10 citations · 15 across the 18 of their papers we have counts for
8 papers · 1 filter
Avoiding Obfuscation with Prover-Estimator Debate
Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras +3
Training powerful AI systems to exhibit desired behaviors hinges on the ability to provide accurate human supervision on increasingly complex tasks. A promising approach to this pr…
Cautious Optimism: A Meta-Algorithm for Near-Constant Regret in General Games
Ashkan Soleymani, Georgios Piliouras, Gabriele Farina
We introduce Cautious Optimism, a framework for substantially faster regularized learning in general games. Cautious Optimism, as a variant of Optimism, adaptively controls the lea…
Plasticity as the Mirror of Empowerment
David Abel, Michael Bowling, André Barreto +13
Agents are minimally entities that are influenced by their past observations and act to influence future observations. This latter capacity is captured by empowerment, which has se…
Faster Rates for No-Regret Learning in General Games via Cautious Optimism
Ashkan Soleymani, Georgios Piliouras, Gabriele Farina
We establish the first uncoupled learning algorithm that attains per-player regret in multi-player general-sum games, where is the number of players, …
Multi-Agent Risks from Advanced AI
Lewis Hammond, Alan Chan, Jesse Clifton +41
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These s…
Deviation Ratings: A General, Clone-Invariant Rating Method
Luke Marris, Siqi Liu, Ian Gemp +2
Many real-world multi-agent or multi-task evaluation scenarios can be naturally modelled as normal-form games due to inherent strategic (adversarial, cooperative, and mixed motive)…
Agency Is Frame-Dependent
David Abel, André Barreto, Michael Bowling +13
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determini…
Re-evaluating Open-ended Evaluation of Large Language Models
Siqi Liu, Ian Gemp, Luke Marris +3
Evaluation has traditionally focused on ranking candidates for a specific skill. Modern generalist models, such as Large Language Models (LLMs), decidedly outpace this paradigm. Op…