Universal Empathy and Ethical Bias for Artificial General Intelligence
  Rational agents are usually built to maximize rewards. However, AGI agents
can find undesirable ways of maximizing any prior reward function. Therefore
value learning is crucial for safe AGI. We assume that generalized states of
the world are valuable - not rewards themselves, and propose an extension of
AIXI, in which rewards are used only to bootstrap hierarchical value learning.
The modified AIXI agent is considered in the multi-agent environment, where
other agents can be either humans or other "mature" agents, which values should
be revealed and adopted by the "infant" AGI agent. General framework for
designing such empathic agent with ethical bias is proposed also as an
extension of the universal intelligence model. Moreover, we perform experiments
in the simple Markov environment, which demonstrate feasibility of our approach
to value learning in safe AGI.
