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Personal profile

Biography

I am a Senior Lecturer at the Optimisation research group of the Faculty of Information Technology of Monash University.

Currently, my research is mainly focused on the development and improvement of highly efficient SAT- and SMT-based (satisfiability modulo theories) decision and optimization procedures targeting a variety of important practical applications in AI: from software package upgradability and Boolean formula minimization to model-based diagnosis (MBD), software fault localization and eXplainable AI (XAI).

Education/Academic qualification

Computer Science, Doctor of Philosophy, Irkutsk State University (ISU)

Award Date: 19 Nov 2010

Applied Mathematics, Diploma (Honours Degree), Irkutsk State University (ISU)

Award Date: 15 Jun 2006

Research area keywords

  • Satisfiability
  • Computational Logic
  • Knowledge Representation and Reasoning
  • Automated Reasoning
  • Artificial Intelligence
  • Combinatorial Optimisation

Network

Recent external collaboration on country level. Dive into details by clicking on the dots or
  • Branch location problems with maximum satisfiability

    Zaikin, O., Ignatiev, A. & Marques-Silva, J., 2020, ECAI Digital - 2020: 24th European Conference on Artificial Intelligence . De Giacomo, G., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarin, A. & Lang, J. (eds.). Amsterdam Netherlands: IOS Press, p. 379-386 8 p. (Frontiers in Artificial Intelligence and Applications; vol. 325).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Open Access
    File
  • Computing optimal decision sets with SAT

    Yu, J., Ignatiev, A., Stuckey, P. J. & Le Bodic, P., 2020, Principles and Practice of Constraint Programming: 26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings. Simonis, H. (ed.). Cham Switzerland: Springer, p. 952-970 19 p. (Lecture Notes in Computer Science ; vol. 12333).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

  • Towards formal fairness in machine learning

    Ignatiev, A., Cooper, M. C., Siala, M., Hebrard, E. & Marques-Silva, J., 2020, Principles and Practice of Constraint Programming : 26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings. Simonis, H. (ed.). Cham Switzerland: Springer, p. 846-867 22 p. (Lecture Notes in Computer Science ; vol. 12333).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Open Access
    File
  • Towards trustable explainable AI

    Ignatiev, A., 2020, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. Bessiere, C. (ed.). Marina del Rey CA USA: Association for the Advancement of Artificial Intelligence (AAAI), p. 5154-5158 5 p.

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Open Access
    File
  • Abduction-based explanations for machine learning models

    Ignatiev, A., Narodytska, N. & Marques-Silva, J., 2019, Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence. Van Hentenryck, P. & Zhou, Z-H. (eds.). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI), p. 1511-1519 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 33, no. 1).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Open Access
    File
    13 Citations (Scopus)
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