How can agents learn to cooperate in a one-shot game?
Grounded Predictions of Teamwork as a One-Shot Game: A Multiagent Multi-Armed Bandits Approach
September 27, 2024
https://arxiv.org/pdf/2409.17214This paper investigates how to predict the performance of teams where members are self-interested and not forced to cooperate, using a game-theoretic model called "teamwork games" and a multi-agent learning system based on multi-armed bandits. The research analyzes how factors like task type (additive, conjunctive, disjunctive), individual skill levels, and evaluation difficulty impact both individual contributions and overall team productivity.
Key findings relevant to LLM-based multi-agent systems include:
- Modeling teamwork as a non-cooperative game with strategic agents can provide valuable insights into real-world team dynamics where full cooperation isn't guaranteed.
- Different task structures require different team compositions and evaluation schemes for optimal performance.
- Multi-armed bandit learning can allow agents to learn effective strategies over time even in complex, mixed-motive scenarios, offering a potential approach for training LLM agents in collaborative settings.