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How should I start designing an AI algorithm for an artillery warfare game?

Asked 2009-01-10T19:28:35.157
19

Here's the background... in my free time I'm designing an artillery warfare game called Staker (inspired by the old BASIC games Tank Wars and Scorched Earth) and I'm programming it in MATLAB. Your first thought might be "Why MATLAB? There are plenty of other languages/software packages that are better for game design." And you would be right. However, I'm a dork and I'm interested in learning the nuts and bolts of how you would design a game from the ground up, so I don't necessarily want to use anything with prefab modules. Also, I've used MATLAB for years and I like the challenge of doing things with it that others haven't really tried to do.

Now to the problem at hand: I want to incorporate AI so that the player can go up against the computer. I've only just started thinking about how to design the algorithm to choose an azimuth angle, elevation angle, and projectile velocity to hit a target, and then adjust them each turn. I feel like maybe I've been overthinking the problem and trying to make the AI too complex at the outset, so I thought I'd pause and ask the community here for ideas about how they would design an algorithm.

Some specific questions:

  1. Are there specific references for AI design that you would suggest I check out?

  2. Would you design the AI players to vary in difficulty in a continuous manner (a difficulty of 0 (easy) to 1 (hard), all still using the same general algorithm) or would you design specific algorithms for a discrete number of AI players (like an easy enemy that fires in random directions or a hard enemy that is able to account for the effects of wind)?

  3. What sorts of mathematical algorithms (pseudocode description) would you start with?

Some additional info: the model I use to simulate projectil

language-agnostic artificial-intelligence
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2 Answers

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A difference between artillery games and real artillery situations is that all sides have 100% information, and that there are typically more than 2 opponents.

As a result, your evaluation function should consider which opponent it would be more urgent to try and eliminate. For example, if I have an easy kill at 90%, but a 50% chance on someone who's trying to kill me and just missed two shots near me, it's more important to deal with that chance.

I think you would need some way of evaluating the risk everyone poses to you in terms of ammunition, location, activity, past history, etc.

answered 2009-01-10T22:01:01.573
3

I'm now addressing the response you posted:

While you have the general idea I don't believe your approach will be workable--it's going to converge way too fast even for a low value of nSims. I doubt you want more than one iteration of get_new_guess between shells and it very well might need some randomizing beyond that.

Even if you can use multiple iterations they wouldn't be good at making a continuously increasing difficulty as they will be big steps. It seems to me that difficulty must be handled by randomness.

First, get_initial_guess:

To start out I would have a table that divides the world up into zones--the higher the difficulty the more zones. The borders between these zones would have precalculated power for 45, 60 & 75 degrees. Do a test plot, if a shell smacks terrain try again at a higher angle--if 75 hits terrain use it anyway.

The initial shell should be fired at a random power between the values given for the low and high bounds.

Now, for get_new_guess:

Did the shell hit terrain? Increase the angle. I think there will be a constant ratio of how much power needs to be increased to maintain the same distance--you'll need to run tests on this.

Assuming it didn't smack a mountain, note if it's short or long. This gives you a bound. The new guess is somewhere between the two bounds (if you're missing a bound, use the value from the table in get_initial_guess in it's place.)

Note what percentage of the way between the low and high bound impact points the target is and choose a power that far between the low and high bound power.

This is probably far too accurate and will likely require some randomizing. I've changed my mind about adding a simple random %. Rather, multiple random numbers should be used to get a bell curve.

answered 2009-01-11T00:38:58.247

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