20152020

Research output per year

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Personal profile

Biography

I am a Lecturer (i.e., Assistant Professor) in the Department of Data Science and Artificial Intelligence (DSAI) at Monash University. I have earned my Ph.D. (2020) under the supervision of Professor Scott Sanner on the topic of optimal planning with deep neural networks, my MASc. (2016) on the topic of least-commitment partial-order planning under the supervision of Professor J. Christopher Beck and Professor Andre Augusto Cire and my BASc. in Industrial Engineering (2014) from University of Toronto. I was also a Postgraduate Affiliate with the Vector Institute and a visiting researcher at the Australian National University.

Education/Academic qualification

Optimal Planning with Learned Neural Network Transition Models, Ph.D., University of Toronto

1 Sep 201614 Apr 2020

Award Date: 18 Jun 2020

Mixed-Integer Linear Programming Models for Least-Commitment Partial-Order Planning, M.A.Sc., University of Toronto

1 Sep 20147 Nov 2016

Award Date: 15 Mar 2017

Industrial Engineering, B.A.Sc., University of Toronto

1 Sep 201018 Jun 2014

Award Date: 18 Jun 2014

Research area keywords

  • Automated Planning
  • Operations Research
  • Neural Networks

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Research Output

  • 8 Conference Paper
  • 2 Article

Compact and efficient encodings for planning in factored state and action spaces with learned Binarized Neural Network transition models

Say, B. & Sanner, S., Aug 2020, In : Artificial Intelligence. 285, 21 p., 103291.

Research output: Contribution to journalArticleResearchpeer-review

Scalable planning with deep neural network learned transition models

Wu, G., Say, B. & Sanner, S., 20 Jul 2020, In : Journal of Artificial Intelligence Research. 68, p. 571 606 p.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

Metric hybrid factored planning in nonlinear domains with constraint generation

Say, B. & Sanner, S., 2019, Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 16th International Conference, CPAIOR 2019 Thessaloniki, Greece, June 4–7, 2019 Proceedings. Rousseau, L-M. & Stergiou, K. (eds.). Cham Switzerland: Springer, p. 502-518 17 p. (Lecture Notes in Computer Science ; vol. 11494 ).

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

1 Citation (Scopus)

Reward potentials for planning with learned neural network transition models

Say, B., Sanner, S. & Thiebaux, S., 2019, Principles and Practice of Constraint Programming: 25th International Conference, CP 2019 Stamford, CT, USA, September 30 – October 4, 2019 Proceedings. Schiex, T. & de Givry, S. (eds.). Cham Switzerland: Springer, p. 674-689 16 p. (Lecture Notes in Computer Science; vol. 11802).

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

Planning in factored state and action spaces with learned Binarized Neural Network transition models

Say, B. & Sanner, S., 2018, Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. Lang, J. (ed.). Marina del Rey CA USA: Association for the Advancement of Artificial Intelligence (AAAI), p. 4815-4821 7 p.

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

Open Access
File