3 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Exploring the Benefits of Teams in Multiagent Learning
David Radke, Kate Larson, Tim Brecht
For problems requiring cooperation, many multiagent systems implement solutions among either individual agents or across an entire population towards a common goal. Multiagent team…
The Importance of Credo in Multiagent Learning
David Radke, Kate Larson, Tim Brecht
We propose a model for multi-objective optimization, a credo, for agents in a system that are configured into multiple groups (i.e., teams). Our model of credo regulates how agents…