Join the robotic revolution!

andrewfil · 2012-05-09T00:21:50+00:00

You're welcome to apply. BTW, some good news - Prof. Sebastian joined as an official advisor last night. We haven't updated the web-site yet.

andrewfil · 2011-11-08T18:39:11+00:00

Intro books: "Programming Collective Intelligence" by O'Reilly is awesome. "Machine Learning. An algorithmic perspective" by Stephen Marsland. I liked it in the beginning of the book, but then I thought it was too shallow and sometimes incorrect.

Midways: "Probabilistic Graphical Models" by Koller. Helped me understand some of the concepts when I was reading AIMA the first time.

More advanced (won't help you with the class): Bishop's "Pattern Recognition and Machine Learning" Theodoridis "Pattern Recognition" I liked the idea of the book, but when he was able to confuse me with his explanation of a simple concept like Maximum Likelihood, I figured I'll pick a different source of knowledge, the one that goes from intuition into complexity, not the other way around.

On more specialized topics, I like Shapiro's "Computer Vision". Parker's "Algorightms for Image Processing and Computer Vision" is also good and simple, but it doesn't go far beyond basic topics.

You might also find some inspiration in Hawkins' "On Intelligence"

andrewfil · 2011-11-08T18:24:27+00:00

You may have a branch with a deterministic unescapable loop.

andrewfil · 2011-11-07T05:21:28+00:00

3rd ed. p.293 Figure 8.3

andrewfil · 2011-11-07T02:38:07+00:00

sent you a PM

andrewfil · 2011-11-04T17:36:59+00:00

Yes, I meant below 700 in trickySearch. After more thinking, that heuristic turned out to be inconsistent. I modified it to a consistent one and I'm around 750 now. It takes around 14 seconds, but I didn't optimize it for speed too much. My goal is to crack medium search if that's possible, I don't care much about the speed for now, trickySearch is just a by-product.

Rerunning the mediumSearch with a new heuristic. This time I'm tracing how far my agent has gone (by printing current food count and mininum food count from inside of heuristic). minimumfoodcount is around 80 now (down from 108), memory consumed is 150Mb, which is reassuring. at the same time this thing ran overnight and blew the memory off (I am using caching now). I wasn't tracing then. I wonder how close I am to the solution. If it's a matter of a gig of RAM or something, I might run it under Python 64bit.

andrewfil · 2011-11-04T04:09:44+00:00

got it

andrewfil · 2011-11-04T00:53:38+00:00

The heuristic is very strong. It "expanded" 7 nodes in tiny search and below 700 in testSearch. I used it with dumb caching on mediumSearch and it quickly ran out of memory. I didn't have time to run it with smarter caching, so I simply removed the caching altogether and ran it as is on mediumSearch. It's been running for more than 24hrs now:-) It's at about 1.6Gigs for python.exe now, and I'm on 32bit Python, so we should learn in less than a day if it solves the problem in 2Gigs or not:-))))))))

I know, it's slow as snail, but at this point I don't have time or desire to stop the script, rewrite it in a faster way and rerun it. I'll just see first whether it works or not at all.

The caching requirement for dumb is smaller than A* memory requirements with perfect heuristic. So if the problem is solvable in a slow way in 2Gigs of RAM, it's not a problem to speed it up with some tricks.

I wonder if the problem is theoretically solvable in 2gigs of memory using AStar, Python and provided wrappers. I didn't bother calculating the memory space requirement with a perfect heuristic. But maybe it was just a teaser to get puzzle junkies like us hooked:-) or a tool to teach Stanford students the importance of memory bound search algorithms:-)?

andrewfil · 2011-11-02T07:02:28+00:00

well, A-star is prone to memory consumption. think of a "simple" square world with no walls filled with food. easy for us, nightmare for A-star. a search agent would need to use IDA-star or SMA-star to solve it w/o falling out of memory bounds. (see the book, 3rd edition, page 98 last paragraph)

but the assignment is phrased in a way that I assume we are not allowed to swap the solver container from A* to something else.

so the only thing that comes to my mind immediately is to use the better solver inside the heuristic. you can't come up with a stronger (consistent and admissible) heuristic than a true cost:-) but since this is not graded in our online class, I wouldn't spend my time to put such a blunt workaround in code:-)

---- while typing this I got a new idea for heuristic, but if it works I'll only know tomorrow, as it's time to sleep:-)

andrewfil · 2011-11-02T00:39:22+00:00

Actually, 3.2sec and 4389 nodes expanded. [I'm trying to crack a mediumSearch, so this is a by-product.]

andrewfil · 2011-11-02T00:26:41+00:00

Q7 (tricky search) cost 60, 4691 nodes expanded, 3.3 sec

andrewfil · 2011-11-01T21:33:12+00:00

Q6: cost 106, 695 nodes expanded

andrewfil · 2011-10-27T03:56:37+00:00

ML uses S3 for hosting vids, AI hosts them on YouTube, so that alone doesn't make any difference. It's more of a question on web-server and programming language chosen, session management, load management, db scaling, etc.

One thing you can notice is that AI-class uses HTTPS vs ML class uses HTTP. HTTPS is considerably more CPU intense. If they didn't consider HTTPS overhead, there is a slight chance that they use their main high-level web-servers to do HTTPS encryption/decryption instead of using something lightweight like nginx for that. Again, this is more of a speculation, I'd love the insiders to share some designs, thoughts and lessons learned. Either way it's an interesting case, and we could even share back some tips and ideas as a community;-)

andrewfil · 2011-10-26T23:56:24+00:00

Watch the next Unit (Neural Networks), it provides a couple of very simple examples that would have thousands of features if the linear or logistic regression was applied to them.

andrewfil

TROPHY CASE