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Gerald R. Barber

dblp:77/6055 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1983
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 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.

Artificial intelligence
2 papers
Knowledge representation and reasoning · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › reasoning about action and change
reasoning about change
0.011980
Reasoning about Change in Knowledgeable Office Systems · AAAI 1980
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology
0.011980
Reasoning about Change in Knowledgeable Office Systems · AAAI 1980

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

viewpoint mechanism · 0.0knowledge embedding · 0.0
YearPublicationVenuePosition
1983 Supporting Organizational Problem Solving with a Work Station
abstract
Problem SolvingAn approach to supporting work in the office is described.Using and extending ideas from the field of artificial intelligence (AI) we describe office work as a problem-solving activity.A knowledgeembedding language called OMEGA is used to embed knowledge of the organization into an office worker's work station in order to support the office worker in problem solving.A particular approach to reasoning about change and contradiction is discussed.This approach uses OMEGA's viewpoint mechanism, which is a general contradiction-handling facility.Unlike other knowledge representation systems, when a contradiction is reached the reasons for the contradiction can be analyzed by the deduction mechanism without having to resort to search mechanisms such as a backtracking.The viewpoint mechanism is the heart of the problem-solving support paradigm, a paradigm which supplements the classical AI view of problem solving.An example is presented in which OMEGA's facilities are used to support an office worker's problem-solving activities.The example illustrates the use of viewpoints and of OMEGA's capabilities to reason about its own reasoning processes.
Gerald R. Barber
ACM Trans. Inf. Syst.1
1980 Reasoning about Change in Knowledgeable Office Systems
Gerald R. Barber
AAAI1