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

Paul Thagard

dblp:53/3965 · DBLP profile ↗
← Back
8ranked-venue papers
4as first author
0since 2021 · last 2019
0000-0002-6594-4761ORCID · corroborated

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

Artificial intelligence and machine learning · 8 · 4 first-authorApplied, interdisciplinary, general and emerging computing · 3Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 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.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Computational social science and digital humanities · 99% Computational science and engineering · 1%
Artificial intelligence
3 papers
Knowledge representation and reasoning · 64% Learning theory · 28% Planning, search and constraint satisfaction · 8%

Topics — the 3 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
cognitive science
0.112007
Theory and experiment in cognitive science · Artif. Intell. 2007
Knowledge, reasoning and agents › Knowledge representation and reasoning
analogical reasoning
0.011989
Chemical Analogies: Two Kinds of Explanation · IJCAI 1989
Machine learning › Learning theory
inductive inference
0.011985
Discovering the Wave Theory of Sound: Inductive Inference in the Context of Problem Solving · IJCAI 1985

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

constraint satisfaction · 0.0
YearPublicationVenuePosition
2019 How Does Current AI Stack Up Against Human Intelligence?
Kenneth D. Forbus, Dedre Gentner, John E. Laird, Thomas R. Shultz, Ardavan Salehi Nobandegani, Paul Thagard
CogSci6
2018 Models of Human Scientific Discovery
Robert L. Goldstone, Alison Gopnik, Paul Thagard, Tomer D. Ullman
CogSci3
2014 Symposium: Creative Cognition
Will Bridewell, Liane Gabora, David Kirsh, Paul Thagard
CogSci4
2007 Theory and experiment in cognitive science
Paul Thagard
Artif. Intell.1
2005 Changing personalities: towards realistic virtual characters
abstract
Computer modelling of personality and behaviour is becoming increasingly important in many fields of computer science and psychology. Personality and emotion-driven Believable Agents are needed in areas like human–machine interfaces, electronic advertising and, most notably, electronic entertainment. Computer models of personality can help explain personality by illustrating its underlying structure and dynamics. This work presents a neural network model of personality and personality change. The goals are to help understand personality and create more realistic and believable characters for interactive video games. The model is based largely on trait theories of personality. Behaviour in the model results from the interaction of three components: (1) personality-based predispositions for behaviour, (2) moods/emotions and (3) environmental situations. Personality develops gradually over time depending on the situations encountered. Modelling personality change produces interesting and believable virtual characters whose behaviours change in psychologically plausible ways.
Mike Poznanski, Paul Thagard
J. Exp. Theor. Artif. Intell.2
1990 Analog Wetrieval by Constraint Satisfaction
Paul Thagard, Keith J. Holyoak, Greg Nelson, David Gochfeld
Artif. Intell.1
1989 Chemical Analogies: Two Kinds of Explanation
Paul Thagard, Dawn M. Cohen, Keith J. Holyoak
IJCAI1
1985 Discovering the Wave Theory of Sound: Inductive Inference in the Context of Problem Solving
Paul Thagard, Keith J. Holyoak
IJCAI1