Archive for AI

Memory-efficient pathfinding

Third time’s a charm they say, and as I’ve talked about pathfinding twice before (in Don’t follow the shortest path! and Aiding pathfinding with cellular automata) I thought I’d charm everyone with a third pathfinding article. This time I’ll talk about how we can reduce the memory consumption on a commonly used pathfinding algorithm by about a magnitude! Read the rest of this entry »

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Posts and links you should have read

I thought I’d post another summary of some (mostly) recent blog posts and links that are worthy of a read, in case you didn’t read them already. Here goes… Read the rest of this entry »

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Aiding pathfinding with cellular automata

Anyone who has ever experimented with Conway‘s Game of Life or any other cellular automata (CA) know they can be very fun to play with. You can easily lose several hours in e.g. George Maydwell’s awesome Modern CA site; his CA evolution lab is particularly cool. If you like to write your own CA code, there are some good efficiency hints on Tim Tyler’s CA page. (As a side note, Tim also has a good — but NSFW, due to boobie pic — rant on why the entertainment industry should be destroyed.) Read the rest of this entry »

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Don’t follow the shortest path!

The A* algorithm is perhaps the most ubiquitous algorithm in games but also seemingly one of the more misunderstood algorithms. Not in the sense that people don’t know how to implement it (they do) but in failing to use it properly. Read the rest of this entry »

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Learning patterns and the game of Flip

A foolproof way of separating the men from the boys, the women from the girls, or perhaps just an old programmer from a not quite so old programmer, is to mention the books Basic Computer Games and More Basic Computer Games and see if it results in frowning never-heard-of-them faces or strange dreamy looks of people recollecting an era long gone by. As their titles imply, these books were collections of games listings, all written in BASIC. (And originally published in the equally famous Creative Computing magazine, now defunct, R.I.P.) Read the rest of this entry »

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Reading comprehension skills are down

I see in my incoming blog links that someone linked my most recent post with the following comment: Read the rest of this entry »

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Sorry state of academic crack-smoking

When writing my earlier blog entry on how you can get complex behaviors from very little and why it’s okay to fake AI (and anything else), I came across this slightly insulting comment about game AI vs. academic AI research that I just have to share (emphasis added): Read the rest of this entry »

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Appear smarter by moving in the right circles (or vector fields)

Simon’s ant

You don’t have to be smart in order to appear smart. An illustration of the previous is the now famous ant-on-the-beach parable as presented by well-known AI pioneer Herbert Simon in his book The Sciences of the Artificial. As an illustration of how complex behavior can arise from simple rules applied to nonsimple data, Simon asks us to consider the path taken by an ant on a sandy beach. Simon writes: Read the rest of this entry »

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Formulas that play { chess, checkers, whatever } optimally

It was reported today that Jonathan Schaeffer and his Games Group team at University of Alberta have solved checkers. Assuming perfect play, the game is a draw. (The result is actually a few months old, but American news media were busy reporting about freeway chases and other important events and didn’t notice until the result was published in Science.) Additional information is available from the University of Alberta website. Read the rest of this entry »

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