Marcus Hutter

So a dollar today is about worth as much as $1.05 tomorrow.

So if you do this so-called geometric discounting, you have introduced an effective horizon.

So the agent is now motivated to look ahead a certain amount of time effectively.

It's like a moving horizon.

And for any fixed effective horizon, there is a problem to solve which requires larger horizons.

So if I look ahead five time steps, I'm a terrible chess player.

I need to look ahead longer.

If I play Go, I probably have to look ahead even longer.

So for every horizon, there is a problem which this horizon cannot solve.

Yes.

But I introduced the so-called near harmonic horizon, which goes down with one over T rather than exponentially T, which produces an agent which effectively looks into the future proportional to each age.

So if it's five years old, it plans for five years.

If it's 100 years old, it then plans for 100 years.

Interesting.

And it's a little bit similar to humans too, right?

I mean, children don't plan ahead very long, but then we get adult, we play ahead more longer.

Maybe when we get very old, I mean, we know that we don't live forever.

Maybe then our horizon shrinks again.

With the long-moment reduction sort of prediction part, we have extremely strong finite time, or finite data results.

So you have so and so much data, then you lose so and so much.