Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners - Robohub

Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners

robohub.org/congratulati...

Just got back from #AAMAS2025. Great conference! Highly recommended! Very high density of cool material!

Learning in Games with Progressive Hiding When learning to play an imperfect information game, it is often easier to first start with the basic mechanics of the game rules. For example, one can play several example rounds with private cards r...

📍Poster: 10am
🎤 Talk: 11:30am
📄 Paper: arxiv.org/abs/2409.03875
If you're at #AAMAS2025, come by and say hi to @sharky6000.bsky.social!
And let us know what you think! 🤝

6/6

To our knowledge, this is the first use of information relaxation techniques from operations research in computational game theory.
We're excited to see how this perspective bridges communities!
#AAMAS2025 #ReinforcementLearning #MultiAgentSystems

5/6

🎉 Super excited: today @sharky6000.bsky.social is presenting our new algorithm Progressive Hiding at #AAMAS2025!
It's a learning method for games with imperfect information.
🔗 See his post: bsky.app/profile/shar...
Wish I could be there! 😢 1/6

Discussions, energy, and enthusiasm!

Session 3: Demos and posters
📍3rd floor, Ontario Exhibit Hall
#AAMAS2025

Main track presentations started today! #AAMAS2025

Today our paper with James Rudd-Jones and María Pérez-Ortiz "Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis" will be presented at the #AAMAS2025 Conference in Detroit.

Time: 2pm ET
Room: Richard

Link to the paper: arxiv.org/abs/2504.12777

@aamasconf.bsky.social

AI coach improves human teamwork | Computer Science | Rice University

Rice CS researchers have developed a first-of-its-kind AI Coach, Socratic, that analyzes team interactions and offers real-time collaboration tips to improve human teamwork. Assistant Prof Vaibhav Unhelkar and his PhD student Sangwon Seo will present Socratic at #AAMAS2025. bit.ly/3SzPXlT

Looking forward to present our research at #AAMAS2025 in Detroit, USA!

Excited our #AAMAS2025 paper has been selected as Best Paper Award finalist. It studies on-demand delivery with UAVs that learn their capabilities

🎥 youtu.be/9afhI1pgeI8
📄 arxiv.org/abs/2504.08585

@salahtalamali.bsky.social @genkimiyauchi.bsky.social @tuda.bsky.social @sheffielduni.bsky.social

Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures Counterfactual explanations indicate the smallest change in input that can translate to a different outcome for a machine learning model. Counterfactuals have generated immense interest in high-stakes...

🔈 Sharing our recent paper on "Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures" 🎉 🎉 Accepted at #AAMAS2025
Joint work with: Erfaun Noorani Pasan Dissanayake Faisal Hamman #Explainability #XAI #AlgorithmicRecourse #EntropicRisk arxiv.org/abs/2503.07934

Super excited to attend #AAMAS2025 where I will be showcasing my new paper "Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment"... See my profile for a full thread explaining the work!

ALA 2025

📢 Deadline Extended! 📢

Due to multiple requests and the overlap with @rl-conference.bsky.social and #aaai2025, we’re extending the Adaptive Learning Agent workshop #aamas2025 submission deadline to March 1st (AOE)! 🚀

🔗 More details: ala-workshop.github.io

Pasan's research focuses on reconstructing simpler, interpretable, and efficient models from larger complex models by specifically integrating explainability (recent publications at #NeurIPS2024 #AISTATS2025 #AAMAS2025)

So excited to go !! #AAMAS2025 IS shaping up to be spectacular!

Scalable Offline Reinforcement Learning for Mean Field Games Reinforcement learning algorithms for mean-field games offer a scalable framework for optimizing policies in large populations of interacting agents. Existing methods often depend on online interactio...

Excited to announce that our paper "Scalable Offline Reinforcement Learning for Mean Field Games" has been accepted at #AAMAS2025! 🚀 We propose Off-MMD, an offline RL algorithm for learning equilibrium policies in MFGs from static datasets. arxiv.org/abs/2410.17898