short pdf (AGI-16), tech report pdf (arxiv)
Paper Title
Martin J., Everitt T., Hutter M. (2016) Death and Suicide in Universal Artificial Intelligence. In: Steunebrink B., Wang P., Goertzel B. (eds) Artificial General Intelligence. AGI 2016, AGI 2016. Lecture Notes in Computer Science, vol 9782. Springer, Cham.
Abstract
Reinforcement learning (RL) is a general paradigm for studying intelligent behaviour, with applications ranging from artificial intelligence to psychology and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over semimeasures that need not sum to 1, rather than over proper probability measures. In this work we argue that the shortfall of a semimeasure can naturally be interpreted as the agent’s estimate of the probability of its death. We formally define death for generally intelligent agents like AIXI, and prove a number of related theorems about their behaviour. Notable discoveries include that agent behaviour can change radically under positive linear transformations of the reward signal (from suicidal to dogmatically self-preserving), and that the agent’s posterior belief that it will survive increases over time.