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Parent Message
Other Bots
Just wanted to point you to other Bots for Q2 which also don't rely that much on user input for navigation (and you can feed them any map): All in all I've got my doubts that the UN Bot performs as good in more difficult terrain where you have to climb ladders, crouch, swim and dive and use tricky button combinations to open closed doors. But nevertheless good job ! |
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Others...
Hi Markus, welcome to the boards. CFG has a pre-processing phase, which William describes in his "Terrain Analysis for 3D Games" article. That's not first person. The Neural Bot does not claim to learn the map, since it actually can't. It has no input or state to allow it to do that. And the parabot requires human interaction, even if not directly. The aim was to make this one fully autonomous. As you said, the bot doesn't yet do too well in complex environments, but this is being polished right now... it will soon. All the features you mention are the next level up in the navigation, since they can be classed as high-level behaviours. Thanks for the comments. |
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CGF
CGF intentionally does not autonomously learn the map. The bots together do learn the tactical value of waypoints (getting a tactical understanding of the is done both in-game and off-line, and any of them already has an effect). Early on in the development of CGF, the bots were able to do some roaming and they created a waypoint graph on the fly. I tossed this functionality out for several reasons: <ul> <li>building a good map using solely roaming is quite tough in the Q2 environment</li> <li>having the AI operate using incomplete information about the terrain, while also gathering more information is a large and interesting task in itself (as Alex demonstrates), but not one that coincided with my aims</li> <li>most gamers don't want to fight bots that still need to learn the terrain; instead, they want opponents at about the strenght of their friends</li> <li>exploring the terrain in-game takes away a significant amount of CPU (most of CGF was developed on a PentiumPro 200Mhz)</li> <li>the roaming/mapping code and the changing terrain information dimensions would greatly complicate my (already) large design</li> <li>investments in "auto-mapping AI" typically aren't appreciated by game developers, because they don't need it in their games (note that most Q3Arena engine based games do not use the off-line automated mapping (AAS) facilities included in the engine license)</li> </ul> William |
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Map Learning
Regarding your 3rd point; it takes a bot significantly less time to learn a map than it does a human. Bots are very well suited to remembering stuff, and as long as the exploration scheme is reasonable -- it's acceptable in the demo, but the curiosity scheme I'm working on will make it better -- you can build up a map decent enough to embarrass a human player ;) For the 6th point, that's true for deathmatch FPS. But assuming a more interesting storyline, with an AI character that progresses through the level as you move forward, it would be an amazing feature. For MMORPG to, pre-processing is rarely possible. The applications are far and wide ;) |
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some comments & questions
"Regarding your 3rd point; it takes a bot significantly less time to learn a map than it does a human." I fully agree (although the bot might not autonomously gain the understanding that the level editor could give him using a few 'hint waypoint flags'). However, the problem is that if the AI needs to learn a map (and hence use incomplete terrain information), it is hard to write very efficient terrain reasoning algorithms on top of it. Do you have an example of a MMORPG where I cannot do pre-processing (I don't play them, so I don't know much about them)? |
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Embedded Agents
I don't play MMORPG either. Isn't it difficult to find time when you're a developer? Anyway, there are two cases where learning a terrain is essential -- aside just providing realistic human-like terrain exploration: 1) Huge static terrains, where pre-computing everything would take too much memory. You'd need forgetting for the bot to be able to cope with new areas, so learning goes hand in hand with it. Anyway, the navigation designed fits into my research approach to game agent AI, namely situatedness. The agent is embedded into its body (the animat), and cannot cheat by pre-processing or accessing unknown information. This is a bit of a hassle, and a lot of work to optimise, but possible and most definitely rewarding! Though I would have agreed with your other points a couple month's ago, the quality of the maps that are currently been generated are amazing for terrain reasoning: they more than match the brute force waypoint placing thanks to the simplicity of the representation, and since they are iteratively corrected, they tend towards the perfect representation for the bot -- the best compromise between simple and useful. Since your mesh is dense, you have to deal with information in a statistical fashion. With a simple representation, that can be done with vector arithmetic and graph operations. I'm polishing up the lower-level before I move on, but this is one of the things I'm intensely looking forward to (notably using a knowledge-based GA for topography based squad tactics). There are a few problems with the discovery still, like finding all the places in Q2 'base1', as you said on flipCode. This can be done with really random discovery behaviours, or brute force wall following, but I don't like the way it's done at all. But once these problems have been overcome, the system's potential may be unleached ;) |
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on large terrain and on your work
>Huge static terrains Operation Flashpoint comes to mind... Impressive game. Should have received a number of awards... This is exactly an area where pre-computing pays off. Keeping everything in memory is often not feasible (PS2 - not enough memory) or incurs significant overhead due to page misses and access to memory that had to be swapped to disk. I'd be curious how an 'on-the-fly' learning system would deal with an Operation Flashpoint situation. (Frankly, I believe Operation Flashpoint does not use not either method, but basically navigates on the heightmap and a 2.5D mesh; the terrain is pretty much free of obstacles, and solely in a handful of buildings and in guard towers you can arrive above other accessible locations). >[writing a paper] William |
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Different Approaches
On landscapes, it is true that very little terrain representation is required, as very simple reactive rules can get you around obstacles. This applies to towns aswell, but the more complex they get, the more a higher level of intelligence is required. I'm sure the streaming approach would work, and having spent a bit of time on landscapes with static pre-computed LOD levels, I can foresee how that would be done. Consoles are well suited to background loading in such a way. I guess I just like the idea of honesty ;) Until it comes crashing down on me that is! The paper will be on the path planning only, as that is a topic big enough for a paper. I thinking of ask you to read it anyway, but a review would be even better ;) The execution of the motion, and the learning of the terrain will be inside my dissertation. Cheers! |
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botepidemic.com
Could somebody tell me if I can find all interesting stuff that were located at botepidemic.com is available on another site. Is it mirrored elsewhere? It seems like botepidemic.com doesn't contain anything interesting any more. /Björn |
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mirror of botepidemic.com
Found an archive containing a "mirror" of botepidemic.com. It's quite big though, 58 MB. ftp://ftp.splatterworld.de/games/mirrors/www_botepidemic_com.zip |
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