Fire like will

This is pretty much the logic of the chase in lesson21 within the windowed quote below, and I wouldn't expect it to be very sophisticated as it is just an example. The SDL sound works and the openGL is without err that I see. It uses fine position and integer position to define the interaction and it uses a number rather than a #define and I will certainly grep and sed that out or actually I think I will leave everything as it is and rip this section out to a separate file that does the hunting and escape logic. I hate fighting the computer when it has advantage and so I will let the computer struggle with itself and see how to improve the wolf and prey in stages so they continually increase their talent.

The goal is to have a hunt and avoid that is effective. I see the avoidance of the hunter as time when applied to a real scenario and it is adjusted to force the computer to make choices that are less than optimal but act in real time as you could possibly make it perfect in its choice, but it might be doomsday before it decided and acted. Life requires action in context and this hunter is the fire that drives the beast to act. This should be relatively easy to implement as a malloc'd tree

The balance of the drive and search should ultimately yield a strategy that is optimum for the particular situation and the talent of the seeker. I see now there won't be much of the original game left when I am through as window code and SDL and time and randomness is something I have already implemented and I mainly wanted to see this below and analyze it as a predator prey relationship.

        if (!gameOver) {
            for (loop1 = 0; loop1 < (stage * level); loop1++) {
                if ((enemy[loop1].x < player.x) &&
                    (enemy[loop1].fy == enemy[loop1].y * 40)) {
                    enemy[loop1].x++;
                }

The code should be very obvious as fy is "fine" y or dots on the screen for smooth movement and the integers are positions on the limited grid. The array indexed [loopl] simply allows a structure for each predator when there are multiple predators and a single prey. I am thinking now that I might experiment with the nature of pack behavior for some insight into that also.

I am definitely ripping everything out of this in concept and using my own code as it suffers the same issues as `einstein` like failure to scale, things that should be variable are numbers instead of even define and too many printfs to indicate things that are obvious. I really am only left with a concept and an intent and "Y Meistr"," Y Blaidd Ofn".

So I will simply incorporate the concepts in my code and perhaps that is all I wanted out of this is a framework to consider "drive" and real time in the context of finding solutions in an n-Dimensional probabilistic decision tree structure.

This looks like a lot of fun and the implementation of expanding and contracting decision trees in many dimensions with an underlying matrix construction should be very entertaining when displayed in OpenGL and rotated through dimensions. I can retrofit that to the `einstein` like puzzle with probabilities set at 100% since that system is unambiguous in its solutions. All in all it looks to be very informative in what I learn about making matrices, Markov chains, trees, and decisions interact in a real world scenario. It needs a web interface to allow it to look for real information and I probably will implement something akin to page rank for my own interests. Probably something that looks for key information about my interests and attempts to correlate them and reduce redundancy using a weighted structure based on my valuation of the general quality of information source.