Demo Data from EDBT 2026 / reviewers in the wild / expert
Tran Hoai Nam
dblp:45/526
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › temporal information extraction
temporal ordering |
0.1 | 1 | 2005 | 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 |
0.1 | 1 | 2005 | Issues in Reasoning about Interaction Networks in Cells: Necessity of Event Ordering Knowledge · AAAI 2005 |
Bioinformatics and computational biology
systems biology |
0.1 | 1 | 2005 | 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 |
0.0 | 1 | 2004 | 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 |
0.0 | 1 | 2004 | Encoding Probabilistic Causal Model in Probabilistic Action Language · AAAI 2004 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
answer set programming |
0.0 | 1 | 2004 | 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 |
0.0 | 1 | 2004 | Encoding Probabilistic Causal Model in Probabilistic Action Language · AAAI 2004 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
logic programming |
0.0 | 1 | 2004 | 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 |
0.0 | 1 | 2004 | Encoding Probabilistic Causal Model in Probabilistic Action Language · AAAI 2004 |
Bioinformatics and computational biology
molecular interaction |
0.0 | 1 | 2004 | Reasoning about Triggered Actions in AnsProlog and Its Application to Molecular Interactions in Cells · KR 2004 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Domain-dependent knowledge in answer set planningabstractIn 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 |
AAAI | 1 |
| 2004 | Encoding Probabilistic Causal Model in Probabilistic Action Language
Tran Hoai Nam, Chitta Baral |
AAAI | 1 |
| 2004 | Reasoning about Triggered Actions in AnsProlog and Its Application to Molecular Interactions in Cells
Tran Hoai Nam, Chitta Baral |
KR | 1 |