Learning Independently from Causality in Multi-Agent Environments

Motivated by the indications of how causality can be so successfully linked to machine learning, the applications The use of causality in the field of Artificial Intelligence have been studied in different fields. In (AI) has been gaining the attention of the research neurology, causality has been used to find causal relations community. Recent discussions argue how among different regions of the brain (Glymour causality can play an important role to improve many et al., 2019). This can be important to understand the traditional machine learning approaches (Peters et al., reason of certain events lighted by different parts of 2017). More specifically, recent works argue that our brains. Besides the relevance in the healthcare causality can be used to get a deeper understanding of field, in agriculture it can be critical to understand the underlying properties of systems within the field what is causing the harvesting to be less fruitful in one of AI. While it can be relatively straight forward to year than in the previous, and not only to see what is learn the underlying distributions of a given system, to correlated to this event (Sgaier et al., 2020).