On COPs, Bandits, and AI for Good

Add to your list(s) Download to your calendar using vCal

• Long Tran-Thanh (University of Warwick)
• Tuesday 28 July 2020, 13:00-14:00
• via Zoom, Meeting ID: 326 414 7316, Password: 772020..

If you have a question about this talk, please contact Hong Duong.

In the recent years there has been an increasing interest in applying techniques from artificial intelligence (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 work in this area have been focusing on designing machine learning algorithms to learn new insights/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/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.

Short bio of the speaker:

Long is a Hungarian-Vietnamese computer scientist at the University of Warwick, UK, where he is currently an Associate Professor. He obtained his PhD 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 Artificial Intelligence 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 have received a number of national/international awards, such as:

(i) BCS /CPHC Best Computer Science PhD Dissertation Award (2012/13) – Honourable Mention; (ii) ECCAI /EurAI Best Artificial Intelligence Dissertation Award (2012/13) – Honourable Mention; (iii) AAAI Outstanding Paper Award (2012) – Honourable Mention (out of more than 1000 submissions); and (iv) ECAI Best Student Paper Award (2012)- Runner-Up (out of more than 600 submissions). (v) 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, which will be held in London, UK.

This talk is part of the Data Science and Computational Statistics Seminar series.

This talk is included in these lists:

• Data Science and Computational Statistics Seminar
• School of Mathematics events
• via Zoom, Meeting ID: 326 414 7316, Password: 772020.

Note that ex-directory lists are not shown.