  Based on Darwin's natural selection, we developed "machine scientists" to
discover the laws of nature by learning from raw data. "Machine scientists"
construct physical theories by applying a logic tree (state Decision Tree) and
a value tree (observation Function Tree); the logical tree determines the state
of the entity, and the value tree determines the absolute value between the two
observations of the entity. A logic Tree and a value tree together can
reconstruct an entity's trajectory and make predictions about its future
outcomes. Our proposed algorithmic model has an emphasis on machine learning -
where "machine scientists" builds up its experience by being rewarded or
punished for each decision they make - eventually leading to rediscovering
Newton's equation (classical physics) and the Born's rule (quantum mechanics).
