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Nil Geisweiller

dblp:66/981 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2011
0000-0001-5041-6299ORCID · corroborated

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1Theory of computation · 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
Knowledge representation and reasoning · 44% Video understanding and tracking · 44% Motion planning and robot control · 13%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › cognitive modeling
cognitive architecture		 0.112011
Cognitive Synergy between Procedural and Declarative Learning in the Control of Animated and Robotic Agents Using the OpenCogPrime AGI Architecture · AAAI 2011
Computer vision › Video understanding and tracking › activity recognition › procedural activity understanding
procedure learning		 0.112011
Cognitive Synergy between Procedural and Declarative Learning in the Control of Animated and Robotic Agents Using the OpenCogPrime AGI Architecture · AAAI 2011
Robotics › Motion planning and robot control
robot control		 0.012011
Cognitive Synergy between Procedural and Declarative Learning in the Control of Animated and Robotic Agents Using the OpenCogPrime AGI Architecture · AAAI 2011

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

declarative learning · 0.1
YearPublicationVenuePosition
2011 Cognitive Synergy between Procedural and Declarative Learning in the Control of Animated and Robotic Agents Using the OpenCogPrime AGI Architecture
abstract
The hypothesis is presented that "cognitive synergy" -- proactive and mutually-assistive feedback between different cognitive processes associated with different types of memory -- may serve as a foundation for advanced artificial general intelligence. A specific AI architecture founded on this idea, OpenCogPrime, is described, in the context of its application to control virtual agents and robots. The manifestations of cognitive synergy in OpenCogPrime's procedural and declarative learning algorithms are discussed in some detail.
Ben Goertzel, Joel Pitt, Jared Wigmore, Nil Geisweiller, Zhenhua Cai, Ruiting Lian, Deheng Huang, Gino Yu
 AAAI4
2008 Relating continuous and discrete PEPA models of signalling pathways
Nil Geisweiller, Jane Hillston, Marco Stenico
 Theor. Comput. Sci.1