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Comment: Re:Meh (Score 1) 293

by DarkAnt (#35907068) Attached to: Taking the Fun Out of <em>StarCraft II</em>

Starcraft plays very similar to chess. So before a game starts you should have a game plan. You should think about what kind of unit composition you'd like in the late game, then figure out what mid game strategy will help you get to the late game. With a general concept of what you'd like the mid game to look like you then pick an early game build that will help you get to the mid game. Of course the game might deviate from your original game plan, but using it as a guide is very helpful.

It's a bit interesting that you picked TA (I love TA) over Starcraft in terms of tactical gameplay, because Starcraft is much more micro oriented than TA. If you'd like to see some very top level tactical decision making take a look at SeleCT and qxc's replays. There's actually a huge amount of tactical decision making in Starcraft. So if we take for instance a hellion attempting to harass the natural's drone line the "terrain" becomes relatively complex. If the hellion comes in for the harass and the queen is right next to the hatchery then we might be able to scoot around the outside of the mineral patch to roast a few drones and then run up into the main to continue the economic damage, before the queen and zerglings can close in on the hellion. A lot of "terrains" can simply just shut down your harass such as a queen blocking the ramp with speedlings in play or a well positioned spine crawler. I don't think I've done the concept of hellion harass nearly enough justice, but hopefully I've expressed some flavor of the situational complexities that can occur with relatively few units, let alone 200 supply armies. Starcraft is much more of a deterministic game than TA, which lends itself to e-sports. Oh, if you really like TA take a look at the Spring Engine.

Comment: Re:Likely more prevalent an issue than we realize. (Score 2) 221

by DarkAnt (#35261396) Attached to: Feds Pay Millions For Bogus Spy Software

The reason these individuals were not fired is because it's a merit-based organization. A meritocracy penalizes failure and rewards success. By penalizing honest mistakes the people who end up on top may not be those with the most merit, but those who hide their mistakes the best. This has the added detriment of not allowing the organization to learn from its failures.

Comment: Re:Social networking for robots (Score 1) 97

by DarkAnt (#35125578) Attached to: <em>RoboEarth</em> Teaches Robots to Learn From Peers

I was looking at their site and I can't tell what they actually have completed. It looks like they have a framework that they'd like to implement, but then what was the robot using? Was it a proof of concept that the robot could identify the situation and request code from a server?

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