113 citations · 399 across the 53 of their papers we have counts for
7 papers · 1 filter
Scaling Small Agents Through Strategy Auctions
Lisa Alazraki, William F. Shen, Yoram Bachrach +1
Small language models are increasingly viewed as a promising, cost-effective approach to agentic AI, with proponents claiming they are sufficiently capable for agentic workflows. H…
Soft Condorcet Optimization for Ranking of General Agents
Marc Lanctot, Kate Larson, Michael Kaisers +7
Driving progress of AI models and agents requires comparing their performance on standardized benchmarks; for general agents, individual performances must be aggregated across a po…
Crowd IQ -- Aggregating Opinions to Boost Performance
Michal Kosinski, Yoram Bachrach, Thore Graepel +2
We show how the quality of decisions based on the aggregated opinions of the crowd can be conveniently studied using a sample of individual responses to a standard IQ questionnaire…
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…
D3C: Reducing the Price of Anarchy in Multi-Agent Learning
Ian Gemp, Kevin R. McKee, Richard Everett +4
In multiagent systems, the complex interaction of fixed incentives can lead agents to outcomes that are poor (inefficient) not only for the group, but also for each individual. Pri…
Biases for Emergent Communication in Multi-agent Reinforcement Learning
Tom Eccles, Yoram Bachrach, Guy Lever +2
We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks. In temporally extended…