A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics

By John Edwin Laird, Christian Lebiere, Paul S. Rosenbloom. The AI Magazine, 2017

401 citations recorded by the source corpus.

n A standard model captures a com- munity consensus over a coherent region of science, serving as a cumulative reference point for the field that can pro- vide guidance for both research and applications, while also focusing efforts to extend or revise it. Here we propose developing such a model for humanlike minds, computational entities whose structures and processes are substantially similar to those found in human cognition. Our hypothesis is that cognitive architectures provide the appropri- ate computational abstraction for defin-ing a standard model, although the standard model is not itself such an architecture. The proposed standard model began as an initial consensus at the 2013 AAAI Fall Symposium on Integrated Cognition

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/ab32951c9b568b683fe1466ae81a8c076c83fe98

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Citations in the corpus

Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge (2020, background): “, 2017) including many cognitive architectures which used hybrid symbolic and subsymbolic systems (Laird et al., 2017), and more recently, compositional query languages for embedding KBs that are similar to”
MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks (2021, background): “Theory of mind as a subject, especially in computation (Laird et al., 2017), has gained increased”
Generative Agents: Interactive Simulacra of Human Behavior (2023, background): “In doing so, this work reopens the door to examining foundational human-computer interaction questions around cognitive models such as GOMS and Keystroke-LevelModel(KLM)[22,23],aroundprototypingtools[80], andaroundubiquitouscomputingapplications[26,31,101].”
Grounding Robot Plans from Natural Language Instructions with Incomplete World Knowledge (2018, background): “Alternatively, symbolic reasoning systems [5, 6, 7, 8, 9] incorporate background knowledge while reasoning about appropriate plans for the agent.”