Machine Learning Teaches Me How to Write Better AI | Hacker News Reader

Machine Learning Teaches Me How to Write Better AI

yieldthought.com

24 pointsmoconnor7 comments

Some more details:

* I used the complearn package from http://complearn.org/ - it hasn't been updated in a while and needed a few minor changes to compile on Ubuntu 14; drop me a line if you'd like the patch.

* Similarity clustering with NCD works best when you can visually inspect the items and get some level of understanding as to what they are doing. I recently clustered all of our web logs on a monthly basis and found that, while e.g. summer months were clustered together (around releases and conferences typically) there were a couple of unexpected winter months in there too. The logs were too large to inspect for differences by hand - in this case it is more useful to cluster e.g. individual requests, or summaries tables of each month.

* Wesnoth is a great example of an open source game that continues to grow and develop and the team are friendly and welcoming. I can highly recommend contributing to it!

Could you upload a svg example?

Sure, here's the AI after a change introduced a pretty severe bug:

https://www.dropbox.com/s/lqclj3toywnh8ak/losing.svg?dl=1

In this case looking at the replays for the most similar win/loss runs (15 and 4) and the most severe losses (31+32) made the cause absolutely clear.

Here is the current state of the AI:

https://www.dropbox.com/s/e1n3wevn2gggbic/winning.svg?dl=1

In both the cases in which is loses its opponent gets lucky in combat and has a critical unit level up twice. There was no need for the AI to risk this happening though, so the next improvement might be to make it target units that are gaining XP more aggressively earlier on when winning, or simply avoid combat with units likely to level up and move straight in for the kill.

Of course, there are a wide range of more general improvements that can be made as well - this version of the AI only recruits skeletons, for example!

Similarity clustering with NCD works best when you can visually inspect the items..

Doesn't this apply to all the clustering algorithms ? And this is what we want to avoid for clustering, because it is not easy to visualize in higher dimensions.

It's a particular problem with NCD because you don't always know what the compressor is measuring. At least with the L2 distance on a vector you know that those points were close to each other for a well-defined definition of 'close'. If it is surprising that they are close, you might want to investigate whether your feature selection really makes sense for these items.

With NCD the compressor selects its own features in a largely opaque way. This makes it fun to use but difficult to debug!

Thanks for the description of your workflow and the reflective thinking about your journey to get there. I was just looking at both R and Julia for similarity distances earlier today and thinking about which might work better in an automated analytic application. Your post convinced me to go take a look at complearn.org

What is your sense for how it compares with kNN or hierarchical clustering at the clustering level or with various statistical distance measures like Euclidean, Manhattan etc. at the raw distances level?

NCD as a distance measure can be used for kNN and in this case actually was used for hierarchical clustering in the form of the unrooted binary tree.

As for how NCD compares to euclidian, manhattan distances and so on - it's interesting. One of NCD's strengths is that you can apply it even if your data is not readily representable as a uniformly-long vector of numbers, whereas most other distance measures require this and it's not always convenient to represent input data in that form.

For example, in this application games may be of widely-varying lengths. That doesn't matter when computing the NCD, but I'd have had to pad out the "missing" values from shorter runs to the lengths of longer ones, or truncated long ones, or resized them all with some smoothing.

None of those seemed like particularly good options for this application.

Also, with time series (which this essentially is), most compressors are good at recognizing patterns repeated in items. That means that 0, 0, 4, 7, 9, 4, 0 is very close to 0, 4, 7, 9, 4, 0, 0 under NCD and is comparatively distant under euclidian, manhattan and cosine distances as the elements of each vector are assumed to be independent.

In short, your mileage may vary and will depend on the application. I like NCD because it's super easy to throw it at almost any problem and get a quick understanding of how the data is structured.

The algorithms in complearn are slow. I'm experimenting with much faster ones that scale better at the moment.

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