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

Tran Hoai Nam

dblp:45/526 · DBLP profile ↗
← Back
4ranked-venue papers
3as first author
0since 2021 · last 2006
—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 · 2 · 2 first-authorTheory of computation · 2 · 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
3 papers
Knowledge representation and reasoning · 70% Information extraction and text analysis · 16% Probabilistic and Bayesian machine learning · 14%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 10 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › temporal information extraction
temporal ordering
0.112005
Issues in Reasoning about Interaction Networks in Cells: Necessity of Event Ordering Knowledge · AAAI 2005
Bioinformatics and computational biology › biological network › network biology
interaction networks
0.112005
Issues in Reasoning about Interaction Networks in Cells: Necessity of Event Ordering Knowledge · AAAI 2005
Bioinformatics and computational biology
systems biology
0.112005
Issues in Reasoning about Interaction Networks in Cells: Necessity of Event Ordering Knowledge · AAAI 2005
Knowledge, reasoning and agents › Knowledge representation and reasoning
action and change
0.012004
Reasoning about Triggered Actions in AnsProlog and Its Application to Molecular Interactions in Cells · KR 2004
Knowledge, reasoning and agents › Knowledge representation and reasoning › reasoning about action and change › reasoning about actions
action languages
0.012004
Encoding Probabilistic Causal Model in Probabilistic Action Language · AAAI 2004
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
answer set programming
0.012004
Reasoning about Triggered Actions in AnsProlog and Its Application to Molecular Interactions in Cells · KR 2004
Knowledge, reasoning and agents › Knowledge representation and reasoning
causal reasoning
0.012004
Encoding Probabilistic Causal Model in Probabilistic Action Language · AAAI 2004
Knowledge, reasoning and agents › Knowledge representation and reasoning
logic programming
0.012004
Reasoning about Triggered Actions in AnsProlog and Its Application to Molecular Interactions in Cells · KR 2004
Machine learning › Probabilistic and Bayesian machine learning › causal inference › causal model
probabilistic causal models
0.012004
Encoding Probabilistic Causal Model in Probabilistic Action Language · AAAI 2004
Bioinformatics and computational biology
molecular interaction
0.012004
Reasoning about Triggered Actions in AnsProlog and Its Application to Molecular Interactions in Cells · KR 2004
YearPublicationVenuePosition
2006 Domain-dependent knowledge in answer set planning
abstract
In this article we consider three different kinds of domain-dependent control knowledge (temporal, procedural and HTN-based) that are useful in planning. Our approach is declarative and relies on the language of logic programming with answer set semantics (AnsProlog*). AnsProlog* is designed to plan without control knowledge. We show how temporal, procedural and HTN-based control knowledge can be incorporated into AnsProlog* by the modular addition of a small number of domain-dependent rules, without the need to modify the planner. We formally prove the correctness of our planner, both in the absence and presence of the control knowledge. Finally, we perform some initial experimentation that demonstrates the potential reduction in planning time that can be achieved when procedural domain knowledge is used to solve planning problems with large plan length.
Tran Cao Son, Chitta Baral, Tran Hoai Nam, Sheila A. McIlraith
ACM Trans. Comput. Log.3
2005 Issues in Reasoning about Interaction Networks in Cells: Necessity of Event Ordering Knowledge
Tran Hoai Nam, Chitta Baral, Carron Shankland
AAAI1
2004 Encoding Probabilistic Causal Model in Probabilistic Action Language
Tran Hoai Nam, Chitta Baral
AAAI1
2004 Reasoning about Triggered Actions in AnsProlog and Its Application to Molecular Interactions in Cells
Tran Hoai Nam, Chitta Baral
KR1