2016/06/02 by Jarryd Martin, Martin, Jarryd, Tom Everitt +4 · 1 voice · 2 citations
Computer Science · #Computability, Logic, AI Algorithms #Evolutionary Algorithms and Applications #Cellular Automata and Applications
paper · pdf · doi:10.48550/arxiv.1606.00652
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.