A search party combs a foggy field. At first, people spread out wide: a high 'temperature' of search means a great variety of attempts. Upon finding a trace, they close in. But the speed of convergence is critical — narrow the circle too early, and the best find stays aside. The coordinator assesses the spread of searchers and commands when to speed up or slow down.
In robotics, such a coordinator is entropy — a measure of uncertainty and diversity. The algorithm generates hundreds of possible routes and measures their spread in real time. As long as the options are as colorful as a spectroscopy spectrum, cooling is accelerated. When the spread drops, the algorithm slows down to avoid getting stuck in a dead end — much like the search for dark matter: we don't see it but catch hints.
Entropy was born from thoughts about steam engines and now helps robots avoid getting stuck in labyrinths — truly a universal advisor.
In movies, robots like the Terminator sift through millions of options instantly. The proposed method does it more efficiently: the machine doesn't sift through everything indiscriminately; rather, by heeding entropy's signal, it cleverly narrows the set of guesses.