Hi folks, This thread is sort of a fork from the ''AI using cover'' thread. It was briefly discussed in that thread that both FPS and RTS genres required environment (terrain) analysis to enable AI to make use of their environment for better game play. For example, in an FPS environment, a bot might used the hidden region behind an objects as cover, or the specific corner of a room to maximise arc of fire coverage of all doors into the room. In an RTS, an army general might select a given hillside on which to deploy his units, so as to maximise their effectiveness in combat. These thoughts led me to the conclusion that it would be very useful to have the ability to process the world model - in real time - to obtain information useful to the AI agents deployed in these domains. For example, in the FPS genre, obtaining a visual representation from the point of view of a specific player or bot could be used to deduce locales for cover and concealment. The interaction of sound and the walls of the environment could be turned into a sound map for the bot. In RTS, tactical terrain analysis can, to some extent, be handled with Potential Fields (Influence Maps). However, these are typically handled on a case by case basis and highly depend on the game design. What I am thinking of is a generic processing capability (that might be configurable to specific genres) that can process the world model in real time and extract important sensory information for the AI agents acting in this world. I specifically want to take the game designers out of the loop, so the use of embedded tags in the world model denoting good locations for cover, or the emmission of sensory signals (a la Sims) is not what I''m looking for. If you have thoughts, comments or suggestions as to the sorts of input and output formats of said data, or algorithms to compute it, I''d really like to hear them. While you''re considering this, ask yourself how generic your idea is and whether it would work on a piece of hardware optimised for processing world models in real time. Cheers and thanks, Timkin

Hi Timkin,
I am VERY inexperienced with AI but do enjoy reading up on the subject.
I enjoyed...
http://terra.cs.nps.navy.mil/www.movesinstitute.org/Theses/MorganThesis.pdf
with summarising .ppt on this page... http://online.cs.nps.navy.mil/www.movesinstitute.org/openhouse2003-wslides.html
The paper mentions depth maps.
There is an article, Using a Spatial Database for Runtime Spatial Analysis, in AI Programming Wisdom 2 by Paul Tozour which discusses the combination of data maps.
The use of 'Influence' maps for terrain analysis seems to be getting more popular. Is this true?

P.S. There are a few of the Monterey Naval PostGraduate papers which are very interesting but never seem to be quoted. What is the relationship between Military AI and Games AI people?


P.P.S. Nice to see William Van Der Sterren in the other post.
It was after reading 'Terrain Reasoning in 3D Action Games' that I began to believe I CAN write AI for my bedroom coded games.
I still haven't but at least I now believe I can ;-)

P.P.P.S Just realised this should have gone in the other thread.

[edited by - TonyG on April 23, 2004 6:01:18 AM]

I''ve always thought that if the game world uses pre-defined objects (such as "crate" or "table"), it might be useful to store information about that object''s cover characteristics with the object itself. I doubt this would solve the problem, but it seems to me it could form part of the solution, and it would probably be pretty efficient (e.g. one instance of the metadata applies to all instances of the object).

On the other hand, I don''t know anything about the subject, and it does strike me as an obvious concept that someone would have already come up with.

(This may be too far off topic, so I will keep this post short)

Dave,

That seems like it borders on the 'how do we do dynamic pathing' topic. While there are solutions based on using some sort of 'local pathing' (force based?), this seems to ignore the possibility that a large obstruction that totally when renders a space totally blocked.

Perhaps these could be addressed by the same sort of algorithm that determines which falling objects are considered cover.

[edited by - BrianL on April 23, 2004 2:49:32 PM]

First up, thanks for the replies and the interesting discussion. One point though; influence maps are not generic enough for the sort of analysis I''m looking at. They may be applicable to a wide range of scenarios, however they are the sort of algorithm you would expect to find in an AI middleware solution, rather than an environmental analysis setting. Sure, there''s overlap, but what I''m more interested is the fundamental issue of analysing the raw domain model and creating useful data (from just the world model) that a Game AI agent can use to make tactical decisions (This has strong implications beyond games).

Alex, I''ll definitely look at those references... it may not be until next weekend though... I have a paper to write and a funding proposal, all by next Friday 8(.

Bob, I understand that tagging the environment at design stage ''works'', but as Dave said, it relies on the environment not changing... and not all environments are designed (just most of those in the Games industry)!

Dave, I like the idea of your last post, however I don''t necessarily agree that we make decisions about cover based on size and density (at least, not just on those). We might use those attributes to perform a rough filtering of the domain to choose candidate areas. I think though, that once we have a list of candidates, that we probably hypothesise as to the ''goodness'' of the location based on what the enemy might do and what we''re trying to achieve.E.g. "Can an enemy who walks through door X see me behind crate Y?" Such decisions though are left to the AI itself...

I had an idea last year (or what is the year before) abou turning visual (or other sensory) representations of an environment into a semantic representation, so that scene analysis could result in scene descriptors which could then be used in an appropriate AI in whatever way was desired. So, for example, a description of the connectivity of rooms and corridors, along with doors (and their states) could be used in a pathfinding algorithm. Or, the same information could be used to plan escape routes from a room, or predict enemy entry points. I''m not really interested in how the information gets used, but this was one idea along the lines of environmental analysis that I had. Others we more basic: perform image understanding to identify objects in a field of view (2D projection of 3D space) and then pass those objects off to the AI for classification and action.

I hope this makes my intentions/desires a little more clear.

Cheers,

Timkin