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Let's take a look at an off-topic issue in this article: gaming. Why is it even worth to talk about gaming? First of all, gaming is the first decision driven activity a human can do. Second, gaming is a huge economic factor of today's digital business. Third, everybody loves to play. So, let's take a look at challenges to the developers of computer games. One big challenge to the game developers of modern computer games is to create opponents that are challenging the player - but never too far. Always matching a grade of difficulty that evokes a kind of thrill to the player without being hopeless to beat the computer player. But why is it a challenge? Well, it is something that affects the player directly - and a frustrated player will be a hard judge to the game developer or publisher. We could say: adequate opponents in games are a kind of game quality. Computers can always easily defeat up to 98% of human players For a computer, it is no problem to beat a hum

As introduces atn PAM 11 in Trier: The Python-Logger-Class. This is the main class for my Raspberry Pi 3 machine learning experiment. Please consider, that three more classes are required: The QMC5883 driver class , the pushbutton class and the blinker class . Furthermore, the R script for the training of the neuronal net is required. ######################################## # # Python Magnetic Field Logger and Detector # by Sebastian Folz # Introduced on Pi And More 11 # ######################################## # Don't use time module, because there is no valid time without WiFi import time import sqlite3 as lite from qmc5883 import QMC5883 import statistics # import functions for system calls from subprocess import call import sys # import pushbutton class from pushbutton import pushbutton, Push # import blink class from Blinker import Blink1, Blink2 from enum import Enum import RPi.GPIO as GPIO # fix copy bug with switched argume