Opponent Modeling by Expectation–Maximization and Sequence Prediction in Simplified Poker

By Richard Mealing, Jonathan L. Shapiro. 2017

27 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Clinical Psychology, Economics and Econometrics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shimon Whiteson
2. Stefano V. Albrecht
3. Sergey Levine
4. Pieter Abbeel
5. Jakob N. Foerster
6. Igor Mordatch
7. Filippos Christianos
8. Peter Stone

In Clinical Psychology

1. Peter Stone
2. Jakob N. Foerster
3. Stefano V. Albrecht

In Economics and Econometrics

1. Shimon Whiteson
2. Peter Stone
3. Jakob N. Foerster
4. Pieter Abbeel
5. Stefano V. Albrecht
6. Igor Mordatch
7. J. Urgen Schmidhuber
8. Adam Lerer

Citations in the corpus

• Learning with Opponent-Learning Awareness (2017, methodology): “Our work also relates to opponent modelling, such as fictitious play [5] and action-sequence prediction [29, 36].”
• Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, methodology): “For example, Southey et al. (2005) maintain Gaussian beliefs over the continuous parameters of a specified player function for Poker (cf. Table 1 in their paper).”