# Conservation ![](/sites/g/files/omnuum6171/files/styles/hwp_1_1__720x720_scale/public/crcs/files/lion_in_crosshairs.jpg?itok=hRuNuGGz) AI for Conservation refers to the application of artificial intelligence to conservation, such as wildlife protection and the conservation and responsible stewardship of natural resources. For example, in the green security domain, the repeated and strategic interaction between those who protect these resources and those who seek to attack or exploit these resources can be modeled using game theory as a repeated game. While our predictive analytics effort focuses on predicting where adversaries (e.g., poachers) will strike, our prescriptive analytics work provides recommendations to defenders (e.g., rangers) to conduct strategic, randomized patrols. These analytics can be supported using machine learning, for example by detecting poachers or animals in unmanned aerial vehicle (UAV) imagery automatically. [**Project Spotlight: PAWS - Protection Assistant for Wildlife Security**](https://arxiv.org/pdf/2009.06560.pdf) The PAWS AI system has been developed by CRCS researchers and collaborators. The Protection Assistant for Wildlife Security (PAWS) is an artificial intelligence (AI) software for wildlife protection. PAWS has been deployed in collaboration with wildlife conservation agencies to assist rangers around the world in protecting endangered wildlife. PAWS helps conservation managers plan more informed patrol strategies to combat wildlife poaching, illegal logging, and illegal fishing. Taking in past poaching records and data about the geography of the protected area, PAWS uses machine learning to predict poachers’ behavior. After learning a model of poaching behavior, PAWS produces poaching risk maps and suggests patrol routes for rangers. PAWS led to removals of thousands of traps used to kill and maim endangered wildlife in national parks in countries such Cambodia and Uganda. Furthermore, PAWS is integrated with the SMART software, which is a monitoring and reporting tool used in over 800 protected areas around the world. This means that PAWS is available for use at hundreds of national parks across the globe. The research areas we focus on here merges game theory and machine learning, in an approach called Green Security Games. [Multi-armed bandits to balance exploration and exploitation (AAAI'21)](https://arxiv.org/abs/2009.06560) Our paper Dual-Mandate Patrols: Multi-Armed Bandits for Green Security, which was selected as a Best Paper Runner Up at AAAI 2021, focuses on helping rangers choose where to patrol in a protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. Our proposed LIZARD algorithm bridges the gap between combinatorial and Lipschitz bandits, presenting an approach that achieves theoretical no-regret and performs well in experiments on real-world poaching data. For more information about PAWS, please visit the [Teamcore PAWS](https://teamcore.seas.harvard.edu/paws-protection-assistant-wildlife-security) webpage. ## Publications on Conservation Download 5 citations download- [BibTeX](/bibcite/export?pager_style=standard_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B0%5D%5Btarget_id%5D=118667&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B1%5D%5Btarget_id%5D=122860&&&format=bibtex) - [EndNote X3 XML](/bibcite/export?pager_style=standard_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B0%5D%5Btarget_id%5D=118667&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B1%5D%5Btarget_id%5D=122860&&&format=endnote8) - [EndNote 7 XML](/bibcite/export?pager_style=standard_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B0%5D%5Btarget_id%5D=118667&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B1%5D%5Btarget_id%5D=122860&&&format=endnote7) - [Endnote tagged](/bibcite/export?pager_style=standard_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B0%5D%5Btarget_id%5D=118667&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B1%5D%5Btarget_id%5D=122860&&&format=tagged) - [Marc](/bibcite/export?pager_style=standard_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B0%5D%5Btarget_id%5D=118667&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B1%5D%5Btarget_id%5D=122860&&&format=marc) - [PubMedId](/bibcite/export?pager_style=standard_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B0%5D%5Btarget_id%5D=118667&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B1%5D%5Btarget_id%5D=122860&&&format=pubmed_id) - [RIS](/bibcite/export?pager_style=standard_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B0%5D%5Btarget_id%5D=118667&taxonomy_filters%5Bfield_hwp_c_publications12345678%5D%5B1%5D%5Btarget_id%5D=122860&&&format=ris) ### 2022 Xu, L., Biswas, A., Fang, F. & Tambe, M. [Ranked Prioritization of Groups in Combinatorial Bandit Allocation](/publications/ranked-prioritization-groups-combinatorial-bandit-allocation). in *31st International Joint Conference on Artificial Intelligence (IJCAI 2022)* (2022). Xu, L., Biswas, A., Fang, F. & Tambe, M. [Ranked Prioritization of Groups in Combinatorial Bandit Allocation](/publications/ranked-prioritization-groups-combinatorial-bandit-allocation). in *31st International Joint Conference on Artificial Intelligence (IJCAI 2022)* (2022). - [ descriptionPublisher's Version](https://arxiv.org/abs/2205.05659) - [ descriptionPublisher's Version](https://arxiv.org/abs/2205.05659) ### 2021 Puri, A. & Bondi, E. [Space, Time, and Counts: Improved Human vs Animal Detection in Thermal Infrared Drone Videos for Prevention of Wildlife Poaching](/publications/space-time-and-counts-improved-human-vs-animal-detection-thermal-infrared-drone). *KDD 2021 Fragile Earth Workshop* (2021). Puri, A. & Bondi, E. [Space, Time, and Counts: Improved Human vs Animal Detection in Thermal Infrared Drone Videos for Prevention of Wildlife Poaching](/publications/space-time-and-counts-improved-human-vs-animal-detection-thermal-infrared-drone). *KDD 2021 Fragile Earth Workshop* (2021). ### 2020 Bayramli, I., Bondi, E. & Tambe, M. [In the Shadow of Disaster: Finding Shadows to Improve Damage Detection](/publications/shadow-disaster-finding-shadows-improve-damage-detection). in *AI for Social Good Workshop* (2020). Bayramli, I., Bondi, E. & Tambe, M. [In the Shadow of Disaster: Finding Shadows to Improve Damage Detection](/publications/shadow-disaster-finding-shadows-improve-damage-detection). in *AI for Social Good Workshop* (2020). - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfIn the Shadow of Disaster...](/sites/g/files/omnuum6171/files/crcs/files/ai4sg_2020_paper_76.pdf) Rapid damage assessment after natural disasters is crucial for effective planning of relief efforts. Satellites with Very High Resolution (VHR) sensors can provide a detailed aerial image of the affected area, but current damage detection systems are... - [ picture\_as\_pdfIn the Shadow of Disaster...](/sites/g/files/omnuum6171/files/crcs/files/ai4sg_2020_paper_76.pdf) Perrault, A., Fang, F., Sinha, A. & Tambe, M. [AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline](/publications/ai-social-impact-learning-and-planning-data-deployment-pipeline). *AI Magazine* (2020). Perrault, A., Fang, F., Sinha, A. & Tambe, M. [AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline](/publications/ai-social-impact-learning-and-planning-data-deployment-pipeline). *AI Magazine* (2020). - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdf2001.00088.pdf](/sites/g/files/omnuum6171/files/crcs/files/2001.00088.pdf) With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond... - [ picture\_as\_pdf2001.00088.pdf](/sites/g/files/omnuum6171/files/crcs/files/2001.00088.pdf) Wang, K., Wilder, B., Perrault, A. & Tambe, M. [Automatically Learning Compact Quality-aware Surrogates for Optimization Problems](/publications/automatically-learning-compact-quality-aware-surrogates-optimization-problems). in *NeurIPS (Spotlight)* (2020). Wang, K., Wilder, B., Perrault, A. & Tambe, M. [Automatically Learning Compact Quality-aware Surrogates for Optimization Problems](/publications/automatically-learning-compact-quality-aware-surrogates-optimization-problems). in *NeurIPS (Spotlight)* (2020). - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfautomatically-learning-co...](/sites/g/files/omnuum6171/files/crcs/files/neurips-2020-automatically-learning-compact-quality-aware-surrogates-for-optimization-problems-paper.pdf) Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solvingthe problem using these values. Recent work has shown that including the optimization problem... - [ picture\_as\_pdfautomatically-learning-co...](/sites/g/files/omnuum6171/files/crcs/files/neurips-2020-automatically-learning-compact-quality-aware-surrogates-for-optimization-problems-paper.pdf) [ More arrow\_circle\_right ](/publications)