The Doctoral Consortium at AAMAS-2023

The DC at AAMAS-2023 provides an opportunity for PhD students working in the research area served by the conference to interact closely with established researchers and other students, to receive feedback on their work, and to get advice on managing their career. It is chaird by Ulle Endriss and Maria Gini.

Thanks to our PC members: Noa Agmon, Natasha Alechina, Chris Amato, Dorothea Baumeister, Joydeep Biswas, Ioannis Caragiannis, Siobhán Clarke, Ed Durfee, Piotr Faliszewski, Matthieu Geist, Rica Gonen, Marc Lanctot, Jérôme Lang, Kate Larson, Birgit Lugrin, Viviana Mascardi, Nicolas Maudet, Felipe Meneguzzi, Ann Nowé, Frans Oliehoek, Catherine Pelachaud, Juan A. Rodríguez-Aguilar, Jeffrey S. Rosenschein, Sasha Rubin, Fernando P. Santos, Gita Sukthankar, Samarth Swarup, Matthew Taylor, Kagan Tumer, Karl Tuyls, Mathijs de Weerdt, Michael Winikoff, and Makoto Yokoo.

Thanks to our mentors: Natasha Alechina, Chris Amato, Francesco Amigoni, Bo An, Elisabeth André, Haris Aziz, Francesco Belardinelli, Joydeep Biswas, Hau Chan, Virginia Dignum, Prashant Doshi, Edith Elkind, Piotr Faliszewski, Alessandro Farinelli, Maria Gini, Hadi Hosseini, Catholijn Jonker, Bruno Lacerda, Kate Larson, Bo Li, Michael Luck, Birgit Lugrin, Viviana Mascardi, Kamesh Munagala, Ann Nowé, Simon Parsons, Frans Oliehoek, Gopal Ramchurn, Fernando P. Santos, Sebastian Stein, Peter Stone, Samarth Swarup, Matthew Taylor, Kagan Tumer, Karl Tuyls, Michael Winikoff, Rym Zalila-Wenkstern, and Yair Zick.

Programme

The DC takes place on Monday 29 May 2023 in Room 15 of the South Gallery. Posters will be in Room 16. Each participating student presents in either the morning or the afternoon session. Note that one-to-one meetings between a student and their assigned mentor can take place at any time during the conference, on the initiative of the student.

If you present in the morning, put up your poster in Room 16 before 9:00. If you present in the afternoon, put up your poster in Room 16 before 14:00.

Invited Talk

Speaker: Michael Wooldridge (University of Oxford)

Title: How to Speak to the Public about Artificial Intelligence

Abstract: Since everything went crazy in AI, around 2012, I, like many other members of our community, have frequently found myself put in the position of having to talk about our field to a non-specialist audience. I've been interviewed on TV and radio, and spoken to endless university committees, government committees, and industrial conferences. More recently, following the publication of my two popular science books (the Ladybird Expert Guide to AI [2018], and The Road to Conscious Machines [2020]), I've even begun speaking at a literary festivals (believe me, I never expected to be doing this as a PhD student studying multiagent systems back in 1989). In this talk, I will relate these experiences, the mistakes I made, and what I learned from them — how our field is perceived, what people fear, hope, and expect from it, and how best to communicate excitement about the very real progress we've made recently with a realistic understanding of where we are and where we are going.

Bio: Michael Wooldridge is a Professor of Computer Science at the University of Oxford, and a programme director for AI at the Alan Turing Institute. He is a Fellow of the ACM, the Association for the Advancement of AI (AAAI), and the European Association for AI (EurAI). From 2014–16, he was President of the European Association for AI, and from 2015–17 he was President of the International Joint Conference on AI (IJCAI). As well as more than 400 technical articles on AI, he has published two popular science introductions to the field: The Ladybird Expert Guide to AI (2018), and The Road to Conscious Machines (Pelican, 2020).

Students Presenting in the Morning

You can find the extended abstracts associated with these presentations in the conference proceedings.

1. Forward-Looking and Backward-Looking Responsibility Attribution in Multi-Agent Sequential Decision Making
   Stelios Triantafyllou
2. Coalition Formation in Sequential Decision-Making under Uncertainty
   Saar Cohen
3. Preference Inference from Demonstration in Multi-objective Multi-agent Decision Making
   Junlin Lu
4. Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning.
   Lucia Cipolina
5. Reinforcement Learning and Mechanism Design for Routing of Connected and Autonomous Vehicles
   Behrad Koohy
6. Citizen Centric Demand Responsive Transport
   Alexander Masterman
7. Fair Transport Network Design using Multi-Agent Reinforcement Learning
   Dimitris Michailidis
8. Strategy Extraction for Transfer in AI Agents
   Archana Vadakattu
9. Characterizing Fairness in Societal Resource Allocation
   Tasfia Mashiat
10. Algorithmic Fairness in Temporal Resource Allocation
    Ashwin Kumar
11. Contests and Other Topics in Multi-Agent Systems
    Abheek Ghosh
12. Emergence of Cooperation on Networks
    Jacques Bara
13. Towards Creating Better Interactive Agents: Leveraging Both Implicit and Explicit Human Feedback
    Kate Candon
14. Effective Human-Machine Teaming through Communicative Autonomous Agents that Explain, Coach, and Convince
    Aaquib Tabrez
15. Enhancing User Understanding of Reinforcement Learning Agents Through Visual Explanations
    Yotam Amitai
16. Counterfactual Explanations for Reinforcement Learning Agents
    Jasmina Gajcin
17. Planning and Coordination for Unmanned Aerial Vehicles
    Jonathan Diller
18. Towards Scalable and Robust Decision Making in Partially Observable, Multi-Agent Environments
    Jonathon Schwartz
19. AI & Multi-agent Systems for Data-centric Epidemic Forecasting
    Alexander Rodriguez (not present)

Students Presenting in the Afternoon

You can find the extended abstracts associated with these presentations in the conference proceedings.

1. Artificial Intelligence Algorithms for Strategic Reasoning over Complex Multiagent Systems
   Zun Li
2. Learning Transferable Representations for Non-stationary Environments
   Mohammad Yasar
3. Towards Sample-Efficient Multi-Objective Reinforcement Learning
   Lucas N. Alegre
4. Learning Representations and Robust Exploration for Improved Generalization in Reinforcement Learning
   Nasik Muhammad Nafi (not present)
5. Multi-Advisor Dynamic Decision Making
   Zhaori Guo
6. Reinforcement Learning in Multi-Objective Multi-Agent Systems
   Willem Röpke
7. A Toolkit for Encouraging Safe Diversity in Skill Discovery
   Maxence Hussonnois
8. Assistive Robotics for Empowering Humans with Visual Impairments to Independently Perform Day-to-day Tasks
   Shivendra Agrawal
9. Uncertainty-aware Personal Assistant and Explanation Method for Privacy Decisions
   Gönül Ayci
10. Emergent Responsible Autonomy in Multi-Agent Systems
    Jayati Deshmukh
11. Safe Behavior Specification and Planning for Autonomous Robotic Systems in Uncertain Environments
    Jan Vermaelen
12. Verifiably Safe Decision-Making for Autonomous Systems
    Yi Yang
13. Explanation through Dialogue for Reasoning Systems
    Yifan Xu
14. Towards a Logical Account for Human-Aware Explanation Generation in Model Reconciliation Problems
    Stylianos Loukas Vasileiou
15. Logics for Information Aggregation
    John Lindqvist
16. Separations and Collapses in Computational Social Choice
    Michael C. Chavrimootoo
17. Fine-Grained Complexity of Fair and Efficient Allocations
    Aditi Sethia
18. Mechanism Design for Heterogeneous and Distributed Facility Location Problems
    Rongsen Zhang
19. Bipartite Matching for Repeated Allocation Problems
    Yohai Trabelsi