What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
His main research concerns Soar, Artificial intelligence, Chunking, Cognitive science and Human–computer interaction.
His research in Soar intersects with topics in Cognitive architecture and Distributed computing.
His study explores the link between Cognitive architecture and topics such as Elementary cognitive task that cross with problems in Range and Unified Theories of Cognition.
His research integrates issues of Task, Set, Function, Machine learning and Systems architecture in his study of Artificial intelligence.
His Chunking research incorporates themes from Dreyfus model of skill acquisition, Teaching method, Learning curve and Explanation-based learning.
Paul S. Rosenbloom has included themes like Excursion, Stimulus–response compatibility and Choice reaction time in his Human–computer interaction study.
His most cited work include:
- SOAR: an architecture for general intelligence (2247 citations)
- Mechanisms of skill acquisition and the law of practice (646 citations)
- Chunking in Soar: the anatomy of a general learning mechanism (510 citations)
What are the main themes of his work throughout his whole career to date?
Paul S. Rosenbloom mostly deals with Artificial intelligence, Soar, Cognitive architecture, Cognitive science and Machine learning.
When carried out as part of a general Artificial intelligence research project, his work on Chunking and Chunking is frequently linked to work in Structure, therefore connecting diverse disciplines of study.
The various areas that he examines in his Soar study include Distributed computing, Task and Agent architecture.
His study in Cognitive architecture is interdisciplinary in nature, drawing from both Graphical model, Theoretical computer science, Human–computer interaction and Reinforcement learning.
In his research, Domain is intimately related to Intelligent agent, which falls under the overarching field of Human–computer interaction.
His Cognitive science research is multidisciplinary, incorporating perspectives in Field, Unified Theories of Cognition and Connectionism.
He most often published in these fields:
- Artificial intelligence (46.41%)
- Soar (34.81%)
- Cognitive architecture (24.31%)
What were the highlights of his more recent work (between 2012-2021)?
- Cognitive architecture (24.31%)
- Artificial intelligence (46.41%)
- Sigma (9.94%)
In recent papers he was focusing on the following fields of study:
Paul S. Rosenbloom mainly investigates Cognitive architecture, Artificial intelligence, Sigma, Cognitive science and Graphical model.
The concepts of his Cognitive architecture study are interwoven with issues in Cognitive psychology, Human intelligence, Human–computer interaction and Reinforcement learning.
His Artificial intelligence research integrates issues from Machine learning and Cognitive model.
His studies in Cognitive science integrate themes in fields like Field, Metacognition, Consciousness and Generality.
His Graphical model research is multidisciplinary, relying on both Probabilistic logic and Cognitive systems.
Soar connects with themes related to Chunking in his study.
Between 2012 and 2021, his most popular works were:
- A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics (86 citations)
- The Sigma Cognitive Architecture and System: Towards Functionally Elegant Grand Unification (38 citations)
- The Sigma cognitive architecture and system (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Paul S. Rosenbloom focuses on Cognitive architecture, Sigma, Artificial intelligence, Graphical model and Leverage.
His biological study spans a wide range of topics, including Concept learning, Consciousness, Metacognition and Competitive learning.
His work on Robotics is typically connected to International research as part of general Artificial intelligence study, connecting several disciplines of science.
His work deals with themes such as Cognitive psychology and Cognitive systems, which intersect with Graphical model.
His studies deal with areas such as Word, Chunking and Cognitive model as well as Machine learning.
His Human intelligence research incorporates elements of Human–computer interaction and Subgame perfect equilibrium, Nash equilibrium.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
