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About ater1980

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  1. ater1980

    Penalized/Constrained Distance Function

    Ok thanks. Does it work if enemies perform eandom walk on the grid?
  2. ater1980

    Penalized/Constrained Distance Function

    Can you suggest some articles or manuals on this topic? I really have never done this before.
  3. ater1980

    Penalized/Constrained Distance Function

    what you say makes sense, but honestly it's not my impression: for c<1 P pretty much ignores enemies regardless of the distance to them(or so far I haven't noticed much strategy in the trajectory), but for c>1 it just stays in one spot for a long time scared to death by them. I'm quite sure there should be some systematic way people design these ditance functions.
  4. ater1980

    Penalized/Constrained Distance Function

    Simple evolutionary algorithm. A number of candiate solutions are generated base on the current one. Next step is to attach a fitness/objective function to each of them and then derive probability distribution. The second step is a clincher for me since I don't know how to do find this fitness function. Currently I'm just using d1-c*d2, which I guess is a pretty rough way of doing it.
  5. Assume a character is located on a n by n grid and has to reach a certain entry on that grid. Its current position is (x1,y1). Also on the same grid is an enemy with coordinates (x2,y2). Each step algorithm randomly generates new candidate locations for the hero (if there are k candidates then there is a kx2 matrix of new potential locations. What I need is some distance objective function to compare the candidates. I'm currently using d1 - c * d2, where d1 is distance to the objective (measure in terms of number of pixels for each axis), d2 is distance to the enemy and c is some coefficient (this is very much like a set-up for Lagrangian). It's not working very well though. I'd be quite keen to learn how what constrained distance function are used for similar cases. Any suggestions or references to articles are very much appreciated.
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