Welcome to Teamcore
The Teamcore group is focused on "AI for social good". We are focused on advancing AI and multiagent systems research for social impact in topics such as public health, conservation and public safety and security. We focus on fundamental research problems in multiagent systems, machine learning, reinforcement learning, game theory, bandit algorithms that are driven by these topics, ensuring a virtuous cycle of research and real-world applications. We simultaneously aim to achieve real-world social impact, often in domains with marginalized or endangered communities, and those that have not benefited from AI research in the past.
The key question we often focus on is how to optimize our limited intervention resources in these domains, and often take the form of decision aids to assist Non-Governmental (and Governmental) Organizations (NGOs). Our research is in service of the inspiring work done by these NGOs around the world, to empower them to directly use our AI tools and services; ultimately we wish to avoid being gatekeepers to this AI technology for social impact.
Our group has a long track record of building pioneering and influential systems that have achieved social impact in practice. With respect to public health, we have large populations to serve, but limited numbers of social workers or public health resources. Concrete example is work we have done with youth experiencing homelessness in Los Angeles; harnessing the social networks of these youth, we show that our AI algorithms are far more effective in empowering these youth to reduce HIV risk behaviors compared to traditional approaches. The technical research areas we focus on here are social networks -- enable influence maximization in social networks under dynamism and uncertainty. To that end, we work on approaches involving reinforcement learning, graph sampling, social network influence maximization and related topics. We have extended these approaches towards topics such as TB prevention, maternal and child health care and others. The systems we have built are the first large scale applications of influence maximization in social networks for real world impactful public health outcomes.
With respect to conservation, a key example is the PAWS AI system developed by our team that has been deployed in collaboration with wildlife conservation agencies to assist rangers around the world in protecting endagered wildlife. PAWS led to removals of 1000s 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, making PAWS available for use at 100s of national parks around the globe. The research areas we focus on here are merging game theory and machine learning, in an approach called Green Security Games.
Finally, with respect to public safety, we have large number of targets to protect and limited security resources. Our previous work on "security games" (game theory) research was also used on a daily basis (and continues to be used) by agencies such as the US Federal Air Marshals, US Coast Guard and LA Airport, for innovative patrol strategies and for optimally allocating officers, boats, or other security for improving public safety. These systems demonstrate the first use computational game theory for real-world operational security. There are by now many new applications for security games, including in cybersecurity, protecting of endangered wildlife and fisheries, protecting forests, and others. In fact, unanticipated new applications, including audit games, drug design against viruses, traffic enforcement, software code testing, adversarial machine learning, and others, have also sprung up. In addition to the research impact, this article credits our research as having produced a net benefit of over $64 million by saving costs or by increasing security at airports and sea ports.
The Teamcore group started in 1995. The name derives from early multiagent research we conducted in multiagent teamwork, and also inspired by the concept of the "core" in cooperative game theory. Some milestones in terms of systems and key papers of our research group:
- STEAM multiagent system, offering reusable rules for teamwork: Won the Influential paper award at AAMAS conference 2012
- Elective Elves multiagent system, a pioneering system of office personal assistants
- ADOPT first asynchronous complete algorithm for distributed constraint optimization
- ARMOR security games deployed at LA airport first application of computational game theory for operational security
- IRIS security games for optimal randomized scheduling air marshals deployed by the Federal Air Marshals
- PROTECT security games systems deployed by the US Coast Guard for optimized patrolling
- PAWS system for wildlife protection actually operated to remove snares from national parks in Uganda and Cambodia
- CHANGE system for HIV Prevention first large scale application of influence maximization in social network for public health
EC20: AI FOR PUBLIC HEALTH & CONSERVATION: Learning and Planning
Using AI for Social Good – Milind Tambe | Lecture Series on AI
ABC7 Interview COVID-19 modeling
- Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning t.co/J854UDnENw by Kai Wang et al. including @MilindTambe_AI #NeuralNetwork #ReinforcementLearning
- लेख फार छान आहे धन्यवाद. या कामात @WCT_India आणि Google Research India बरोबरच @PradeepVarakan1 आणि @susobhang70 हे सुद्धा भागीदार आहेत t.co/ODy8bLxUwj
- @etzioni @WSJ Congratulations Oren! Great news!
- PAWS: Anti-Poaching AI Predicts Where Illegal Hunters Will Show Up Next
- ARMMAN scales its AI efforts to improve maternal and child health in India, with support from Google.org
- AI can help reduce the risk of HIV in high-risk communities
- AAAI 2021 Best papers announced, Synced review
- CREATE Detlof von Winterfeldt Outstanding Research Award, 2020: Professor Milind Tambe
- Where to patrol next: ‘Netflix’ of ranger AI serves up poaching predictions