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Data‐Driven Discovery of Physical Laws
Author(s) -
Langley Pat
Publication year - 1981
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6708.1981.tb00869.x
Subject(s) - generality , heuristics , law , kepler , newton's laws of motion , physical law , ohm's law , mathematics , computer science , mathematical economics , theoretical physics , ohm , epistemology , psychology , physics , classical mechanics , philosophy , stars , quantum mechanics , political science , computer vision , psychotherapist , mathematical optimization
BACON.3 is a production system that discovers empirical laws. Although it does not attempt to model the human discovery process in detail, it incorporates some general heuristics that can lead to discovery in a number of domains. The main heuristics detect constancies and trends in data, and lead to the formulation of hypotheses and the definition of theoretical terms. Rather than making a hard distinction between data and hypotheses, the program represents information at varying levels of description. The lowest levels correspond to direct observations, while the highest correspond to hypotheses that explain everything so for observed. To take advantage of this representation, BACON.3 has the ability to carry out and relate multiple experiments, collapse hypotheses with identical conditions, ignore differences to let similar concepts be treated as equal, and to discover and ignore irrelevant variables. BACON.3 has shown its generality by rediscovering versions of the ideal gas law, Kepler's third law of planetary motion, Coulomb's law, Ohm's law, and Galileo's laws for the pendulum and constant acceleration.