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Arvind Sathi

dblp:89/4677 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1985
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 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
Video understanding and tracking · 50% Knowledge representation and reasoning · 50%
Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking › activity understanding
activity modeling
0.011985
Representation of Activity Knowledge for Project Management · IEEE Trans. Pattern Anal. Mach. Intell. 1985
Knowledge, reasoning and agents › Knowledge representation and reasoning
temporal reasoning
0.011985
Representation of Activity Knowledge for Project Management · IEEE Trans. Pattern Anal. Mach. Intell. 1985
Empirical software engineering
software project management
0.011985
Representation of Activity Knowledge for Project Management · IEEE Trans. Pattern Anal. Mach. Intell. 1985

Methods — techniques the papers use, named apart from their topics

ontology · 0.0knowledge representation · 0.0
YearPublicationVenuePosition
1985 Representation of Activity Knowledge for Project Management
abstract
Representation of activity knowledge is important to any application which must reason about activities such as new product management, factory scheduling, robot control, vehicle control, software engineering, and air traffic control. This paper provides an integration of the underlying theories needed for modeling activities. Using the domain of large computer design projects as an example, the semantics of activity modeling is described. While the past research in knowledge representation has discovered most of the underlying concepts, our attempt is toward their integration. This includes the epistemological concepts for erecting the required knowledge structure; the concepts of activity, state, goal, and manifestation for the adequate description of the plan and the progress; and the concepts of time and causality to infer the progression among the activities. We also address the issues which arise due to the integration of aggregation, time, and causality among activities and states.
Arvind Sathi, Mark S. Fox, Michael Greenberg 0001
IEEE Trans. Pattern Anal. Mach. Intell.1