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.

Agustin A. Araya

dblp:46/4709 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Requirements engineering and software design
knowledge-based design
0.011986
A Knowledge-Based Framework for Design · AAAI 1986
Machine learning › Transfer learning and domain adaptation
cross-domain transfer
0.011983
Learning by Controlled Transference of Knowledge Between Domains · IJCAI 1983
Machine learning › Learning theory
generalization
0.011984
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
YearPublicationVenuePosition
1989 Handling contingencies in planned office activities
abstract
A 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
IJCAI1
1986 A Knowledge-Based Framework for Design
Sanjay Mittal, Agustin A. Araya
AAAI2
1984 Learning Problem Classes by Means of Experimentation and Generalization
Agustin A. Araya
AAAI1
1983 Learning by Controlled Transference of Knowledge Between Domains
Agustin A. Araya
IJCAI1
1980 Research on Expert Problem Solving in Physics
Gordon S. Novak, Agustin A. Araya
AAAI2