A Learning Apprentice System for VLSI Design

Mitchell, Tom M.; Mahadevan, Sridhar; Steinberg, Louis I.

doi:10.1007/978-1-4613-2279-5_44

Cited by 79 publications

(81 citation statements)

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“…Because these methods are based on searching for features that are common to the training examples, we shall refer to them as similarity-based generalization methods. 1 In recent years, a number of researchers have proposed generalization methods that contrast sharply with these data-intensive, similarity-based methods (e.g., Borgida et al, 1985;DeJong, 1983;Kedar-Cabelli, 1985;Keller, 1983;Lebowitz, 1985;Mahadevan, 1985;Minton, 1984;Mitchell, 1983;Mitchell et al, 1985;O'Rorke, 1984;Salzberg & Atkinson, 1984;Schank, 1982;Silver, 1983;Utgoff, 1983;Winston et al, 1983). Rather than relying on many training examples and an inductive bias to constrain the search for a correct generalization, these more recent methods constrain the search by relying on knowledge of the task domain and of the concept under study.…”

Section: Introductionmentioning

confidence: 99%

Explanation-Based Generalization: A Unifying View

Mitchell¹,

Keller²,

Kedar-Cabelli³

1988

Readings in Cognitive Science

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293

481

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Abstract. The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to formulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area.

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Section: Introductionmentioning

confidence: 99%

Explanation-Based Generalization: A Unifying View

Mitchell¹,

Keller²,

Kedar-Cabelli³

1988

Readings in Cognitive Science

Self Cite

293

481

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“…Three good examples of exploratory research papers are (1) Amarel's [1968] paper on reformulation in the missionaries-and-cannibals problem, (2) the first paper describing LEX [Mitchell, Utgoff, & Banerji, 1983], and (3) the first LEAP paper [Mitchell, Mahadevan, & Steinberg, 1985]. Each of these papers described either hand examples or partial implementations of systems.…”

Section: The Role Of Machine Learningmentioning

confidence: 99%

Editorial Exploratory research in machine learning

Dietterich¹

1990

Mach Learn

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“…However, it has not been until now that the concept of CAD tools as learning assistants, as well as design assistants, have been considered [although "learning apprentices" have been discussed (Mitchell et al, 1985)]. This paper presents and explores the possibility of realizing learning assistance in Int.CAD and focuses on discussing how to empower an IDA with learning assistance.…”

Section: Introductionmentioning

confidence: 99%

Sharing the learning activity using intelligent CAD

Duffy

1996

AIEDAM

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In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing "learning" assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. "Controlled" computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of "controlled" computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD.

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A Learning Apprentice System for VLSI Design

Cited by 79 publications

References 0 publications

Explanation-Based Generalization: A Unifying View

Explanation-Based Generalization: A Unifying View

Editorial Exploratory research in machine learning

Sharing the learning activity using intelligent CAD

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