49 citations · 81 across the 8 of their papers we have counts for
8 papers
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
Approximating the Core via Iterative Coalition Sampling
Ian Gemp, Marc Lanctot, Luke Marris +11
The core is a central solution concept in cooperative game theory, defined as the set of feasible allocations or payments such that no subset of agents has incentive to break away…
Reward Design with Language Models
Minae Kwon, Sang Michael Xie, Kalesha Bullard +1
Reward design in reinforcement learning (RL) is challenging since specifying human notions of desired behavior may be difficult via reward functions or require many expert demonstr…
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication
Kalesha Bullard, Douwe Kiela, Franziska Meier +2
Effective communication is an important skill for enabling information exchange in multi-agent settings and emergent communication is now a vibrant field of research, with common s…
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
Kalesha Bullard, Franziska Meier, Douwe Kiela +2
Effective communication is an important skill for enabling information exchange and cooperation in multi-agent settings. Indeed, emergent communication is now a vibrant field of re…
Active Learning within Constrained Environments through Imitation of an Expert Questioner
Kalesha Bullard, Yannick Schroecker, Sonia Chernova
Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives. However, this may be insufficient in more realistic huma…
Proceedings of the AI-HRI Symposium at AAAI-FSS 2018
Kalesha Bullard, Nick DePalma, Richard G. Freedman +6
The goal of the Interactive Learning for Artificial Intelligence (AI) for Human-Robot Interaction (HRI) symposium is to bring together the large community of researchers working on…