One landmark application of evolutionary game theory is the study of social dilemmas. This
literature explores why people cooperate even when there are strong incentives to defect …

Social behavior in human and animal populations can be studied as an evolutionary
process. Individuals often make decisions between different strategies, and those strategies …

Games are an increasingly useful tool for training and testing learning algorithms. Recent
examples include GANs, AlphaZero and the AlphaStar league. However, multi-agent …

Coal mine safety management involves many interested parties and there are complex
relationships between them. According to game theory, a multiparty evolutionary game …

Arguably one of the thorniest problems in game theory is that of equilibrium selection.
Specifically, in the presence of multiple equilibria do self-interested learning dynamics …

What does it mean to fully understand the behavior of a network of adaptive agents? The
golden standard typically is the behavior of learning dynamics in potential games, where …

Cooperation in settings where agents have both common and conflicting interests (mixed-
motive environments) has recently received considerable attention in multi-agent learning …

Gradient-based optimization has been critical to the success of machine learning, updating
a single set of parameters to minimize a single loss. A growing number of applications rely …

Ridge Rider (RR) is an algorithm for finding diverse solutions to optimization problems by
following eigenvectors of the Hessian (" ridges"). RR is designed for conservative gradient …

Uncontrolled intersections, as a typical urban traffic environment, are challenging scenarios
for autonomous driving due to the potential conflicts and lack of co-ordination between traffic …