DISCLAIMER: this is not an "issue", rather a discussion about ex 9.1 (as there is no "correct" solution)
[of course feel free to close the issue if it's inappropriate, I didn't find any other place where to discuss this]
I think that the interesting point in the example in the book is that the assumption that "the opponent is like a rotating turret, equally likely to shoot in any direction", i.e. theta has a uniform distribution, implies that x is not uniform by construction (x ~ tan(theta)). I guess the key point is that P(theta) = uniform implies P(x) ~ 1/StrafingSpeed.
In my solution, to go beyond that assumption, I simply introduced a non uniform distribution of theta, which I made dependent on alpha and beta (long story short: if the shooter is close to a wall, he/she is more likely to shoot away from the wall). In order to implement this in the solution, I made: P(x) ~ P(theta)/StrafingSpeed.
What did other people do? What is the form P(x) in your more realistic case?
Hello!
DISCLAIMER: this is not an "issue", rather a discussion about ex 9.1 (as there is no "correct" solution) [of course feel free to close the issue if it's inappropriate, I didn't find any other place where to discuss this]
Here is my worked out example and solution.
I think that the interesting point in the example in the book is that the assumption that "the opponent is like a rotating turret, equally likely to shoot in any direction", i.e.
thetahas a uniform distribution, implies thatxis not uniform by construction (x~ tan(theta)). I guess the key point is that P(theta) = uniform implies P(x) ~ 1/StrafingSpeed.In my solution, to go beyond that assumption, I simply introduced a non uniform distribution of
theta, which I made dependent onalphaandbeta(long story short: if the shooter is close to a wall, he/she is more likely to shoot away from the wall). In order to implement this in the solution, I made: P(x) ~ P(theta)/StrafingSpeed.What did other people do? What is the form P(
x) in your more realistic case?