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Constantinos Papatheodorou

dblp:44/956 · DBLP profile ↗
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4ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 1 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1Theory of computation · 1

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
2 papers
Knowledge representation and reasoning · 70% Image recognition and object detection · 30%
Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology
semantic web
0.112011
Reasoning and Proofing Services for Semantic Web Agents · IJCAI 2011
Computer vision › Image recognition and object detection › object detection
contextual reasoning
0.112010
Reasoning about Context in Ambient Intelligence Environments: A Report from the Field · KR 2010
Ubiquitous computing and smart environments
ambient intelligence
0.012010
Reasoning about Context in Ambient Intelligence Environments: A Report from the Field · KR 2010
YearPublicationVenuePosition
2011 Reasoning and Proofing Services for Semantic Web Agents
Kalliopi Kravari, Constantinos Papatheodorou, Grigoris Antoniou, Nick Bassiliades
IJCAI2
2010 Reasoning with Imperfect Context and Preference Information in Multi-Context Systems
Grigoris Antoniou, Antonis Bikakis, Constantinos Papatheodorou
ADBIS3
2010 On the Deployment of Contextual Reasoning in Ambient Intelligence Environments
abstract
Ambient Intelligence environments consist of various devices that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of ambient environments, and the special characteristics of the involved devices have introduced new research challenges on how to represent and reason with contextual information. Previous work presented a solution based on an extension of Multi-Context Systems through the use of defeasible reasoning to reason efficiently with conflicts. This paper reports on initial experiences gained from the deployment of contextual defeasible reasoning in real environments. We report on the architecture of an implementation on small devices, present the definition and implementation of two concrete application scenarios, and discuss the performance and issues of scalability of the approach.
Constantinos Papatheodorou, Grigoris Antoniou, Antonis Bikakis
Intelligent Environments1
2010 Reasoning about Context in Ambient Intelligence Environments: A Report from the Field
Grigoris Antoniou, Constantinos Papatheodorou, Antonis Bikakis
KR2