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In contrast to the knowledge-intensive approach of Meta-DENDRAL, [[Ross Quinlan]] invented a ___domain-independent approach to statistical classification, [[decision tree learning]], starting first with [[ID3 algorithm|ID3]]<ref>{{harvc|in1=Michalski|in2=Carbonell|in3=Mitchell|year=1983|c=Chapter 15: Learning Efficient Classification Procedures and their Application to Chess End Games |first=J. Ross |last=Quinlan}}</ref> and then later extending its capabilities to [[C4.5]].<ref>{{Cite book| edition = 1st | publisher = Morgan Kaufmann| isbn = 978-1-55860-238-0| last = Quinlan| first = J. Ross| title = C4.5: Programs for Machine Learning| ___location = San Mateo, Calif| date = 1992-10-15}}</ref> The decision trees created are [[glass box]], interpretable classifiers, with human-interpretable classification rules.
Advances were made in understanding machine learning theory, too. [[Tom M. Mitchell|Tom Mitchell]] introduced [[version space learning]] which describes learning as a search through a space of hypotheses, with upper, more general, and lower, more specific, boundaries encompassing all viable hypotheses consistent with the examples seen so far.<ref>{{harvc|in1=Michalski|in2=Carbonell|in3=Mitchell|year=1983 |c=Chapter 6: Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics |first1=Tom M. |last1=Mitchell |first2=Paul E. |last2=Utgoff |first3=Ranan |last3=Banerji}}</ref> More formally, [[Leslie Valiant|Valiant]] introduced [[Probably approximately correct learning|Probably Approximately Correct Learning]] (PAC Learning), a framework for the mathematical analysis of machine learning.<ref>{{Cite journal| doi = 10.1145/1968.1972| issn = 0001-0782| volume = 27| issue = 11| pages = 1134–1142| last = Valiant| first = L. G.| title = A theory of the learnable| journal = Communications of the ACM| date = 1984-11-05| s2cid = 12837541| doi-access = free}}</ref>
Symbolic machine learning encompassed more than learning by example. E.g., [[John Robert Anderson (psychologist)|John Anderson]] provided a [[cognitive model]] of human learning where skill practice results in a compilation of rules from a declarative format to a procedural format with his [[ACT-R]] [[cognitive architecture]]. For example, a student might learn to apply "Supplementary angles are two angles whose measures sum 180 degrees" as several different procedural rules. E.g., one rule might say that if X and Y are supplementary and you know X, then Y will be 180 - X. He called his approach "knowledge compilation". [[ACT-R]] has been used successfully to model aspects of human cognition, such as learning and retention. ACT-R is also used in [[intelligent tutoring systems]], called [[cognitive tutors]], to successfully teach geometry, computer programming, and algebra to school children.<ref "pump"="">{{Cite journal| volume = 8| pages = 30–43| last1 = Koedinger| first1 = K. R.| last2 = Anderson| first2 = J. R.| last3 = Hadley| first3 = W. H.| last4 = Mark| first4 = M. A.| last5 = others| title = Intelligent tutoring goes to school in the big city| journal = International Journal of Artificial Intelligence in Education (IJAIED)| accessdate = 2012-08-18| date = 1997| url = http://telearn.archives-ouvertes.fr/hal-00197383/}}</ref>
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