Empathic DQN augments DQN value estimates with an empathy term computed by swapping the learning agent into other agents' situations, reducing collateral harms in two gridworld proof-of-concept environments.
The surprising creativity of digital evolution: A col- lection of anecdotes from the evolutionary computation and artificial life research communities
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes a classification schema for AI failures drawn from historical cases to improve incident response and guide risk assessment in development.
citing papers explorer
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Towards Empathic Deep Q-Learning
Empathic DQN augments DQN value estimates with an empathy term computed by swapping the learning agent into other agents' situations, reducing collateral harms in two gridworld proof-of-concept environments.
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Classification Schemas for Artificial Intelligence Failures
Proposes a classification schema for AI failures drawn from historical cases to improve incident response and guide risk assessment in development.