Existing machine learning techniques have only limited capabilities of handling computationally intractable domains. This research extends explanation-based learning techniques in order to overcome such limitations. It is based on a strategy of sacrificing theory accuracy in order to gain tractability. Intractable theories are approximated by incorporating simplifying assumptions. Explanations of teacher-provided examples are used to guide a search for accurate approximate theories. The paper begins with an overview of this learning technique. Then a typology of simplifying assumptions is presented along with a technique for representing such assumptions in terms of generic functions. Methods for generating and searching a space of approxim...
In this paper we investigate inductive inference identification criteria which permit infinitely man...
The effect of a partial explanation as additional information in the learning process is investigate...
Many AI problem solvers possess explicitly encoded knowledge - a domain theory ““ that they use to s...
Existing machine learning programs possess only limited abilities to exploit previously acquired bac...
A powerful new technique for learning to solve intractable problems is presented in this dissertatio...
Similarity-based learning, which involves largely structural comparisons of instances, and explanati...
A domain independent mechanism for generating heuristics for intractable theories has been implement...
This paper describes an explanation-based approach lo learning plans despite a computationally intra...
Modern applications of machine teaching for humans often involve domain-specific, non- trivial targe...
Inductive learning, which involves largely structural comparisons of examples, and explanation-based...
290 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1988.Explanation-based learning is...
Two disparate machine learning approaches have received considerable attention. These are explanatio...
AbstractThis paper studies the approximation of the set of minimal implicates and the effect this ap...
The use of approximation as a method for dealing with com-plex problems is a fundamental research is...
AbstractThe effect of a partial explanation as additional information in the learning process is inv...
In this paper we investigate inductive inference identification criteria which permit infinitely man...
The effect of a partial explanation as additional information in the learning process is investigate...
Many AI problem solvers possess explicitly encoded knowledge - a domain theory ““ that they use to s...
Existing machine learning programs possess only limited abilities to exploit previously acquired bac...
A powerful new technique for learning to solve intractable problems is presented in this dissertatio...
Similarity-based learning, which involves largely structural comparisons of instances, and explanati...
A domain independent mechanism for generating heuristics for intractable theories has been implement...
This paper describes an explanation-based approach lo learning plans despite a computationally intra...
Modern applications of machine teaching for humans often involve domain-specific, non- trivial targe...
Inductive learning, which involves largely structural comparisons of examples, and explanation-based...
290 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1988.Explanation-based learning is...
Two disparate machine learning approaches have received considerable attention. These are explanatio...
AbstractThis paper studies the approximation of the set of minimal implicates and the effect this ap...
The use of approximation as a method for dealing with com-plex problems is a fundamental research is...
AbstractThe effect of a partial explanation as additional information in the learning process is inv...
In this paper we investigate inductive inference identification criteria which permit infinitely man...
The effect of a partial explanation as additional information in the learning process is investigate...
Many AI problem solvers possess explicitly encoded knowledge - a domain theory ““ that they use to s...