The DC at AAMAS 2024 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 chaired by Bahar Rastegari and Serena Villata, with Maria Gini and Stephen Cranefield as co-chairs on the day.

Thanks to our PC members: Maria Gini, Ed Durfee, Kagan Tumer, Natasha Alechina, Viviana Mascardi, Matthew E. Taylor, Rafael Bordini, Makoto Yokoo, Edith Elkind, Piotr Faliszewski, Jérôme Lang, Noa Agmon, Samarth Swarup, Lirong Xia, Marc Lanctot, Nicolas Maudet, Elizabeth Black, Fernando Santos, Sebastian Stein, Frans Oliehoek, Siobhan Clarke, Birgit Lugrin, Juan Antonio Rodriguez Aguilar, Jeffrey S. Rosenschein, Enrico Gerding, Toby Walsh, Catherine Pelachaud, Silvia Schiaffino, Michael Winikoff, Christopher Amato, Leila Amgoud.

Thanks to our mentors: Julie A. Adams, Christopher Amato, Reyhan Aydogan, Haris Aziz, Tim Baarlsag. Maria Gini, Hadi Hosseini, Ioannis Caragiannis, Louis Dennis, Sven Koenig, Ayumi Igarashi, Marc Lanctot, Minming Li , Maite Lopez-Sanches, Fabian Lorig, Artur Niewiadomski, Fernando P. Santos, Juan Antonio Rodriguez Aguilar, Sandip Sen, Sarath Sreedharan, Sebastian Stein, Samarth Swarup, Kagan Tumer, Michael Wellman, Michael Winikoff.

The DC takes place on Monday 6 May 2024 in Jade Room 3. 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.

Panelists: Maria Gini (University of Minnesota, USA), Gauthier Picard (ONERA, France), Fernando Pascoal Dos Santos (Amsterdam University, The Netherlands), Bastin Tony Roy Savarimuthu (University of Otago, NZ).

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

Students presenting in the morning

1. Tamara C.P. Florijn, Negotiation strategies for one-to-many negotiation with partial deals
2. Yihan Dong, The Multi-agent System based on LLM for Online Discussions
3. Yiwei Lyu, Interactive Control and Decision-Making for Multi-Robots Systems
4. Shiji Xing, Allocating Resources with Imperfect Information: from Cardinal to Epistemic Fairness
5. Nicholas Teh, Distributive and Temporal Fairness in Algorithmic Collective Decision-Making
6. Jiaxun Cui, Communication and Generalization in Multi-Agent Learning
7. Bram Grooten, Large Learning Agents: Towards continually aligned robots with scale in RL
8. Shivam Goel, Towards building Autonomous AI Agents and Robots for Open World Environments
9. Victor Gimenez-Abalos, Toward explainable agent behaviour
10. Pascal van der Vaart, Bayesian Model-Free Deep Reinforcement Learning
11. Jérôme Botoko Ekila, Emergence of Linguistic Conventions In Multi-Agent Systems Through Situated Communicative Interactions
12. Erin Richardson, Predicting and Protecting the Cognitive Health of Operators in Isolated, Confined, and Extreme Environments

Students presenting in the afternoon

1. Eura Nofshin, Leveraging Human Models to Personalize AI Interventions for Behavior Change
2. Baiting Luo, Adaptive Decision-Making in Non-Stationary Markov Decision Processes
3. Zhicheng Zhang, Advancing Sample Efficiency and Explainability in Multi-Agent Reinforcement Learning
4. Yash Shukla, Formal and Natural Language assisted Curriculum Generation for Reinforcement Learning Agents
5. Balint Gyevnar, Building Trustworthy Human-Centric Autonomous Systems Via Explanations
6. Himanshu Gupta, Efficient Continuous Space BeliefMDP Solutions for Navigation and Active Sensing
7. Gautham Vasan, Autonomous Skill Acquisition for Robots Using Graduated Learning
8. Jarrod Shipton, Cooperative Multi-Agent Reinforcement Learning in Convention Reliant Environments
9. Iosif Apostolakis, Abstraction in  Non-Monotonic Reasoning
10. Minghong Geng, Scaling up Cooperative Multi-agent Reinforcement Learning Systems
11. Pedro Santos        Generalizing, Objective-Specification in Markov Decision Processes
12. Calarina Muslimani, Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning