21–30 of 87 posts
How is this better than applying the minimum description length principle formalization of Occam's razor? (from 1978 [1]) I admit that I didn't spend time to really delve into it but nothing in this article strikes me as particularly ground breaking. [1] http://en.wikipedia.org/wiki/Minimum_description_length edit: innovative --> ground breaking
Or the Minimum Message Length principle (from 1968 [1])
1. Do we have to engineer how much it's being rewarded in each game? 2. What happens in the infinite case? With pacman, there are always a finite number of choices at each state. 3. If it doesn't have to be told the rules, then how does it make decisions? In theory, it may be able to learn how to play jeopardy, but it may be way too inefficient in practice. Humans don't even start with a blank state. 4. I don't think…
Why do you think it is relevant what you do consciously? The only things that you do consciously are those things which your brain is ill-equipped to do. The vast majority of your thinking processes are subconscious, as are the principles that drive your conscious thinking. And I guarantee you, Ockham's razor is in there whether you realize it or not. When things get complicated do you purposefully look for a simpler solution? When trying to understand an unknown situation, do you start with something simple and ad complexity as needed? Ockham's razor.
> I would be ok with it if there was some parallel between ockham's razor and physics.
... there's not?
EDIT: As an AI researcher, I'd be more interested in creating an artificial scientist than "artificial science." So what makes the scientist work? Ockham's razor is at the foundation of that.
Am I the only one that gets a bit of a chill watching an UFAI play a game where it runs from its pursuers collecting resources until it gains the power to kill them?
True, but it doesn't kill them very efficiently.
How is this better than applying the minimum description length principle formalization of Occam's razor? (from 1978 [1]) I admit that I didn't spend time to really delve into it but nothing in this article strikes me as particularly ground breaking. [1] http://en.wikipedia.org/wiki/Minimum_description_length edit: innovative --> ground breaking
It's interesting that, while the system can learn lots of different games, you still have to give it the reward function special for each different game. This may seem obvious, and not much of a limitation- after all, lots of things that we think of as intelligent to varying degrees (different human beings, as well as members of other species) have wildly different ideas of what constitutes "reward", so that can't be…
It's interesting that, while the system can learn lots of different games, you still have to give it the reward function special for each different game. This may seem obvious, and not much of a limitation- after all, lots of things that we think of as intelligent to varying degrees (different human beings, as well as members of other species) have wildly different ideas of what constitutes "reward", so that can't be…
My thoughts are that if they were to take such a direction with this AI, they'd give it the basics and let it evolve and learn its own complicated reward structure. When you're trying to get a monkey to play pac-man, you bribe him with a capful of ribena - he doesn't care about fun intellectual challenges, but sweet liquids motivate the hell out of him.
(This is the state of actual monkey research - ribena is monkey crack)
It's interesting that, while the system can learn lots of different games, you still have to give it the reward function special for each different game. This may seem obvious, and not much of a limitation- after all, lots of things that we think of as intelligent to varying degrees (different human beings, as well as members of other species) have wildly different ideas of what constitutes "reward", so that can't be…
Beware, you need some serious maths and a board to follow him. In the end the book is quite rewarding.
Now to AIXI: I don't belive that having a formula solves AI problem. Yes you can model it, but in real environment, I believe there are simpler models to try first. Remember: nature like simpliness first, complex when needed.
1. Do we have to engineer how much it's being rewarded in each game? 2. What happens in the infinite case? With pacman, there are always a finite number of choices at each state. 3. If it doesn't have to be told the rules, then how does it make decisions? In theory, it may be able to learn how to play jeopardy, but it may be way too inefficient in practice. Humans don't even start with a blank state. 4. I don't think…
A disciple of another sect once came to Drescher as he was eating his morning meal.
“I would like to give you this personality test”, said the outsider, “because I want you to be happy.”
Drescher took the paper that was offered him and put it into the toaster, saying: “I wish the toaster to be happy, too.”
1. Do we have to engineer how much it's being rewarded in each game? 2. What happens in the infinite case? With pacman, there are always a finite number of choices at each state. 3. If it doesn't have to be told the rules, then how does it make decisions? In theory, it may be able to learn how to play jeopardy, but it may be way too inefficient in practice. Humans don't even start with a blank state. 4. I don't think…
http://www.cs.mcgill.ca/~jpineau/files/whamilton-icml13.pdf
http://www.cs.mcgill.ca/~whamil3/files/cpsr-talk.pdf
Also, don't forget that this is a partially observable domain (with only local sensory information) with no apriori knowledge of the overall goal to achieve. It learns completely from scratch ! If we find this task easy as human beings, it's because we have a lot of prior knowledge that we can transfer into this task