EMAI trains masking agents to randomize low-importance agents while preserving reward, and reads agent importance from the learned masking probabilities.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning
EMAI trains masking agents to randomize low-importance agents while preserving reward, and reads agent importance from the learned masking probabilities.