This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.
"COPs, Bandits, and AI for Good"
In recent years, there has been an increasing interest in applying techniques from AI to tackle societal and environmental challenges, ranging from climate change and natural disasters, to food safety and disease spread. These efforts are typically known under the name AI for Good. While many research works in this area have been focusing on designing machine learning algorithms to learn new insights and predict future events from previously collected data, there is another domain where AI has been found to be useful, namely resource allocation and decision making. In particular, a key step in addressing societal and environmental challenges is to efficiently allocate a set of sparse resources to mitigate the problem(s). For example, in the case of wildfire, a decision maker has to adaptively and sequentially allocate a limited number of firefighting units to stop the spread of the fire as soon as possible. Another example comes from the problem of housing management for people in need, where a limited number of housing units have to be allocated to applicants in an online manner over time.
While sequential resource allocation can be often casted as (online) combinatorial optimisation problems (COPs), they can differ from the standard COPs when the decision maker has to perform under uncertainty (e.g., the value of the action is not known in advance, or future events are unknown at the decision-making stage). In the presence of such uncertainty, a popular tool from the decision-making literature, called multi-armed bandits, comes in handy. In this talk, I will demonstrate how to efficiently combine COPs with bandit models to tackle some AI for Good problems. In particular, I first show how to combine knapsack models with combinatorial bandits to efficiently allocate firefighting units and drones to mitigate wildfires. In the second part of the talk, I will demonstrate how interval scheduling, paired up with blocking bandits, can be a useful approach as a housing assignment method for people in need.
About the Speaker
Long is a Hungarian-Vietnamese computer scientist at the University of Warwick, U.K., where he is currently an associate professor. He obtained his Ph.D. in Computer Science from Southampton in 2012, under the supervision of Nick Jennings and Alex Rogers. Long has been doing active research in a number of key areas of AI and multi-agent systems, mainly focusing on multi-armed bandits, game theory, and incentive engineering, and their applications to crowdsourcing, human-agent learning, and AI for Good. He has published more than 60 papers at top AI conferences (AAAI, AAMAS, ECAI, IJCAI, NeurIPS, UAI) and journals (JAAMAS, AIJ), and has received a number of national and international awards, such as BCS/CPHC Best Computer Science Ph.D. Dissertation Award (2012/13 Honourable Mention); ECCAI/EurAI Best Artificial Intelligence Dissertation Award (2012/13 Honourable Mention); AAAI Outstanding Paper Award (2012 Honourable Mention); ECAI Best Student Paper Award (2012 Runner-Up); and IJCAI 2019 Early Career Spotlight Talk (invited). Long currently serves as a board member (2018-2024) of the IFAAMAS Directory Board, the main international governing body of the International Federation for Autonomous Agents and Multiagent Systems, a major sub-field of the AI community. He is also the local chair of the AAMAS 2021 conference.