Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Mark A. Young

dblp:28/5756 · DBLP profile ↗
← Back
3ranked-venue papers
3as first author
0since 2021 · last 1993
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 3 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
1 paper
Knowledge representation and reasoning · 100%
Theoretical computer science
1 paper
Logic in computer science · 100%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › nonmonotonic reasoning
default logic
0.011993
A Logical Semantics for Nonmonotonic Sorts · ACL 1993
Knowledge, reasoning and agents › Knowledge representation and reasoning › representation language › knowledge representation formalisms › knowledge representation language
feature structures
0.011993
A Logical Semantics for Nonmonotonic Sorts · ACL 1993
Knowledge, reasoning and agents › Knowledge representation and reasoning
nonmonotonic reasoning
0.011993
A Logical Semantics for Nonmonotonic Sorts · ACL 1993
Logic in computer science
nonmonotonic reasoning
0.011992
Nonmonotonic Sorts for Feature Structures · AAAI 1992

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

reiter's default logic · 0.0kasper-rounds logic · 0.0sort theory · 0.0
YearPublicationVenuePosition
1993 A Logical Semantics for Nonmonotonic Sorts
abstract
Suppose we have a feature system, and we wish to add default values in a well-defined way. We might start with Kasper-Rounds logic, and use Reiter's example to form it into a default logic. Giving a node a default value would be equivalent to saying "if it is consistent for this node to have that value, then it does." Then we could use default theories to describe feature structures. The particular feature structure described would be the structure that supports the extension of the default theory. This is, in effect, what the theory of nonmonotonic sorts gives you. This paper describes how that theory derives from what is described above.
Mark A. Young, William C. Rounds
ACL1
1992 Nonmonotonic Sorts for Feature Structures
Mark A. Young
AAAI1
1991 Determining intended evidence relations in natural language arguments
abstract
When trying to understand a speaker's argument, it is necessary to determine what her claim is and what evidence she provides for it. It is necessary, therefore, to be able to recognize evidence relations in terms of the speaker's beliefs. This paper describes an implementation of anevidence oracle, which tests for evidence between statements and builds a model of the speaker based on the evidence relations found. This implementation is intended to be an advance in the development of practical discourse analysis systems, proposing a basis for verifying certain relationships between utterances. Another contribution of the work is a stratified speaker model which allows for varying levels of acceptance of beliefs attributed to the speaker. Integration of the implemented evidence oracle into a full discourse analyser is presented, together with output illustrating the analysis for several sample arguments. Some extensions of this approach for plan inference are also discussed. Lorsque l'on essaie de comprendre l'argument d'un locuteur, il importe de déterminer la nature de sa prétention et le type d'évidence qui l'accompagne. Par conséquent, il est nécessaire de pouvoir distinguer des relations d'évidence les croyances du locuteur. Cet article décrit la mise en oeuvre d'un oracle qui recherche l'évidence entre des énoncés et construit un modèle du locuteur en fonction des relations d'évidence constatées. Cette mise en oeuvre propose une base pour vérifier certaines relations entre des énoncés; elle se veut une contribution au développement d'un système pratique d'analyse du discours. Une autre contribution de cette recherche est l'élaboration d'un modèle de locuteur stratifyé qui tient compte de niveaux variables d'acceptation des croyances attributeés au locuteur. l'intégration de l'oracle d'évidence sous forme d'analyseur de discours est présentée, ainsi que des illustrations de l' analyse de plusieurs arguments types. Une extension de cette approche à l'inférence de plans est également discutée.
Mark A. Young, Robin Cohen
Comput. Intell.1