligence and Compiler Design.

My research aims to push the boundaries of Answer Set Programming (ASP) and its integration into broader AI systems. I also focus on employing ASP particularly in the context of Explainable AI, where making AI systems more interpretable and transparent is crucial.
 

• Answer Set Programming (ASP)
• Satisfiability (SAT)
• Constraint Programming
• AI Planning and Heuristic Search
• Knowledge Representation and Reasoning
• Explainable AI

My academic career includes a Postdoctoral Fellowship at Tampere University, a Postdoctoral Researcher position at Aalto University, and extensive experience in AI research at Sharif University of Technology. My work, especially in Answer Set Programming (ASP) and Explainable AI, has been published in leading AI conferences and journals. I am dedicated to advancing AI through innovative ASP research and making AI systems more interpretable and practical.

1. Jussi Rintanen and Masood Feyzbakhsh Rankooh. Symmetry-Breaking Constraints for Directed Graphs. 26th European Conference on Artificial Intelligence (ECAI 2024).
2. Masood Feyzbakhsh Rankooh and Tomi Janhunen. Improved Encodings of Acyclicity for Translating Answer Set Programming into Integer Programming. Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI 2024).
3. Masood Feyzbakhsh Rankooh and Tomi Janhunen. Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs (Extended Abstract). Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI 2024).
4. Masood Feyzbakhsh Rankooh and Tomi Janhunen. Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs. Theory and Practice of Logic Programming 23(4), pp. 782-796, 2023. Best Paper Award at the 39th International Conference on Logic Programming (ICLP 2023).
5. Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, and Miikka Vilander. Short Boolean Formulas as Explanations in Practice. Logics in Artificial Intelligence - 18th European Conference (JELIA 2023).
6. Anssi Yli-Jyrä, Masood Feyzbakhsh Rankooh, and Tomi Janhunen. Pruning Redundancy in Answer Set Optimization Applied to Preventive Maintenance Scheduling. Practical Aspects of Declarative Languages - 25th International Symposium (PADL 2023).
7. Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, and Miikka Vilander. Explainability via Short Formulas: the Case of Propositional Logic with Implementation. Joint Proceedings of the 1st International Workshop on HYbrid Models for Coupling Deductive and Inductive ReAsoning (HYDRA 2022) and the 29th RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA 2022) co-located with the 16th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR 2022).
8. Masood Feyzbakhsh Rankooh and Tomi Janhunen. Efficient Computation of Answer Sets via SAT Modulo Acyclicity and Vertex Elimination. Logic Programming and Nonmonotonic Reasoning - 16th International Conference (LPNMR 2022).
9. Masood Feyzbakhsh Rankooh and Jussi Rintanen. Efficient Computation and Informative Estimation of h+ by Integer and Linear Programming. Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling (ICAPS 2022).
10. Masood Feyzbakhsh Rankooh and Jussi Rintanen. Efficient Encoding of Cost Optimal Delete-Free Planning as SAT. 36th AAAI Conference on Artificial Intelligence (AAAI 2022).
11. Masood Feyzbakhsh Rankooh and Jussi Rintanen. Propositional Encodings of Acyclicity and Reachability by Using Vertex Elimination. 36th AAAI Conference on Artificial Intelligence (AAAI 2022).
12. Masood Feyzbakhsh Rankooh and Gholamreza Ghassem-Sani. ITSAT: An Efficient SAT-Based Temporal Planner. Journal of Artificial Intelligence Research (JAIR), Vol. 53, 2015, pp. 541-632.
13. Masood Feyzbakhsh Rankooh and Gholamreza Ghassem-Sani. New Encoding Methods for SAT-based Temporal Planning. Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS 2013).
14. Masood Feyzbakhsh Rankooh, Ali Mahjoob, and Gholamreza Ghassem-Sani. Using Satisfiability for Non-optimal Temporal Planning. 13th European Conference on Logics in Artificial Intelligence (JELIA 2012).
15. Masood Feyzbakhsh Rankooh and Gholamreza Ghassem-Sani. A Complete State-Space Based Temporal Planner. Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2011).