Demo Data from EDBT 2026 / reviewers in the wild / expert
S. J. Noronha
dblp:64/1217
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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 |
Planning, search and constraint satisfaction · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › scheduling
knowledge-based scheduling |
0.0 | 1 | 1991 | Knowledge-Based Approaches to Scheduling Problems: A Survey · IEEE Trans. Knowl. Data Eng. 1991 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
knowledge-based planning |
0.0 | 1 | 1991 | Knowledge-Based Approaches to Scheduling Problems: A Survey · IEEE Trans. Knowl. Data Eng. 1991 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Knowledge-Based Approaches to Scheduling Problems: A SurveyabstractRecent developments in artificial intelligence (AI) have led to the use of knowledge-based techniques for solving scheduling problems. The authors survey several existing intelligent planning and scheduling systems with the aim of providing a guide to the main AI techniques used. In view of the prevailing difference is usage of the terms planning and scheduling between AI and operations research (OR), a taxonomy of planning and scheduling problems is presented. The modeling of real world problems from closed deterministic worlds to complex real worlds is illustrated with a project scheduling example. Some of the more successful planning and scheduling systems are surveyed, and their features are highlighted. The AI approaches are consolidated into knowledge representation and problem solving in the project management context.> S. J. Noronha, V. V. S. Sarma |
IEEE Trans. Knowl. Data Eng. | 1 |