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Learning in MathematicallyBased Domains
A DomainIndependent Approach
An Empirical Analysis of ExplanationBased Learning
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acquired rules additional analyzing approach arbitrary number autonomous mode bagger system blockers blocks world cancellation graph collection COMMON LISP concepts consequent constraints construct contains DeMorgan's Law determine disjuncts domain theory efficiency eggs eliminated equation schemata experiments explanation structure expression external forces Figure final focus rule formulae goal Hence implementation inference rules instantiation inter-object forces INTERLISP intermediate involves knowledge learned rule learning systems left-hand side machine learning mathematical momentum moved multiple NO-LEARN node object ObjectsInWorld obstacleSet operator performance physics i0i preconditions predicate predicate calculus presents primary obstacles problem solver Problem-Solving Schema produces proof recurrence recursive requires rule applications rules learned sample problem satisfy secondary obstacles sequence sequential rule situation calculus special-case specific example specific problem standard explanation-based learning stdebl step strategy structure of explanations subexplanations table2 teacher's solution technique tower-building training mode unknown unwindable rules variables vector