Comment by jveness on Help request: What is the Kolmogorov complexity of computable approximations to AIXI? · 2010-12-18T10:15:21.305Z · LW · GW

Hi.

A couple of comments from the author of mc-aixi(fac-ctw).

There is a massive difference in generalisation ability between Solomonoff Induction and the scaled down Bayes mixture used by mc-aixi(fac-ctw). The mixture used in this approximation scales with more CPU power, however it will never get anywhere near the full power of Solomonoff Induction. The class of environments the agent supports is described in more detail in the paper Louie references.

Let's be clear though - 100 years of Moores Law would significantly improve the performance of even the current implementation of the agent. However it is important to not forget about data efficiency. mc-aixi(fac-ctw) can only exploit very simple structure given reasonable amounts of data. If this structure doesn't exist, the performance will not be much better than learning by rote. Thus Louie's comment about image recognition is spot on. Future versions will be able to generalise more effectively for certain kinds of data, however expect progress to be very incremental.

For what it's worth, the next version of mc-aixi will contain about 3x more lines of source code. It will also be restricted to toy domains, however they will be significantly more interesting than what is currently published.

Happy to answer any other questions.

Cheers, Joel