Nov
4,2009
Deja vu
Posted by Sorian at 10:27 PM
I had the strangest sense of deja vu today. I spent most of the day essentially doing what I have been doing in my spare time for the past 2 1/2 years; tweak AI, watch AI play, tweak AI some more, rinse, repeat. It was surreal.
Dru has been awesome to work with. I was worried that he might be protective of his AI code (no real reason to think that, really, but I did). It has been the complete opposite. He has been completely open to any suggestions I have had and I have gotten the opportunity to fix some of the major things that bugged me with the Forged Alliance AI.
The AI isn’t much at the moment, but I can see where Dru is taking it and I like where it is going. One thing about this new economy is it should make things easier for the AI. Either the AI can afford to buy something or it can’t, there isn’t any guesswork or complicated economy balancing act. I managed to get a good balancing pass in today, but it will still need a lot more work. I am really looking forward to seeing what we can pull off.
For anyone interested, Dru is being interviewed by AiGameDev.com Sunday, November 8th.
From AiGameDev.com: This interview with Dru Staltman, Gameplay Engineer at Gas Powered Games, will look into the iterative development process that went in to creating Demigod’s innovative AI, as described in this article. How was the system originally conceived, what where the major challenges, which were the biggest problems in the end, and how could the AI be extended and improved further?
The announcement is tagged as Premium, so if you happen to have a Premium membership to AiGameDev.com and are interested in AI it might be a good read. If you do get a chance to read it, let me know how it is.
Labels: AI, Demigod, dru, forged alliance, gas powered games, programming, Supreme commander 2
以下为上文提到的那篇文章的描述,不是正文,正文要付费才能看
Goal-Based Action Optimization and the Hybrid Hero AI in Demigod
Alex J. Champandard
Demigod is an innovative hybrid between RPG and RTS games, challenging the player to control and manage a Hero-like unit to accomplish a variety of missions (e.g. Dominate, Conquest, Fortress) in a set of predefined maps. It’s built mostly as a multi-player experience, but includes an offline Skirmish mode for both quick games and tournaments against the AI. Since the implementation is based on a goal-oriented action planner, I’ve been keen to play the game and dig into the Lua code for the last week.
I was expecting to find a textbook application of the goal-oriented action planner (GOAP) from F.E.A.R. pioneered by Jeff Orkin. On the surface that seems to be the case, as the architecture and many of the high-level concepts are similar. However, under the hood, almost everything is done differently — to the extent that it’s more similar to other game AI approaches than goal-oriented planning. And indeed, due to the RPG nature of the game, the long term progress of the AI Demigods, and the dynamic yet tactical gameplay, the problem required a very different solution.
In this in-depth feature, you’ll learn how the AI used for the Demigods was architected and implemented (a.k.a. Hero GOAP). You’ll find out why the name “goal-based action optimization” fits better than calling it a traditional STRIPS-like planner implementation, and what was necessary to make such a system work on such a large search space with a wide range of choices available.
NOTE: This first article covers only Demigod’s Hero AI — which is the lowest-level and most complex layer of the whole system. The next article will show how everything fits into a three-layer architecture with multiple planners that include a squad AI and strategic reasoning.