2 citations · 2 across the 6 of their papers we have counts for
6 papers
Safe Pareto Improvements for Expected Utility Maximizers in Program Games
Anthony DiGiovanni, Jesse Clifton, Nicolas Macé
Agents in mixed-motive coordination problems such as Chicken may fail to coordinate on a Pareto-efficient outcome. Safe Pareto improvements (SPIs) were originally proposed to mitig…
Towards the Scalable Evaluation of Cooperativeness in Language Models
Alan Chan, Maxime Riché, Jesse Clifton
It is likely that AI systems driven by pre-trained language models (PLMs) will increasingly be used to assist humans in high-stakes interactions with other agents, such as negotiat…
Evolutionary Stability of Other-Regarding Preferences Under Complexity Costs
Anthony DiGiovanni, Nicolas Macé, Jesse Clifton
The evolution of preferences that account for other agents' fitness, or other-regarding preferences, has been modeled with the "indirect approach" to evolutionary game theory. Unde…
Commitment games with conditional information disclosure
Anthony DiGiovanni, Jesse Clifton
The conditional commitment abilities of mutually transparent computer agents have been studied in previous work on commitment games and program equilibrium. This literature has sho…
Normative Disagreement as a Challenge for Cooperative AI
Julian Stastny, Maxime Riché, Alexander Lyzhov +3
Cooperation in settings where agents have both common and conflicting interests (mixed-motive environments) has recently received considerable attention in multi-agent learning. Ho…
Parameterized Exploration
Jesse Clifton, Lili Wu, Eric Laber
We introduce Parameterized Exploration (PE), a simple family of methods for model-based tuning of the exploration schedule in sequential decision problems. Unlike common heuristics…