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AI Challenge Forums • View topic - Here is how diffusion looks like in practice

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Here is how diffusion looks like in practice

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Here is how diffusion looks like in practice

Postby agent_smith » Wed Dec 07, 2011 6:29 pm

The topic on diffusion is very popular at this forum and rightly so, because it's a technique well suited for this game. I have read the original paper cited by icefox and implemented diffusion successfully in my bot. It works very well for navigating the map and finding food. Fighting is a slightly different story.

So, to show you how it works, using an AI visualizer:



http://serprankings.com/ants/games/tutorial.html
http://serprankings.com/ants/games/maze_02p_01.html

Notes:
* turn visualizing on using a button in the left upper corner
* both bots are the same
* scent comes from food, currently invisible areas, unexplored areas and enemy hills (if discovered)
* maps only show areas where the scent is above average (for better performance)
* water doesn't conduct scent, it acts as walls
* own ants don't conduct scent either - this way they scatter instead of grouping

Here's a quick and easy intro to collaborative diffusion, it will give you a good idea in a few minutes:
http://scalablegamedesign.cs.colorado.e ... _Diffusion

One important idea to understand is that for practical purposes, you'll want to do a few diffusion passes over the map in each turn. The more passes you do, the quicker the scent travels.
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Re: Here is how diffusion looks like in practice

Postby seylom » Thu Dec 15, 2011 2:21 am

I would like to get some advice about what to do when your algorithm doesn't seem to work.

I have been trying to implement collaborative diffusion for the past three day or two and although I understand the paper (I have read it,) I am failing at bringing to life any kind of behavior for some reason. So I would like to know what I might be doing wrong. First off, I would like to say that I did indeed do a search on collaborative diffusion on the forum and read all the thread about it.

Here are my questions.
- How do you go about choosing what goal values (coefficient) to assign to different goals and how they get updated after each pass of the diffusion loop. Clearly, those who got it working did something that I might be missing. I am not looking for values, but rather, just methods for adjusting cells goal values

- In the paper, they explain the collaborative part of the algorithm with the idea that an agent by not diffusing pacman's scent can block that scent and allow another hunter agents two split from its path and (...). Now what does it mean for our implementation of the algorithm? when in the loop should we update the current cell value in order for the next iteration of the diffusion loop to dampen the cell value? And concretely what does the dampening process means for the other adjacent cells?

Any word of advice or explanation will be more than welcome. This is my first ever competition in AI and first real application of pathfinding algorithm and I would really love to learn more from it than I have so far. I hope the nOOb nature of this question won't offend all of the well-established hardcore AI members here.

Thank you.
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Re: Here is how diffusion looks like in practice

Postby deccan » Thu Dec 15, 2011 2:41 am

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Re: Here is how diffusion looks like in practice

Postby seylom » Thu Dec 15, 2011 5:40 am

Thank you for your reply.
Now how does one decide what scent a cell should be emitting predominantly?do we just choose the strongest scent among the agents in the cell? or does each ant may decide what they scent they choose to follow independantly of their values?
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Re: Here is how diffusion looks like in practice

Postby DrClaes » Thu Dec 15, 2011 2:49 pm

Hey and thanks for the nice illustration of the diffusion principle as applied to our ant game.

I'm using algorithms which produce broadly similar results without using the actual diffusion equations; one of the behaviors I found when directing ants "uphill" towards the "nearest unexplored edge" was that some paths, particularly in mazes, get explored by a single (typically one of the first ants) ant, then never gets explored again if it's in the shape of a reasonably long corridor before it branches off.

The same thing can be seen in your second example, where (in my opinion) not enough orange ants move west from the point 40,61, but rather go north and explore that part of the map.

I've attempted to correct for this behavior by making the scent of previously visualized, not currently seen food increase with time, which looks a little bit better in the mazelike maps...

Anyone else notice this behavior and think about another way of approaching it?

Regards /C
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Re: Here is how diffusion looks like in practice

Postby agent_smith » Thu Dec 15, 2011 5:37 pm

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