Michael Timothy Bennett
Australian National University, Australian National University, Australian National University, Australian National University, Australian National University, Australian National University, Australian National University
Corresponding Author:[email protected]
Author ProfileAbstract
An artificial general intelligence (AGI), by one definition, is an agent
that requires less information than any other to make an accurate
prediction. It is arguable that the general reinforcement learning agent
AIXI not only met this definition, but was the only mathematical
formalism to do so. Though a significant result, AIXI was incomputable
and its performance subjective. This paper proposes an alternative
formalism of AGI which overcomes both problems. Formal proof of its
performance is given, along with a simple implementation and
experimental results that support these claims.