| Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient S Li, Y Wu, X Cui, H Dong, F Fang, S Russell Proceedings of the AAAI conference on artificial intelligence 33 (01), 4213-4220, 2019 | 220 | 2019 |
| When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing. F Fang, P Stone, M Tambe IJCAI, 2589-2595, 2015 | 209 | 2015 |
| Stackelberg security games: Looking beyond a decade of success A Sinha, F Fang, B An, C Kiekintveld, M Tambe IJCAI, 2018 | 162 | 2018 |
| Deploying paws: Field optimization of the protection assistant for wildlife security F Fang, T Nguyen, R Pickles, W Lam, G Clements, B An, A Singh, ... Proceedings of the AAAI Conference on Artificial Intelligence 30 (2), 3966-3973, 2016 | 148 | 2016 |
| " A Game of Thrones" When Human Behavior Models Compete in Repeated Stackelberg Security Games D Kar, F Fang, F Delle Fave, N Sintov, M Tambe Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015 | 132 | 2015 |
| Protecting moving targets with multiple mobile resources F Fang, AX Jiang, M Tambe Journal of Artificial Intelligence Research 48, 583-634, 2013 | 127 | 2013 |
| Protecting moving targets with multiple mobile resources F Fang, AX Jiang, M Tambe Journal of Artificial Intelligence Research 48, 583-634, 2013 | 127 | 2013 |
| Robust protection of fisheries with compass W Haskell, D Kar, F Fang, M Tambe, S Cheung, E Denicola Proceedings of the AAAI Conference on Artificial Intelligence 28 (2), 2978-2983, 2014 | 110 | 2014 |
| Deceiving cyber adversaries: A game theoretic approach A Schlenker, O Thakoor, H Xu, F Fang, M Tambe, L Tran-Thanh, ... AAMAS'18: Proceedings of the 17th International Conference on Autonomous …, 2018 | 109 | 2018 |
| Spatio-temporal pricing for ridesharing platforms H Ma, F Fang, DC Parkes Operations Research 70 (2), 1025-1041, 2022 | 83 | 2022 |
| What game are we playing? end-to-end learning in normal and extensive form games CK Ling, F Fang, JZ Kolter arXiv preprint arXiv:1805.02777, 2018 | 83 | 2018 |
| Computational sustainability: Computing for a better world and a sustainable future C Gomes, T Dietterich, C Barrett, J Conrad, B Dilkina, S Ermon, F Fang, ... Communications of the ACM 62 (9), 56-65, 2019 | 82 | 2019 |
| Spot poachers in action: Augmenting conservation drones with automatic detection in near real time E Bondi, F Fang, M Hamilton, D Kar, D Dmello, J Choi, R Hannaford, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 81 | 2018 |
| Cloudy with a chance of poaching: Adversary behavior modeling and forecasting with real-world poaching data D Kar, B Ford, S Gholami, F Fang, A Plumptre, M Tambe, M Driciru, ... International Conference on Autonomous Agents and Multiagent Systems, 2017 | 75 | 2017 |
| Evolutionary population curriculum for scaling multi-agent reinforcement learning Q Long, Z Zhou, A Gupta, F Fang, Y Wu, X Wang arXiv preprint arXiv:2003.10423, 2020 | 68 | 2020 |
| Deep reinforcement learning for green security games with real-time information Y Wang, ZR Shi, L Yu, Y Wu, R Singh, L Joppa, F Fang Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1401-1408, 2019 | 68 | 2019 |
| PAWS—A deployed game-theoretic application to combat poaching F Fang, TH Nguyen, R Pickles, WY Lam, GR Clements, B An, A Singh, ... AI Magazine 38 (1), 23-36, 2017 | 65 | 2017 |
| Artificial intelligence for social good: A survey ZR Shi, C Wang, F Fang arXiv preprint arXiv:2001.01818, 2020 | 64 | 2020 |
| Patrol strategies to maximize pristine forest area M Johnson, F Fang, M Tambe Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 295-301, 2012 | 63 | 2012 |
| Patrol strategies to maximize pristine forest area M Johnson, F Fang, M Tambe Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 295-301, 2012 | 63 | 2012 |
Milind TambeProfessor & Director CRCS Center @Harvard; Director "AI for Social Good" @Google ResearchVerified email at harvard.edu
