It's Deep Blue, not Big Blue. The parameters used by its evaluation function were tuned by the system on games played by human masters.
But it's a mistake to think that a system learning by playing against itself is something new. Arthur Samuel's draughts (chequers) program did that in 1959.
It's not that it's new, it's that they've achieved it. Chess was orders of magnitude harder than draughts. The solution for draughts didn't scale to chess but Alpha Go zero showed that chess was ridiculously easy for it once it had learned Go.
Both Samuel's chequer's program and Deep Blue used alpha-beta pruning for search, and a heuristic function. Deep Blue's heuristic function was necessarily more complex because chess is more complex than draughts. I think the reason master chess games were used in Deep Blue instead of self-play was the existence of a large database of such games, and because so much of its performance was the result of being able to look ahead so far.
But it's a mistake to think that a system learning by playing against itself is something new. Arthur Samuel's draughts (chequers) program did that in 1959.
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