Probabilistic knowledge and probabilistic common knowledge

By Paul J. Krasucki, R. Parikh, Gilbert Ndjatou. 1991

How researchers see this paper

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

1. Christian Schröder de Witt
2. Wendelin Böhmer
3. Shimon Whiteson
4. Gregory Farquhar
5. Tabish Rashid
6. Philip H. S. Torr
7. Jakob N. Foerster
8. Tonghan Wang

Citations in the corpus

• Multi-Agent Common Knowledge Reinforcement Learning (2018, methodology): “MACKRL outperforms both IL and JAL. Furthermore, we use a noisy variant of the same matrix game to show that MACKRL can exploit a weaker form of group knowledge called probabilistic common knowledge (Krasucki et al., 1991), that is induced by agent beliefs over common knowledge, derived from noisy o”