Computer science is an academic discipline with a long tradition. Its core elements are programming languages and algorithms. .Endless amount of source code was written in multiple programming languages, there are even esoteric languages available like Intercal and Befunge which are examples for computer art. Unfortunately, computer science consists of a seldom described weakness which are np hard problem.
An np hard problem is a mathematical challenge which can't be solved with existing programming languages nor algorithms. That means, modern languages like C++ in combination with modern 64bit operating systems like Linux isn't powerful enough to determine the shortest route for 15 cities or find the shortest sequence for solving the rubik's cube puzzle. What makes the situation more complicated is, that even after adding more RAM to a computer, np hard problems remain unsolvable.
One possible explanation why computer science is ignoring np hard problems might be, that its useless to invest ressources into problems, which can't be solved. If 100 researchers in the past have independent from each other tried to solve the Traveling salesman problem, and all of them have failed, it doesn't make much sense if additional researchers are trying to solve the same problem. .np hard problems can be compared to the perpetuum mobile in mechanical engineering which is also described as a dead end.
On the other hand there is a discipline available which is devoted to np hard problems. This discipline is called Artificial intelligence and its main problem is to solve all these unsolvable problems like rubik's cube, path planning and motion planning. Its important the difference between computer science which is only about polynomial problems vs. Artificial intelligence which is only about np hard problems. Both disciplines are trying to achieve different goals and they are operating with opposite tools.
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