100 citations · 251 across the 21 of their papers we have counts for
5 papers · 1 filter
TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI
Andrew Critch, Stuart Russell
While several recent works have identified societal-scale and extinction-level risks to humanity arising from artificial intelligence, few have attempted an {\em exhaustive taxonom…
Optimal Policies Tend to Seek Power
Alexander Matt Turner, Logan Smith, Rohin Shah +2
Some researchers speculate that intelligent reinforcement learning (RL) agents would be incentivized to seek resources and power in pursuit of their objectives. Other researchers p…
Servant of Many Masters: Shifting priorities in Pareto-optimal sequential decision-making
Andrew Critch, Stuart Russell
It is often argued that an agent making decisions on behalf of two or more principals who have different utility functions should adopt a {\em Pareto-optimal} policy, i.e., a polic…
Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making
Andrew Critch
Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players wit…
Logical Induction
Scott Garrabrant, Tsvi Benson-Tilsen, Andrew Critch +2
We present a computable algorithm that assigns probabilities to every logical statement in a given formal language, and refines those probabilities over time. For instance, if the…