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Margot Flowers

dblp:63/4755 · DBLP profile ↗
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13ranked-venue papers
1as first author
0since 2021 · last 1990
—ORCID · none

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

Artificial intelligence and machine learning · 12 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 8 · 1 first-authorDatabases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 1

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
8 papers
Knowledge representation and reasoning · 66% Information extraction and text analysis · 22% Learning theory · 12%
Theoretical computer science
2 papers
Logic in computer science · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 9 heaviest of 12, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
causal reasoning
0.021987
The Role of Prior Causal Theories in Generalization · AAAI 1986
Using Prior Learning to Facilitate the Learning of New Causal Theories · IJCAI 1987
Knowledge, reasoning and agents › Knowledge representation and reasoning
qualitative reasoning
0.011987
Naive Mechanics Comprehension and Invention in EDISON · IJCAI 1987
Natural language and speech › Information extraction and text analysis
argument mining
0.011986
Editorial Comprehension in OpEd through Argument Units · AAAI 1986
Machine learning › Learning theory
generalization
0.011986
The Role of Prior Causal Theories in Generalization · AAAI 1986
Knowledge, reasoning and agents › Knowledge representation and reasoning
argumentation
0.021981
Opportunistic Processing in Arguments · IJCAI 1981
Towards an AI Model of Argumentation · AAAI 1980
Knowledge, reasoning and agents › Knowledge representation and reasoning
inconsistency handling
0.011982
On Being Contradictory · AAAI 1982
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge base
inconsistent knowledge bases
0.011982
On Being Contradictory · AAAI 1982
Logic in computer science › philosophical logic › non-classical logic
paraconsistent logic
0.011982
On Being Contradictory · AAAI 1982
Computational social science and digital humanities › computational linguistics
opinion mining
0.011986
Editorial Comprehension in OpEd through Argument Units · AAAI 1986

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

invention · 0.0comprehension · 0.0natural language understanding · 0.0
YearPublicationVenuePosition
1990 Argument representation for editorial text
Sergio Alvarado, Michael G. Dyer, Margot Flowers
Knowl. Based Syst.3
1990 Argument comprehension and retrieval for editorial text
Sergio J. Alvarado, Michael G. Dyer, Margot Flowers
Knowl. Based Syst.3
1988 Recognizing and Responding to Plan-Oriented Misconceptions
Alex Quilici, Michael G. Dyer, Margot Flowers
Comput. Linguistics3
1987 Precedent-Based Legal Reasoning and Knowledge Acquisition in Contract Law: A Process Model
abstract
In the law, decisions in previous cases play a significant role in the presentation, understanding, and outcome of new cases. This is particularly true in the area of contract law where few statutes (explicit legal rules) exist. When presented with a new case, a lawyer must be able to identify important issues and make some predictions about how the case might be decided. The lawyer will often recall past cases which bear similarities to the current case and reason analogically to make these predictions. In order to perform these tasks, a lawyer must be able to remember past cases, organize them in memory so that cases that are conceptually similar are stored together (a lawyer normally won't be reminded of an irrelevant case), and make analogies between cases. Thus the organization and representation of knowledge in memory is crucial in building a model of lawyer's cognitive processes. This paper describes a process model, implemented in a computer program called STARE, which addresses these issues in the context of first-year law students learning contract law.
Seth R. Goldman, Michael G. Dyer, Margot Flowers
ICAIL3
1987 Naive Mechanics Comprehension and Invention in EDISON
Michael G. Dyer, Margot Flowers, Jack Hodges
IJCAI2
1987 Using Prior Learning to Facilitate the Learning of New Causal Theories
Michael J. Pazzani, Michael G. Dyer, Margot Flowers
IJCAI3
1986 Editorial Comprehension in OpEd through Argument Units
Sergio Alvarado, Michael G. Dyer, Margot Flowers
AAAI3
1986 The Role of Prior Causal Theories in Generalization
Michael J. Pazzani, Michael G. Dyer, Margot Flowers
AAAI3
1986 EDISON: An engineering design invention system operating naively
Michael G. Dyer, Margot Flowers, Jack Hodges
Artif. Intell. Eng.2
1985 Learning to Understand Contractual Situations
Seth R. Goldman, Michael G. Dyer, Margot Flowers
IJCAI3
1982 On Being Contradictory
Margot Flowers
AAAI1
1981 Opportunistic Processing in Arguments
Rod McGuire, Lawrence Birnbaum, Margot Flowers
IJCAI3
1980 Towards an AI Model of Argumentation
Lawrence Birnbaum, Margot Flowers, Rod McGuire
AAAI2