Allocating training instances to learning agents for team formation

Author:

Liemhetcharat Somchaya,Veloso Manuela

Funder

Air Force Research Laboratory

Office of Naval Research

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference47 articles.

1. Agmon, N., & Stone, P. (2011). Leading multiple ad hoc teammates in joint action settings. In Proceedings of the international workshop on interactive decision theory and game theory (pp. 2–8).

2. Agmon, N., & Stone, P. (2012). Leading ad hoc agents in joint action settings with multiple teammates. In: Proceedings of the international conference on autonomous agents and multiagent systems (pp. 341–348).

3. Albrecht, S. V., & Ramamoorthy, S. (2012). Comparative evaluation of mal algorithms in a diverse set of ad hoc team problems. In Proceedings of the international conference on autonomous agents and multiagent systems (pp. 349–356).

4. Albrecht, S. V., & Ramamoorthy, S. (2013). A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems. In Proceedings of the international conference on autonomous agents and multiagent systems (pp. 1155–1156).

5. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2–3), 235–256.

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