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Using genetic algorithms to find Starcraft 2 build orders

UPDATE: Just to make this explicitly clear: I did not write this program. The fourth sentence makes this fairly clear, but some comments indicate that some people are a bit lazy . I also added a video of the rush to the bottom.

I’ve been on a bit of starcraft kick recently (see my replay aggregator: replayspider). I also work in computer vision, and general AI has always been an interest (see this post on “evolving” faces). Sometimes strange interests collide. Over on a forum called teamliquid, a user by the name of Lomilar posted a fairly long thread about a program he had written that optimized build orders for the zerg race in starcraft. He eventually cleaned up his code and posted the code to googlecode. The program is called EvolutionChamber (a clever name, as it’s the name of one of the buildings in the game), and it uses genetic algorithms to find build orders.

This I had to see.

A quick Starcraft 2 primer: setting up the rules

Since this is primarily a programming blog, I’m going to assume you know a fair bit about video games. Feel free to skip this section if you know anything about starcraft 2, in particular. Essentially, SC2 is a real-time strategy game that starts you with a simple base, some workers, and some resources, and asks you to put those workers to work collecting resources that you can use to build things (including other workers), eventually building up an army and killing your opponent. A build order refers to the exact opening steps you take early in the game that best supports the strategy you are trying to conduct. These early games are all about balancing spending money on your economic foundation (making more workers), making units (rush!), or building new buildings or getting new upgrades (tech!).

Build orders generally only cover the very early game because once you’ve scouted the enemy, you have to begin to react to what he’s doing and modifying it as you go. In other words, and a perfect aphorism, no battle plan survives first contact with the enemy. In this way, build orders in real-time strategy are very much akin to openings in chess. They set up the soul of the entire game about to be played, and some players prefer to force certain types of games, depending on what kind of opening they choose to do.

One of the reasons build-order optimization is so important is that you can discover openings that “hard-counter” other openings. If I can get an army of N size into your base when you do opening X, you will always lose.

Enough with the abstract, here’s what you need to know:

The program in question optimizes Zerg build orders (which is one race in starcraft), this is a rather significant choice because the mechanics of the zerg race are arguably the most difficult to manage (esp. for build order optimization).
Of most interest are “rush” build orders. This means “how quickly can I get N of this type of unit?”.
There are two primary resources that workers collect in starcraft: gas and minerals.
Zerg also have a third de factor resource: larva. Larva are used to create ALL zerg units, including workers. So long as you have less than three, they regenerate at a fixed rate (note: this means any time spent at three larva delays all future larva production — very bad).
Most units require some building to be constructed in order to be “unlocked” (and many of these buildings require others as prerequisites – this is the so-called tech tree)
Creating a building causes you to lose the worker who creates it (so the longer you can wait, the more resources that worker can collect before building the building)

These “rules” provide for an extremely complicated search space to find optimal build orders. Essentially, you want to make the exact number of workers you need as quickly as possible (and then no more). Losing a worker when you make a building, and delaying all future larva when at three larva make the dynamics extremely complicated.

How EvolutionChamber works

At its core, the program is a genetic algorithm. For those of you who don’t know, a genetic algorithm is a type of optimization algorithm that tries to find optimal solutions using a method analogous to biologic evolution (to be specific: descent with modification & natural selection). Put simply, you take a “population” of initial build orders, evaluate them for fitness, and modify the population according to each element’s fitness. In other words, have the most successful reproduce.

The program’s input is simply the desired game state. In practice, this means “make N units” to determine some rush build order (but it also allows for other types of builds, like make N workers with some defensive structures and a small army). Here are some of the highlights:

It’s written in Java using JGAP.
A ‘chromosome’, in this case, is an array of ‘actions’ that can be done in game. (e.g, 1) Build a drone. 2) Build a drone. 3) Build a spawning pool. 4) Build an overlord. And so on.)
Invalid actions (ie, trying to build a unit you cannot build because you do not have the tech necessary) are ignored (this allows for “junk dna”).
An action that can’t be done YET (not enough minerals!) causes the simulation to wait until it can be done.
It uses some fairly standard mutation types (deletion, insertion, and one strange one called “overlording” — heh)
It uses the “many villages” approach where there are several separate populations evolving independently.
Populations that are deemed to be stagnant are annihilated and replaced by a variant of the most successful.
The fitness function is really a measure of distance from the “desired” state and the current state (this is measured by the difference in resources required to get there), taking into account the time required (less time is always better).

The 7-roach rush

For the starcraft nerds among you, here’s one of the very first builds constructed by the program:

10 extractor-trick to 11
11 overlord
11 spawning pool
15 extractor
16 queen (stop drones here)
18 overlord
18 roach warren
17 overlord (yes, two)
spawn-larva on queen when she pops
roach x7

This is a fairly fascinating build order in a number of respects.

First, from a starcraft perspective: it is incredibly strong. To be clear, I am certain virtually anyone who practiced this build and went onto the ladder and used it in every game would very easily rise to diamond level (currently the highest league). The seven roaches at that time in the game will destroy all but the most well-executed counter-builds. It caused an almost immediete stir on the starcraft forums, and had one player proclaiming that an all-in variant (“all-in” coming from poker and to mean that you win or lose right now, in this case it means attacking with all your workers as well as your army) was completely unstoppable in a certain matchup.

Second, as far as I know, this build was “discovered” by the program (or at least, it’s never been well known). There is a similar build that’s been well known called the 5-roach-rush (the 5-roach-rush comes later, and is a bit more economical). When comparing the two, the 5RR has certain situational advantages, but the 7RR build, above, has two staggeringly obvious advantages: 1) you get two more roaches, 2) you get them almost 45 seconds sooner. I’m not 100% certain this build was “discovered” by the program, but I do know it’s not been extremely popular or considered standard play so my guess is that it’s not been studied in too much detail.

The most interesting part of this build, however, is how counter-intuitive it is. It violates several well-known (and well-adhered-to) heuristics used by Starcraft players when creating builds. Some of this may be lost on you non-starcraft players, but I’ll do my best to explain.

Extractor trick.

The extractor trick is using a drone to build an extractor (remember this removes the drone), then build a replacement drone, then cancel the extractor, giving you the original drone back — this allows you to build one more worker than your supply would allow. The extractor trick, as used above, has been tested and seen to be economically inferior to a more standard play of buying an overlord on 9 supply. The extractor trick + very early spawning pool do some economic damage and induce a small larva delay, so they are almost never seen. In this case, however, the extractor trick + early pool end up speeding up the entire tech path (this is the primary reason why the 7RR produces roaches much sooner than the 5RR).

Double Overlord at 18/17.

First, a quick discussion about supply. Each unit you create costs supply. So long as you have supply, you can make units. Most RTS games have a similar concept. Overlords, for the zerg, are the unit that provides this supply. So for zerg, you have to spend 100 minerals to unlock additional supply. In general, it is considered “optimal” for you to have just enough supply to not be supply-blocked. It’s generally considered wasteful to buy supply when you don’t need it (since you could spend that money, instead, on units or workers now, and buy the supply later, when you need it).

In almost all cases, it would be extremely wasteful to purchase supply twice in a row. I’m not sure any starcraft player would look at such a build and consider that a good idea. In this case though, it ends up working out so perfectly that you’d actually have to try it any other way to understand how and why. Because your desired goal is 7 roaches, you will need to construct 2 more overlords at some point, but doing them both so early is certainly surprising. During that particular period of the build (around 18 supply), you end up waiting for your roach warren to finish so you can begin creating roaches. This causes you to max out on larva, stopping regeneration. By moving the second overlord so far up into the build, this larva ends up being “free” — you’d lose it anyway because the regeneration would stop. So the only penalty to making the second overlord early is minerals, and during this portion of the build, you are not mineral-bound. The net result is that moving that overlord so far up into the build costs nothing and frees up larva regeneration to produce quicker roaches.

This is the type of non-obvious optimization that genetic algorithms excel at.

As for the 7-roach-rush, I’m certain if you are playing starcraft2, you’ll see this build quite a bit. As for whatever hidden and game-breaking builds remain undiscovered, that remains to be seen.

UPDATE:

Here’s a video of someone doing the ‘all-in’ variant of this build against a real player:

Thanks to rockpapershotgun.com for the writeup and pointing me to this video.

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193 Responses to “Using genetic algorithms to find Starcraft 2 build orders”

November 1st, 2010 at 9:42 am
Total Casual says:

I can’t wait until there’s versions of this program for every race-what other “convential wisdom” of the Starcraft community is defective?

Do you know at what minute/second the 7th roach hatches?
November 1st, 2010 at 9:45 am
louis says:

I think optimally done the 7th roach pops out around 4:40.
November 1st, 2010 at 11:06 am
Mike says:

Wow, I may have to get starcraft going to try it out. I was a victim of the success of the 5RR after the recent patch. I was able to get the 5 roaches out around 4:55 absolute minimum and 5:00 is a more reasonable time.

The only problem with this rush is that the point of the 5RR is to have roaches break the wall and speedlings killing workers, marines, stalkers and sentries. Still very cool from a programmers standpoint and could help blizzard figure out the best openings possible and balance around them. I would genetic algorithms and I like the idea.
November 1st, 2010 at 11:56 am
kflynn says:

can you go into more detail about your fitness function?
November 1st, 2010 at 12:17 pm
Mike Shaver says:

The fitness functions are in the source tree linked early in the article: http://code.google.com/p/evolutionchamber/source/browse/trunk/EvolutionChamber/src/java/com/fray/evo/fitness/EcStandardFitness.java and so forth.
November 1st, 2010 at 1:17 pm
Farid Zakaria says:

You make me want to go to grad school and study this when I’m finished with my Software Engineering degree.

Amazing.
November 1st, 2010 at 1:20 pm
ogrisel says:

Only the twisted distributed mind of a Zerg player could come up with such an messy evolutionary approach to optimal build order discovery.

Pure Protos players would never commit such an heresy and would stick to intelligently designed build orders.

BTW: have you thought about making the fitness function handle several objectives/heuristics explicitly (army size, firepower, defense strength, economy, map control, …) with adjustable weights from one village to another? That would allow you to build the Pareto optimal front [1] of good strategies. Google for NSGA-II and SPEA2 and island model variants.

[1] http://en.wikipedia.org/wiki/Pareto_efficiency#Pareto_frontier

Good job! Long live the Swarm!
November 1st, 2010 at 2:17 pm
john says:

This is very interesting. I would like to see this same program run for Terran. This Zerg rush would be an absolute fail against a Terran with a 111 build because it takes about the same time to produce 2 banshees and the Zerg would have no anti-air. Even if the Terran didn’t kill the roaches and went for a base race, the Terran would have 3-4 banshees by the time Zerg broke the wall, more structure HP, and the DPS would be pretty close or better.
November 1st, 2010 at 2:31 pm
dz says:

The 1-1-1 build is fairly easy to scout, and the fast banshee is doubly so. Naturally no build order is strong against everything, but this 7 roach variation is realtively strong against a pretty impressive number of openings.
November 1st, 2010 at 2:59 pm
ogrisel says:

Only the twisted distributed mind of a Zerg player could come up with such a messy evolutionary approach to optimal build order discovery.

Pure Protos players would never commit such an heresy and would stick to “intelligently designed” build orders.

BTW: have you thought about making the fitness function handle several objectives/heuristics explicitly (army size, firepower, defense strength, economy, map control, …) with adjustable weights from one village to another? That would allow you to build the Pareto optimal front [1] of good strategies. Google for NSGA-II and SPEA2 and island model variants.

[1] http://en.wikipedia.org/wiki/Pareto_efficiency#Pareto_frontier

Good job! Long live the Swarm!
November 1st, 2010 at 3:08 pm
Jon says:

I found an iPhone app that does the same thing and works for all 3 races –
http://itunes.apple.com/us/app/build-calculator-for-starcraft/id381214248?mt=8
November 1st, 2010 at 3:19 pm
Constantin says:

Awesome program, you could easily publish a paper out of this. I was wondering, how long did it take to come upon the first “coherent” strategy, because obviously that is not the actual first strategy discovered. I was also wondering, how did you test the strategies, how did you know the utility of each strategy generated. Thanks.
November 1st, 2010 at 3:31 pm
Ben says:

You left out the most interesting part! What is your breeding function? I don’t see an obvious way to breed two solutions other than swapping elements of the chromosome. Since this doesn’t really take advantage of any structure, I wonder if a reinitializing (or parallel) local search procedure would work just as well, where a neighbor is defined by changing one element of the chromosome.
November 1st, 2010 at 3:48 pm
witek says:

Good works. Remeind me my efforts (about 4-5 years ago) to optimize build order in AstroWars web-based MMORPG. Similar some results was counter-intuitive.

For example, when optimizing for speed of spaceships, one needs very high level of science. But optimial way to achive was to use rase with no science bonus, and no science units until half of the game!

Similary the same with population growth (it is limited by number of culture, which grows proportional to the number of culture buildings). Again, to have highest culture was to not build any culture building until some point in game. It was very counter-intuitive, and totally different than everyone was using in the game.

This strategy (and of course any other results from optimization), leads me and few other pepople to high ranks in final ranking. Similary our whole team once had top10, and once win whole game

My approach was based on evolutionary algorithms, but when i was coding it for astrowars, i have no idea about evolutionary algorithms – I just know that they are based on ideas from evolution. So without reading any articles or textbooks i started experimenting on my own Leading to good scheme. I used order list based representation with few usefull mutations: insertion, deletion, and crossover of lists (swaping tails) beetween two chromosones. I used quadratic bias towards better fited chromosones. There were also few other tricks releated to game mechanics (like the fact that order launching colonisation ships can be done before reaching their destination, but they need to land after the culture switched to next level for example).

Today I know that my algorithm wasn’t pure genetic algorithm (they use only pure binary representation in black box mode). It was in fact genetic program with good and sufficiently well designed representation.

I am happy that similar ideas was applied to SC2
November 1st, 2010 at 4:01 pm
louis says:

@ben,

Unfortunately, there is no crossover or sexual reproduction in the program. This makes the term ‘genetic algorithm’ a bit misleading as it’s really more properly classified an evolutionary algorithm. I tried digging into the code to find any example of either but wasn’t able to — I wasn’t entirely sure what was happening since the program leans heavily on JGAP and I’m not familiar with JGAP.

At any rate, I asked one of the devs on reddit about this very question and he confirmed that there is none. It’s all done asexually through mutation.
November 1st, 2010 at 4:01 pm
Toodles says:

If I remember my Dawkins right, you mean ‘gene’, not ‘chromosome’.
November 1st, 2010 at 4:29 pm
Angelo says:

Prior to reading this article I queued up Crota’s most recent shoutcast — http://www.youtube.com/watch?v=DRSDVr97h2Y — and after checking out your code I watched FruitDealer execute this exact strategy to no avail. Interesting timing, all around.
November 1st, 2010 at 4:33 pm
Whoa says:

You might have done a lot better to use a higher quality evolutionary computation toolkit: for example, ECJ.
November 1st, 2010 at 4:51 pm
Steve says:

As the assumption that you saturate the extractor as soon as it’s down?
November 1st, 2010 at 5:16 pm
John DeHope says:

This is pretty much exactly why I never play online games. I prefer to play games against people rather than algorithms. I wonder if they could mod the game to randomize certain things like build times, damage dealt, damage absorbed, etc., on a per-event basis. So every time you built something, it’s build time was altered by +/-10% or so. As well as every time a unit did damage, etc. This would be no fun whatsoever for genetic algorithm developers, or tournament players, but might actually be more fun for run of the mill people.
November 1st, 2010 at 6:26 pm
Forrest says:

@John DeHope, Even if you add some noise like you suggest, the genetic algorithm will still work just fine on this – assuming that you can figure out how much noise is being used. You just include the noise in the fitness function during evolution. People use GA’s on noisy problems all the time.
November 1st, 2010 at 6:31 pm
day[9] fan says:

So, what does day[9] think of this strategy?
November 1st, 2010 at 6:41 pm
AP says:

7 roach may be pretty strong, diamond league T here, have not seen it in any ladder play recently…
November 1st, 2010 at 7:42 pm
phd-man says:

your formulation suffers from several drawbacks, to name 2 in particular:

1- you are assuming that a human player will not adapt his strategy in response to your “optimal” build order

2- genetic algorithms are based on the hope that the best members of a population are the best overall, which of course is not guaranteed at all

in summary, your results are not applicable nor are they reproducible. have a nice day.

PS- did you think you were the first one to think of this?
November 1st, 2010 at 7:45 pm
RedBlackTree says:

This is a novel idea, however I assume this uses some model to produce outputs as opposed to running the build orders in SC 2 itself?　My primary concern with such an approach would be that the error caused by the model would be greater than the difference between competing strategies. This is only worsened when you consider human error.

Additionally it seems like you’re going to have an issue with local optima vs. global optima. So this is useful for fine tuning a strategy, but not finding the “best” build order (whatever that may mean in this context.)
November 1st, 2010 at 8:05 pm
derp says:

“PS- did you think you were the first one to think of this?”

The point is that he didn’t think of this, the program he made did.
November 1st, 2010 at 8:12 pm
ZLiang says:

Love the genetic approach, but the real watershed would be an algorithm that considers combat factors, which are impossibly myriad.
November 1st, 2010 at 8:23 pm
Barry says:

I am pleasantly surprised at the level you talk about concepts in StarCraft on a general level to make it understandable for non-StarCraft players.
November 1st, 2010 at 8:59 pm
witek says:

@John DeHope

Adding randomned to gameplay will not change anything. Still you can talk about best average strategy and optimialise it. Also playing games offline (like board games) do not rules out genetic algorithms when prepearing (all rules are known, and one can easly determine some scheme of working), but if starting condition in many board/card gams are sufficiently random, then yes, human player will be not able to use heuristics infered from offline GA runs, as every game is different, and no single strategy will work. But well, if he have iPod in his hands…
November 1st, 2010 at 9:25 pm
Andrew says:

“Zerg also have a third de factor resource: larva.”

I think you mean, “de facto”…
November 1st, 2010 at 9:28 pm
Ben Steenhiusen says:

Cool program. I just have 2 questions:

1) Why do you use a GA? I know GA’s are commonly used for scheduling problems due to the large search space; but why is essential for this to use a GA? Surely a DFS with memoization based culling (when you reach a stored (examined) state you cull it if it less optimal) will work just as well. I’m not sure of the complexity and despite its growth will be exponential at worst I am not sure that is a huge problem with limited decision trees?

2) Have you ever brute forced and found a more optimal solution?

Drop me a mail sometime.
Regards
November 1st, 2010 at 11:08 pm
Thomas says:

I am a silver 1v1 player, ranked #9 in my league. I have been using this build all night and it is really great. It is an excellent segue into building mutas. You have to be careful and kill off any air production buildings early off. Really digging it.
November 1st, 2010 at 11:41 pm
XeRoD says:

This is a pretty neat program. However with Toss – Zergs can no longer rush me – I have a perfect counter for the basic rushes with Roaches. Any decent player will be able to combat this. I posted the normal starting build I use when there is a Zerg opponent ( 1 vs 1 or FFA).

You can block the zerg rush easily with a protoss build at the entrance to your base.

Build 4 Probes

Build 1 Pylon at entrance

1 Forge blocking half entrance

1 Warp Gate Blocking other half

1 Photo Canon

2 Probes

1 Zealot

1 Photo Canons Behind

1 Zealot

4 minute mark

1 Vespine Mine

1 Photo Canon

1 Zealot

1 Photo Canon

1 Zealot

5 minute mark

At the 4 minute mark 7 roaches can’t get in and are killed by zealots / photo cannons. You usually lose one of the zealots and the other down to half life. If they wait until the 5 minute mark they are completely destroyed with very little damage to the zealots. Right after this – I send 4 zealots (to lose) into the Zerg base and then the real play begins.
November 2nd, 2010 at 1:00 am
Trevor says:

Everyone commenting here seems to think that Louis Brandy wrote the code for EvolutionChamber when in fact it was Lomilar. Just wanted to point that out, as it’s stated clearly at the top of the post but nobody seemed to notice.
November 2nd, 2010 at 1:02 am
David W. says:

Interesting… but if your opponent scouts, which they always do at higher levels, and they see your roach warren go down, they’ll just wall off with cannons. At that point, your roaches won’t be able to do anything.
November 2nd, 2010 at 1:40 am
EEE says:

@ Zerod + Davud

This isn’t an all-in by any stretch, this is to set the pace of early game which Z couldn’t do prepatch. Z’s knowledge of this + time/money spent stopping roaches puts you at worst at an even footing
November 2nd, 2010 at 1:40 am
Dave says:

I’m just happy to see a blog get slashdotted that actually talks about a real genetic algorithm instead of a hill-climber that somebody thought was clever.
November 2nd, 2010 at 1:47 am
Japu says:

needs to take into consideration a way to deny scouting before throwing down the roach warren. aka 1 pair of lings
November 2nd, 2010 at 1:55 am
EEE says:

@ japu

this isn’t allin but it is blind, its about control rather than a cheesy win vs nonscouting idiot

both fast airs counters (VR/Banshee) aren’t fast enough to pull out a win against the z, and the roaches will win in a base trade every time if they go blind air rush (you can ninja build to live while allin air guy is locked in his starting position)
November 2nd, 2010 at 1:57 am
delta says:

John DeHope says: “I wonder if they could mod the game to randomize certain things like build times, damage dealt, damage absorbed, etc… This would be no fun whatsoever for genetic algorithm developers, or tournament players, but might actually be more fun for run of the mill people.”

Two points.
(1) Blizzard games (Warcraft I, etc.) originally did have randomized damage etc., but that was developed out of later games.
(2) As Richard Garfield wrote in a 2006 Game Developer article, “Historically, games usually evolved in such a way as to reduce the amount of luck in them. Even chess at one time had dice. The people who are in a position to modify a game are likely to be very good at it, and the sort of modifications they will be drawn toward are the ones that showcase their talents and their friends’ talents–although they, of course, are all top players.”

Some discussion here: http://boardgamegeek.com/thread/141471/getting-lucky-the-role-of-randomness-in-games
November 2nd, 2010 at 1:58 am
delta says:

phd-man says: “your formulation suffers from several drawbacks, to name 2 in particular…”

Gonna call “not coherent” on that whole post.
November 2nd, 2010 at 2:51 am
Chris says:

The strength of this type of programmed analysis is that no matter what, it can be rerun with new inputs and a new maximal build order found. Like others above have said, if Blizzard adds RNG noise to the input values, the program can account for that variation in its calculations and at worst, it would give a probability of success along with each generation of build orders, eventually still selecting the single build order that has 83% success rate during testing compared to other, failed build orders that are only successful 62.4% of the time, for example.

The only way around it would be for Blizzard to introduce such a large number of variables and randomly reset/reorder them on some sort of fast schedule such that you don’t have time to run the program through enough iterations to account for the changed values from match to match or day to day or week to week, whatever. Then again, that would never happen, because it would completely baffle the regular human players who would always be wondering why their siege tanks rock on Monday and then fire Nerf balls on Thursday.

It is true that a local minima might be reached, especially since the program jettisons the failed build orders and replaces them with altered versions of what it thinks are the better. It’s possible that the jettisoned build order would actually be more successful in alternate scenarios as 1.) Blizzard patches and nerfs certain units or attacks or 2.) other human players adapt to the 7R build with whatever Strategy X manages to defeat it.

Easiest way to get around that

This reminds me of a research experiment that several of my peers were doing looking at directed evolution in a single gene. They took a strain of bacteria that could natively grow between 30degC and 70degC, and took out a single required gene, Gene X. They then replaced Gene X with the homologue of Gene X from another bacterial species that could only grow at 30degC. So that introduced Gene X is the “weak link.” Then they slowly raised the culture temperatures from 30degC eventually to 40, 50 and 60degC and sequenced the introduced version of Gene X to see which particular DNA changes and amino acid changes allowed for Gene X to function at the higher temperature.

In their experiment, they found that going from 30 to 40deg C produced two common variants, both of which allowed Gene X to function at 40degC, however, Variant 1 had a hard time going up to 50degC with additional changes (Variant 1.alpha), while Variant 2 could easily have a few more changes (Variant 2.alpha) and be functional at 50degC. So now once they brought their bioreactor up to 50degC, it was all Variant 2.alpha since Variant 1 and 1.alpha was selected out (like being jettisoned in Lomilar’s program).

The kicker was that the first change in Variant 1 was eventually conditionally required along with Variant 2 in order for an even higher ceiling of temperatures at which Gene X would function. However, the additional changes to Variant 2 (2.alpha) that allowed it to rise up to 50degC were non-cooperational with Variant 1. So it hit a local dead-end.

As long as Lomilar’s program is capable of walking back down the road to re-explore previously “unwise” build orders in conjunction with modern opponent build order tactics and neomorphic Blizzard unit values, it should theoretically be able to make pretty much any normal 13 year old casual gamer cry in frustration.
November 2nd, 2010 at 3:54 am
Hidenori says:

Great use of a computing technique that is probably still seen as a bit esoteric for an interesting purpose. I have never played either Starcraft before, but it seems like you might get even more gamechanging results by pitting generated builds against each other during evolution. You’d probably have to hack the game engine for this though…
November 2nd, 2010 at 4:19 am
Rob says:

John DeHope says: This is pretty much exactly why I never play online games. I prefer to play games against people rather than algorithms.

Is is me or is this a totally nonsensical comment? I don’t play games online against other people because I prefer to play single player against the computer. Cause I don’t like playing against the computer.

Surely if you don’t want to play against the computer you would play online?
November 2nd, 2010 at 4:38 am
SWW says:

“As long as Lomilar’s program is capable of walking back down the road to re-explore previously “unwise” build orders in conjunction with modern opponent build order tactics and neomorphic Blizzard unit values, it should theoretically be able to make pretty much any normal 13 year old casual gamer cry in frustration.”

I like that at the end of your very interesting science post, it all leads up to this. The end-all be-all of genetic engineering, neural design, and mathematics: making 13-year-old gamers cry. Now that’s something we can all get behind.
November 2nd, 2010 at 4:55 am
Analogy says:

Re: Adding randomness to make algorithms less effective.

The thing about build orders in Starcraft is that you can have a plan for a build order in mind, but there is still an element of skill in actually implementing the build order. It’s not just “make a list of things to do and they just happen,” there are considerations like optimizing your workers to minerals (it is more optimal to split two workers across two mineral patches rather than have them both mining the same patch), knowing when to pull them off the mineral line so that they end up in place to build a structure at the exact moment you get the minerals to start building the structure, things like that. Also, once you get beyond the initial build order (it really is akin to a chess opening) you start getting into the real game where algorithms aren’t going to help you.

All that adding randomness does is add a random component to the outcome of a fight or match. If the point of playing a competitive game is to see who is better at it, basing some of the final result on whether a random roll went the way of one player or another is not a good way to do it. You want every action to have a completely predictable consequence so that when one player wins, it’s because they actually won and not because the computer threw it their way.
November 2nd, 2010 at 5:12 am
Andrew says:

I was amused to see this earlier today, now I see where it came from! My eyebrows went right up when I scouted the early early roach warren, but I wasn’t too stressed – a terran wall-off and height advantage dealt to it well enough.

As usual, the hard bit with evolving algorithms is the success function – working out which of two competing strategies is better.
November 2nd, 2010 at 5:45 am
charles fox says:

Nice work, suggest you make a version for one of the open-source RTS games that are used in many academic papers, then go for a publication yourself!
(Academics like open source games as they can get inside and see exactly what’s happening, and evaluate ideas independently. You might also submit the strategies as patches to those games to improve the computer players?)
November 2nd, 2010 at 6:10 am
rei says:

This program will discover rush builds but not much else. A “rush” is a build in which you rush to produce a certain unit, regardless of what might happen if you happen to get attacked in the process. This program doesn’t take into account defense during your build, so by definition, it only produces rush builds.

By the way, 5RR is already impossible to defend against on Scrap Station, because you need force fields to fend them off — and with the width of the choke, you can only hold them off for half the time that you can on other maps, and only 42 game seconds later.

I always assumed that Blizz would have some in-house build modeling software, but it’s been pretty clear from their recent bug-ridden, untested QA-less patches that they really just wing it.
November 2nd, 2010 at 7:45 am
thatjguy says:

I dunno about this…I’m capable of getting a VR out by 4:42. Dance the probes a bit while the VR hammers the roaches. It would be interesting to play against this…now that this is slashdotted I’m sure I will.
November 2nd, 2010 at 8:09 am
witek says:

Well, most appropriate way would be to actuall varie games rules randomy on each new gameplay. You will start game, game will present you differences beetween normal game, you will have one minute to comprehend it and prepare strategy. It will basically means theere are infinite number of starcraft (some of which would be probably not well balanced).

But this will mean that really creative and innovative players will prepare good strategies faster, even if this is starcraft they never played before (there always would be some differences, so no body would create single or even few good strategies, one would need meta-strategy).

Other possibility (simpler to do well) is to just generate random map. And show its layout on the begining of the match.

Just an idea. Any one knows such games? (RTS with random maps, or with varing rules?)
November 2nd, 2010 at 8:16 am
Nobby-W says:

This is reminiscent of a program called EURISKO (http://portal.acm.org/citation.cfm?id=1056663.1056665) that was used to win some Trillian Credit Squadron (a Traveller related game released by GDW around 1980 or so) tournaments in the 1980s. One mode of playing TCS is pitched battles, which are more or less completely dependent on the fleet design. EURISKO came up with highly counter-intuitive fleets that won the competition a couple of years running. The author was subsequently asked not to participate in subsequent competitions.
November 2nd, 2010 at 8:24 am
jtf says:

First off, this evolutionary algorithm is hilarious. I’m very truly impressed, and I’m wondering what else comes out of it. Contrary to many other people here, I want to see as many unusual things out there as possible for me to encounter. Keeps the game interesting!

As for the specific criticisms against introducing an algorithm to make build orders I have to say that the other commenters are right. There is really no substitute for skill in the game. Not only is executing the build order at maximum efficiency level a task in and of itself (particularly for zerg, who require the highest APM) but there are built-in habits that many players use to be able to save themselves from even unusual build orders. I know how I would react to this build.

For example, in a protoss v zerg matchup you will always see an early scout from protoss. This build also has several telltale signs that signal a protoss player to be afraid of an all-in, and even tell you what kind of all-in it is.
1. No scout drone.
2. No expo.
3. Drone count.
4. No lings.
5. Roach warren.
If by minute 3 there are no units, a drone count of 17, no lings, no scout, and a roach warren out, there is almost certainly a 1-base early push going down.

I’m making a generalization here, but if a good protoss player sees something that makes him or her look like things are going for an all-in, then one of four things will happen:
1. Cannons will go up, negating this build’s aggressiveness. It also causes protoss to lose all initiative, which is something I personally dislike.
2. A 4gate. A well-executed 4gate puts out 4 warp gates with a timing similar to this build (~5mins, if boosted the whole way). Since this build involves no scouting, a proxy pylon placed near the zerg base can simply wait until the roaches and drones waltz out of their choke point and then run in with 8 zealots, which will have enough dps that even on a map like Steppes, at the very least you have a decent chance for a basetrade in protoss’ favor, with the additional power of the zerg all-in mitigated by the ff at your base and the distance they have to traverse. tl;dr protoss can abuse their positional advantage.
3. A stargate will go up. If the ff goes down as in the team liquid post, then a stargate started at 3:30 and double boosted will spit out a void ray just in time for this push… and negate it completely. Sure, the queen can hit the void ray, but sufficient stalkers from a 2gate-stargate opening can and will focus the queen if she’s in any position to hit the void ray. Alternatively, if a phoenix comes out as well, the queen is just dead. Without the queen, the build has no AA.
4. Early harass. This build relies on pinpoint timings and no disruption of resources. It’s not uncommon in pvz matchups for me to look at the timing of a spawning pool or for the presence of zerglings and if I see neither, to delay my cyber and run in with a boosted early zealot. If I can kill 1-2 drones and disrupt the other player’s macro, it’s worth it. Often I can get up to 3-4, even in matchups against 1500 level diamond players. High level players will punish others *mercilessly* for having insufficient early armies, whether it be protoss with running zealots in and killing workers, zerg early lings, or terran reaper harass.

That was a lot longer than I expected. Either way, this build suffers from almost all the flaws of all other all-in builds. It should be *fine* to counter, even though it eats the current protoss meta for breakfast. People shouldn’t be afraid to not robo against zerg.
November 2nd, 2010 at 8:48 am
jhuni says:

Forgiving me for being skeptical of this, however, I think B.O construction is not that useful, simply because in SC you have to consider the map, who you are playing against, what race they are, and a variety of other factors.

Therefore what we need is problem-solving skills and a learning/deduction system not more build orders.
November 2nd, 2010 at 8:58 am
SteveH says:

You might want to check out Lenat’s Eurisko program:

http://en.wikipedia.org/wiki/Eurisko
November 2nd, 2010 at 9:01 am
kamo1000 says:

As a diamond player, there is one thing very important that I think is missing : the distance between 2 player. Many build are ok on small map and are not on 4 player map.

IMHO, You shouldn’t compare the effectivness of two army at 4’40 for example. An army build at 4’40 will encounter an other army built and finished a 4’55 on a small map and maybe on 5’10 on a big 4player map (maybe more if a terran wall repaired). I also guess you didn’t take into account a drone scouting where is the opponent (1 less drone on mineral gathering).

An other information, that I think you should be able to calculate is when a build have to be scout to be countered. For exemple if a protoss scout a 7roach rush before 3’30 he can adapt and auto win, and after here is a list of different build that are ok if the distance between the two base is greater thant X. This could be helpfull to when to scout, with wich build, depending of the size of the map (longer to scout, longer to the other army to attack)

Anyway, this is a great work and offer a lots of great way to get hints on how to improve the game.
November 2nd, 2010 at 9:22 am
High Diamond Zerg says:

@John DeHope That’s possibly the stupidest idea I’ve ever heard. You are playing against real people who make real errors all the time. Not even the best players in the world can execute any build perfectly every time they play. They all have to make real decisions, and sometimes very fast ones.

The reason is, you’re bad. You can’t handle real competition which is actually fun for a lot of people. It may not be as fun sometimes, and it can be frustrating, but a lot of people out there except this challenge and enjoy it.

Once you understand the micro, macro, game sense, mechanics of this game, AND the meta-game. It’s truly fascinating. This is really the new ‘chess’ of our generation. But it has a lot more action and is more fun to watch, in my opinion and probably most young people’s.

Not only is there all these things I mentioned in SC2, but there’s also mind games and knowing your opponent. There’s a huge psychological factor in this game, especially at higher levels of play, just like poker.
November 2nd, 2010 at 11:21 am
B.A. Man says:

@Ph.D. Man

Your posting suffers from drawbacks, to name 2 in particular:

1. It makes you look like a pompous asshole

2. If you had bothered to read what was written you would have seen this isnt the author of the program

in summary, your posting isnt relevant or applicable since the author is way cooler and less assuming then you are.

PS did you think you were the first person to act like a dickhead on the internet?
November 2nd, 2010 at 12:38 pm
celwell says:

Adding randomness to the game properties would not hinder a GA from producing optimal build order.
November 2nd, 2010 at 3:24 pm
Eric says:

Interesting find. Demonstrates some of the strength of AI approaches, such as finding that the extractor trick actually speeds up this build. As a chess player, the use of computers in opening theory has led to some counter-intuitive approaches, of interest even to those who don’t use them.

In an actual game, there are of course ways to counter the build, but 7 roaches are difficult to handle if your scout is not early enough. Roaches are effective against the first unit of both other races (Marines and Zealots). A bigger concern I would have is that the seven roach objective is somewhat arbitrary–how much would the build order change for 9 roaches? 10 roaches and a lair or expansion?

But
November 2nd, 2010 at 5:00 pm
disReGard says:

Hope will Blizzard will hire you.

I’m actually working in the games industry, too. This is just awesome, from both a programmer’s and a balance designer’s point of view …
November 2nd, 2010 at 6:35 pm
Alan Le says:

Eric, the 7 roaches count is not arbitrary. The Zerg hatchery supports up to 3 larva to produce roaches. The Queen spawn larva ability adds 4 more. With correct timing, the roach warren finishes building just right when there’s 7 larva with enough minerals and gas to build them. The timing for 9 or 10 roaches wouldn’t as strong of an attack because your opponent might build up units/defenses to counter the roaches.
November 2nd, 2010 at 10:35 pm
Dimitri says:

I wonder if you could then use such an approach to balance a game – start off with a basic template for unit design, and run thousands of systems simultaneously with random evolutionary tactics, with a goal of having an even distribution of wins for each team.
Every time one team is out of balance, the game is adjusted slightly.
November 3rd, 2010 at 6:22 am
Anonymous says:

Dimitri: The point is that these algorithms don’t take into account what I’d call the “limited human skill”: Let’s have a look at one of the conditions for example:

“It’s generally considered wasteful to buy supply when you don’t need it (since you could spend that money, instead, on units or workers now, and buy the supply later, when you need it).”

No human player will ever be able to play at their supply cap all the time. Because we can’t do an infinite number of things simultaneously, one very basic decision always remains in real-time games: What to do next?

That’s why we tend to just add more supply than we need (for the moment), for we’re able to focus on other things then.
November 3rd, 2010 at 6:23 am
disReGard says:

Last one was my post
November 3rd, 2010 at 10:01 am
Mark says:

This build totally sucks has so many openings ands counters, when scouted means instant lose.
November 4th, 2010 at 6:39 am
Daniel Wright says:

what an interesting discussion! If I was taught mathematics like this when I went school I would certainly be better at it today. Author: you should definitely publish this as a paper, great work!
November 4th, 2010 at 6:59 pm
Ekko says:

I’m a pretty high level SC2 player and I can assure you this build is very strong. A lot of lower level players see it as weak because they don’t quite know the game or how to use it yet. The unit that it produces, Roaches, are a slow moving medium range unit that do a lot of damage and have good armor and hitpoints. Players do scout this and do use defensive structures to try and stop it but because of the range of the unit it is simple to pick off a structure or two paying for the cost of the roaches while creating an even better economy at your base. It doesn’t have to kill them to win.

@dimitri: This is exactly what blizzard does to balance their game, they have posted this in the past. It is difficult to factor in the human level of moving units to optimize damage dealt and minimize damage taken. One of the main units the Zealot does an enormous amount of damage and has high hitpoints, but it moves slow and has no range attack. If a group of these is used to attack a group of ranged units a pure testing program will incorrectly balance them. This is because a good human player will not stand in one spot and let their units be hit. They will move around (called Kiting) and abuse the range of the units.
November 7th, 2010 at 11:31 pm
fjlj says:

you should add a “one base” option and a reset button
November 12th, 2010 at 6:17 am
Lee says:

Absolutely fascinating. Great work!
November 12th, 2010 at 9:13 pm
Paul says:

This kind of reminds me of the results Lenat got with the learning program EURISKO when he applied it to an old pen-and-paper tournament “Traveler Trillion Credit Squadron”. The program came up with strategies that exploited rule loopholes, and the tournament organizers asked him to stop entering after he won two years in a row.

These sorts of systems might be useful for balance testing games, to figure out semi-exploits before the players do.
November 13th, 2010 at 12:03 pm
Fuqnaround says:

I enjoy your exercise in futility. Everyone knows that when (n)local optima combines naturally with the evolutionary principle outline at the base common postulates random mutantenty anima genus chupacabra.

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November 20th, 2010 at 3:57 am
SC 2 Nut says:

It’s obvious this program doesn’t really work.

This isn’t even the right build order for a roach rush, pooling on 11 is too fast, it’s a waste and won’t get you 7 roaches any faster than pooling on 13.

I’ve tested multiple roach build orders in SC 2, this one is the 2nd fastest.

The extractor trick also doesn’t save time in SC 2 because of the time you lose mining with the drone to do it, and because of the temporary cost. You don’t even need to do the extractor trick.
November 21st, 2010 at 3:01 am
blah says:

@SC2 Nut, this isn’t the ideal build order for 7RR. I’ve seen replays of getting 7 out at 4:20-4:30
January 4th, 2011 at 1:29 am
Yisong says:

In the case of early build orders, the search space doesn’t seem that large. I wonder if, given a specified rush build target (e.g., 7RR), one can’t just use dynamic programming to find the global optimum (e.g., the earliest time to meet target).

Genetic algorithms are not guaranteed to find the global optimum due to how they explore the space of possible strategies (i.e., randomly). It seems there is enough structure here for dynamic programming (or a modification of it that does some kind of lazy evaluation) to run efficiently.
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March 3rd, 2011 at 11:31 am
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Starcraft is so much fun. What a great post Thanks!
March 27th, 2011 at 11:46 am
gewinnste says:

Very nice work! I have downloaded another variant of the tool (apparently done after your tool came out) that can also do it for terran and toss:

http://code.google.com/p/scbuildorder/downloads/list?saved=1&ts=1290504747

Here’s the thing: It really provides the fastest BO for, say 2 tanks, 7 rines, stim and siege mode – however, it will always calculate for the end point and not care about earlier defense. That’s why almost always, it will produce something like: 10 Depot; 11 Gas; 14 Rax …
Like this, the Fax is coming out very early – nice – but you won’t have a Rine to kill the scout etc. Now, there is the option of making so called “waypoints” where you can require a marine to be out at e.g. 3:05 and then go on towards the goal. But then, it won’t care about gas until 3:05 which ruins everything!
Similarly, e.g. if you want to have the best BO for a 4 Marauder 8 marine drop with stim ready as your medivacs arrive at their mineral line, it’s just not possible since the tool will calculate a BO where the 8th rine/4th marauder and stim are finished at the earliest possible time. And, again, if you set the unit production as a “waypoint” and stim as the final goal, it will not care about stim until the last unit is produced!. You could obviously try and guess which and how many units should be required at the waypoint so that stim is starting to get researched early enough but this should be sth. the tool should do because there are too many possibilities to try.

If this issue could be worked out, I think, it will be incredible powerful and SC2 openings will really acquire almost the deterministic character of chess openings.
June 22nd, 2011 at 3:40 pm
Stephen says:

Hi, I’m no programmer but I think I get the idea. It is something which any SC2 player does to some degree – trying to squeeze things out at the perfect time. However I see difficulties when you throw the human factor into the mix – most specifically in scouting and harassing. Your opponent is going to reacting to you as well, and will probably be trying some sort of harass. If you are a Zerg player in diamond upwards you will know what I mean
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October 31st, 2011 at 7:11 am
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October 31st, 2011 at 11:26 pm
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November 1st, 2011 at 12:08 am
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November 1st, 2011 at 12:31 am
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November 1st, 2011 at 12:48 am
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November 1st, 2011 at 12:58 am
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November 1st, 2011 at 1:05 am
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November 1st, 2011 at 11:36 pm
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A contract manufacturing company (otherwise known as Original Equipment Manufacturer or OEM) is someone you can get to assemble any product you wish for a fraction of the cost you would pay to set up your own manufacturing plant. For example, let’s say you invented a small electronic tool for your car and want to commercialize it, i.e., mass produce and sell it. Before contract manufacturing, you would need to rent a warehouse, buy all the equipment, and most importantly hire the laborers to assemble your product. Obviously this involves a huge financial outlay and bears the related risk.
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November 1st, 2011 at 11:51 pm
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November 2nd, 2011 at 12:07 am
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November 2nd, 2011 at 12:14 am
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November 2nd, 2011 at 12:24 am
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November 2nd, 2011 at 12:37 am
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November 2nd, 2011 at 12:44 am
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November 2nd, 2011 at 12:51 am
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November 3rd, 2011 at 12:11 am
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Brain-eating amoeba is not really an amoeba, but is just named as one. It belongs to the phylum of Percolozoa.
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November 3rd, 2011 at 12:18 am
R4 R4 says:

This amoeba does not really target a human brain, but it is in search of bacteria for food. When a human consumes water in which these amoebas are present, either by drinking, swimming or bathing, the amoeba finds its way to the brain, through his/her nose. The scariest part is, that the survival rate for the resulting infection is only 1%.

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November 3rd, 2011 at 11:46 am
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November 3rd, 2011 at 11:58 am
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November 3rd, 2011 at 12:01 pm
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November 3rd, 2011 at 12:05 pm
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November 3rd, 2011 at 12:16 pm
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November 3rd, 2011 at 12:24 pm
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November 3rd, 2011 at 12:48 pm
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November 3rd, 2011 at 12:58 pm
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November 3rd, 2011 at 1:01 pm
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November 4th, 2011 at 12:24 am
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November 4th, 2011 at 5:34 am
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Lyme disease is common in parts of Eastern United States, and this tick-borne disease is transmitted to humans through the bite of deer ticks.
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November 4th, 2011 at 5:56 am
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November 4th, 2011 at 9:45 am
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November 4th, 2011 at 10:19 am
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November 4th, 2011 at 10:27 am
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November 4th, 2011 at 10:58 am
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November 4th, 2011 at 11:02 am
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November 4th, 2011 at 11:10 am
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Martedì 01 Novembre 2011 10:19

E’ stato nei giorni scorsi il ministro del Welfare Maurizio Sacconi a lanciare l’allarme: nel nostro Paese esisterebbe un concreto pericolo di ritorno al terrorismo, a quegli anni bui che fecero precipitare l’Italia dentro un clima permanente di tragedia.
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November 4th, 2011 at 10:16 pm
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November 4th, 2011 at 10:53 pm
iPhone says:

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November 5th, 2011 at 3:07 am
Ajay says:

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November 5th, 2011 at 6:11 am
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Oakville real estate is a fabulous town to live or invest in within the Greater Toronto Area (GTA). This beautiful lakeside town is very desirable for residential and business centres within Oakville Real Estate Ontario. With great safe neighbourhoods, close proximity to Lake Ontario, beautiful recreational areas, quality schools, a full service acute care community hospital, popular shopping districts, services, restaurants and downtown area. If you’re looking for a beautiful urban city, while still keeping the small-town atmosphere then Oakville Real Estate is perfect for you.
November 5th, 2011 at 6:24 am
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Your Business Plan should deal with…
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November 5th, 2011 at 11:32 am
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November 5th, 2011 at 11:40 am
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Corporate Video Production. We are a small UK corporate video and DVD production company based just north of London, near Watford. We can handle any type of production from website sales videos to in depth documentaries, from scripting to final delivery. Our clients include a mixture of charities and corporations – you will find many example clips here. We have an open rate card and most non-profit organisations qualify for a 20% discount off these prices. For general advice about budgeting see our information pages. If you would like a quick quotation please email or phone Chris Pettit on 01923 269599.
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November 5th, 2011 at 12:02 pm
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November 5th, 2011 at 12:14 pm
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November 5th, 2011 at 12:33 pm
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November 5th, 2011 at 1:18 pm
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November 5th, 2011 at 10:02 pm
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November 5th, 2011 at 10:13 pm
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November 5th, 2011 at 10:18 pm
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November 6th, 2011 at 12:15 am
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November 6th, 2011 at 12:19 am
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November 6th, 2011 at 12:35 am
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November 6th, 2011 at 12:39 am
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November 6th, 2011 at 1:09 am
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29/10/2011-LICENZIARE PER CRESCERE? FORSE SI’

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November 6th, 2011 at 4:12 am
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January 24th, 2012 at 3:33 am
key says:

At lunch avtoklyuchar was already the fourth door and locksmith seriously worried. King also. Both decided to sabotage avtoklyuchar. During lunch hour locksmith put some laxative in avtoklyuchar lunch and rubbing his hands contentedly. King also put laxative in foods avtoklyuchar and comforted, that would not give his daughter. Avtoklyuchar felt terribly bad and fast-neshte quickly left the race.
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January 25th, 2012 at 10:42 am
key says:

All candidates locksmiths are distributed in different maytori kluchar, who began to teach them the ropes. The first lesson is kluchar how to make a switch. Locksmiths masters show how a piece of metal is placed in a machine for cutting keys, then place the key model and proceed to its elaboration.

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February 6th, 2012 at 9:42 am
key says:

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February 7th, 2012 at 6:01 am
key says:

This, nothing spoke to him. And how in the shaft could be a meadow. Drain had no answer to the questions that tormented him and went looking for someone who can give him a satisfactory answer where he fell.
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February 7th, 2012 at 8:17 am
XREDXR says:

Brisk walking and Jogging offers almost the similar health benefits. The only difference is that you can achieve the benefits faster with running and jogging. If the distance is 3 miles, then a person who is jogging or running covers it faster than a person who is walking.
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February 8th, 2012 at 4:17 am
key says:

Do not risk yourself to change your contacts at home, because very often happens to be something wrong with the installation and unprofessional to make short-circuiting the entire electrical grid in the house.
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February 21st, 2012 at 7:17 am
key says:

One was very tall windows and had to bend his neck to watch it, the other very low and windows had to bend over to hear it, the third very beautiful and looked at each stood in his mirror, including a spoon windows while she eats, the fourth too clever and worrying about things like energy crisis that will occur after 50 years and problems with drinking water in third world countries.
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