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James W. Goodwin

dblp:47/1975 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 1989
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Logic in computer science · 100%
Software engineering, system software, and programming languages
1 paper
Programming languages and type systems · 100%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Logic in computer science
nonmonotonic reasoning		 0.011985
A Process Theory of Non-monotonic Inference · IJCAI 1985
Knowledge, reasoning and agents › Knowledge representation and reasoning
nonmonotonic reasoning		 0.011985
A Process Theory of Non-monotonic Inference · IJCAI 1985
Programming languages and type systems
abstract data types		 0.011981
Why Programming Environments Need Dynamic Data Types · IEEE Trans. Software Eng. 1981
Programming languages and type systems
programming environment		 0.011981
Why Programming Environments Need Dynamic Data Types · IEEE Trans. Software Eng. 1981

Methods — techniques the papers use, named apart from their topics

process theory · 0.0
YearPublicationVenuePosition
1989 Uncertainty and Incompleteness: Breaking the Symmetry of Defeasible Reasoning
Piero P. Bonissone, David A. Cyrluk, James W. Goodwin, Jonathan Stillman
 UAI3
1985 A Process Theory of Non-monotonic Inference
James W. Goodwin
 IJCAI1
1981 Why Programming Environments Need Dynamic Data Types
abstract
Data abstraction is a powerful source of program structure and abstraction. There is nothing about it, or the reasons why it works, which restricts it to static applications. Programming environments (PE's) especially need to use data types dynamically, since it is their function to support the programmer in all phases of work with a program. Thus, the PE must proceed smoothly from definition to use to editing of a type.
James W. Goodwin
 IEEE Trans. Software Eng.1