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Learning Problem Solving (Artificial Intelligence, Machine).

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Learning Problem Solving (Artificial Intelligence, Machine)./
Author:	Porter, Bruce Walter.
Description:	1 online resource (177 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 45-07, Section: B.
Contained By:	Dissertations Abstracts International45-07B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781392562956

Learning Problem Solving (Artificial Intelligence, Machine).
Porter, Bruce Walter.

Learning Problem Solving (Artificial Intelligence, Machine). - 1 online resource (177 pages)

Source: Dissertations Abstracts International, Volume: 45-07, Section: B.

Thesis (Ph.D.)--University of California, Irvine, 1984.

Includes bibliographical references

Learning to problem solve requires acquiring multiple forms of knowledge. Problem solving is viewed as a search of a state-space formulation of a problem. With this formalism, operators are applied to states to transit from the initial state to the goal state. The learning task to acquire knowledge of the state-space to guide search. In particular, three forms of knowledge are required: why each operator is useful, when to apply each operator, and what each operator does. A PROLOG implementation, named PET, demonstrates the learning approach in the domains of simultaneous linear equations and symbolic integration. Episodic learning is a technique for learning why individual operators are useful in a solution path. Episodic learning acquires generalized operator sequences which achieve the goal state. This is done by backing-up state evaluation and learning sub-goals in the state-space. Perturbation is a technique for learning when individual operators are useful. Perturbation guides the generalization process to discover minimally-constrained preconditions for useful operator applications. This done by experimentation, thereby reducing the teacher's role in the learning process. Learning relational models is a technique for discovering what individual operators do. Relational models are an explicit representation of the transformation performed by operators. This representation enables the learning element to reason with operator semantics to guide further learning. Episodic learning, perturbation and relational models form an integrated approach for learning problem solving. The approach demonstrates self-teaching by reasoned experimentation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9781392562956Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Learning Problem Solving (Artificial Intelligence, Machine).
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245 1 0 $a Learning Problem Solving (Artificial Intelligence, Machine).
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500 $a Source: Dissertations Abstracts International, Volume: 45-07, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
502 $a Thesis (Ph.D.)--University of California, Irvine, 1984.
504 $a Includes bibliographical references
520 $a Learning to problem solve requires acquiring multiple forms of knowledge. Problem solving is viewed as a search of a state-space formulation of a problem. With this formalism, operators are applied to states to transit from the initial state to the goal state. The learning task to acquire knowledge of the state-space to guide search. In particular, three forms of knowledge are required: why each operator is useful, when to apply each operator, and what each operator does. A PROLOG implementation, named PET, demonstrates the learning approach in the domains of simultaneous linear equations and symbolic integration. Episodic learning is a technique for learning why individual operators are useful in a solution path. Episodic learning acquires generalized operator sequences which achieve the goal state. This is done by backing-up state evaluation and learning sub-goals in the state-space. Perturbation is a technique for learning when individual operators are useful. Perturbation guides the generalization process to discover minimally-constrained preconditions for useful operator applications. This done by experimentation, thereby reducing the teacher's role in the learning process. Learning relational models is a technique for discovering what individual operators do. Relational models are an explicit representation of the transformation performed by operators. This representation enables the learning element to reason with operator semantics to guide further learning. Episodic learning, perturbation and relational models form an integrated approach for learning problem solving. The approach demonstrates self-teaching by reasoned experimentation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
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710 2 $a University of California, Irvine. $3 1184542
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