artificial-intelligence

I've implemented a minimax algorithm with alpha-beta pruning. In order to get the best move, I call the alpha-beta algorithm with the rootAlphaBeta function. However, in the rootAlphaBeta function, I spotted some very strange behaviour. When I call the rootAlphaBeta function with a ply of 4, it makes about 20 000 calls, but when I call the alphaBeta function directly, it makes only about 2000 calls. I can't seem to find what's the problem, as the number of calls should be the same. The move that both algorithms eventually find should be the same, right? I think so, at least the score of the

I am crawling news websites and want to extract News Title, News Abstract (First Paragraph), etc I plugged into the webkit parser code to easily navigate webpage as a tree. To eliminate navigation and other non news content I take the text version of the article (minus the html tags, webkit provides api for the same). Then I run the diff algorithm comparing various article's text from same website this results in similar text being eliminated. This gives me content minus the common navigation content etc. Despite the above approach I am still getting quite some junk in my final text. This

I'm using redis on an AI project. The idea is to have multiple environment simulators running policies on a lot of cpu cores. The simulators write experience (a list of state/action/reward tuples) to a redis server (replay buffer). Then a training process reads the experience as a dataset to generate a new policy. New policy is deployed to the simulators, data from previous run is deleted, and the process continues. The bulk of the experience is captured in the "state". Which is normally represented as a large numpy array of dimension say, 80 x 80. The simulators generate these as fast as the

I'm trying to implement application that can determine meaning of sentence, by dividing it to smaller pieces. So I need to know what words are subject, object etc. so that my program can know how to handle this sentence. This is an open research problem. You can get an overview on Wikipedia, http://en.wikipedia.org/wiki/Natural_language_processing . Consider phrases like "Time flies like an arrow, fruit flies like a banana" - unambiguously classifying words is not easy. You should look at the Natural Language Toolkit , which is for exactly this sort of thing. See this section of the manual: