And this only takes into account Chess. General Game Playing (referenced by a sibling comment) allows in its simplest version to encode all deterministic games

And this only takes into account Chess. General Game Playing (referenced by a sibling comment) allows in its simplest version to encode all deterministic games with complete information (which is an infinite set but includes Rock Paper Scissors, Connect 4, Checkers, Chess, Go, ...). Now finding heuristics there given only the game description is a completely different set of problem but for quite a few Chess-like games it boils down to valuing pieces (proving that something is a piece is a problem by itself), positions and combinations thereof which again all are derived of "mobility", i.e., the number of possible moves in a given situation. In that sense, piece value is a mere "maximum mobility" indicator per piece. Positioning is a "real mobility indicator" but not only affecting your own mobility but also reducing your opponents. Check is then a mere reduction of your opponents mobility, promotion is a great enhancement of your piece value and castling a way of reducing the chance of "immobilizing your position" via check. TLDR: Machine Learning aside, you could also deduce all Chess heuristics from first principles.