EDBT 2026 Demo / reviewers in the wild / expert
Agustin A. Araya
dblp:46/4709
· DBLP profile ↗
6ranked-venue papers
4as first author
0since 2021 · last 1989
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 6 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 6 · 4 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 |
Planning, search and constraint satisfaction · 41% Learning theory · 35% Transfer learning and domain adaptation · 24% | |
| Theoretical computer science
1 paper |
Automated reasoning and model checking · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Requirements engineering and software design · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 100% |
Topics — the 3 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Requirements engineering and software design
knowledge-based design |
0.0 | 1 | 1986 | A Knowledge-Based Framework for Design · AAAI 1986 |
Machine learning › Transfer learning and domain adaptation
cross-domain transfer |
0.0 | 1 | 1983 | Learning by Controlled Transference of Knowledge Between Domains · IJCAI 1983 |
Machine learning › Learning theory
generalization |
0.0 | 1 | 1984 | Learning Problem Classes by Means of Experimentation and Generalization · AAAI 1984 |
Methods — techniques the papers use, named apart from their topics
heuristic problem solving · 0.0knowledge-based system · 0.0knowledge transfer · 0.0experimentation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1989 | Handling contingencies in planned office activitiesabstractA plan-based approach for systems that support office activities involving multiple agents has several advantages. It can represent important aspects of these activities, it has the potential for alleviating maintenance problems by generating new plans when activities change, and it can increase the reusability of knowledge across activities. On the other hand, because office activities take place in dynamic environments a system using a plan to coordinate an activity will have to deal with many contingencies and surprises. We describe the main characteristics of a plan-based approach and the roles a system of this kind can play in an office, and present a process for handling contingencies which can make the system robust in the presence of contingencies not explicitly dealt with in the plan. We close with a discussion of an implementation of the process and the viability of the approach to support real office activities. Agustin A. Araya |
IEA/AIE (1) | 1 |
| 1987 | Compiling Design Plans from Descriptions of Artifacts and Problem Solving Heuristics
Agustin A. Araya, Sanjay Mittal |
IJCAI | 1 |
| 1986 | A Knowledge-Based Framework for Design
Sanjay Mittal, Agustin A. Araya |
AAAI | 2 |
| 1984 | Learning Problem Classes by Means of Experimentation and Generalization
Agustin A. Araya |
AAAI | 1 |
| 1983 | Learning by Controlled Transference of Knowledge Between Domains
Agustin A. Araya |
IJCAI | 1 |
| 1980 | Research on Expert Problem Solving in Physics
Gordon S. Novak, Agustin A. Araya |
AAAI | 2 |