arXiv AI

Evolutionarily Stable Stackelberg Equilibrium

arXiv:2603. 18385v3 Announce Type: replace-cross Abstract: We present a new solution concept called evolutionarily stable Stackelberg equilibrium (SESS).

arXiv Machine Learning
2d ago

What preferences can - and cannot - predict in multi-agent online learning

arXiv:2608. 13810v1 Announce Type: cross Abstract: We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinatorial data of a game -- its preference graph -- determine the outcomes of no-regret learning dynamics -- such as follow-the-regularized-leader (FTRL).

By Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos
arXiv Machine Learning
Jun 30

Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting

arXiv:2409. 05980v2 Announce Type: replace-cross Abstract: Rested and Restless Bandits are two well-known bandit settings that are useful to model real-world sequential decision-making problems in which the expected reward of an arm evolves over time due to the actions we perform or due to the nature.

By Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli
arXiv AI
Jul 7

Serious Games: Human-AI Interaction, Evolution, and Coevolution

arXiv:2505. 16388v2 Announce Type: replace Abstract: The serious games between humans and AI have only just begun.

By Nandini Doreswamy (Southern Cross University, Lismore, New South Wales, Australia, National Coalition of Independent Scholars), Louise Horstmanshof (Southern Cross University, Lismore, New South Wales, Australia)