Demo Data from EDBT 2026 / reviewers in the wild / expert
Ruiting Lian
dblp:64/8966
· DBLP profile ↗
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 · 4 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › cognitive modeling
cognitive architecture |
0.1 | 1 | 2011 | 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 |
0.1 | 1 | 2011 | 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 |
0.0 | 1 | 2011 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Cognitive Synergy between Procedural and Declarative Learning in the Control of Animated and Robotic Agents Using the OpenCogPrime AGI ArchitectureabstractThe 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 |
AAAI | 6 |
| 2010 | A world survey of artificial brain projects, Part I: Large-scale brain simulations
Hugo de Garis, Ben Goertzel, Ruiting Lian |
Neurocomputing | 4 |
| 2010 | A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures
Ben Goertzel, Ruiting Lian, Itamar Arel, Hugo de Garis |
Neurocomputing | 2 |
| 2010 | Sentence generation for artificial brains: A glocal similarity-matching approach
Ruiting Lian, Ben Goertzel, Michael Ross, Murilo Saraiva de Queiroz, Linas Vepstas |
Neurocomputing | 1 |