2 citations · 3 across the 2 of their papers we have counts for
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cs.AI2023
Towards a Better Understanding of Learning with Multiagent Teams
David Radke, Kate Larson, Tim Brecht +1
While it has long been recognized that a team of individual learning agents can be greater than the sum of its parts, recent work has shown that larger teams are not necessarily mo…
cs.AI2023★ 1 cited
Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams
David Radke, Kyle Tilbury
Mixed incentives among a population with multiagent teams has been shown to have advantages over a fully cooperative system; however, discovering the best mixture of incentives or…
cs.AI2023★ 2 cited
Presenting Multiagent Challenges in Team Sports Analytics
David Radke, Alexi Orchard
This paper draws correlations between several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We speci…