Cognitive architecture

Source: Wikipedia, the free encyclopedia.

A cognitive architecture refers to both a theory about the structure of the

computational cognitive science.[1] The formalized models can be used to further refine a comprehensive theory of cognition and as a useful artificial intelligence program. Successful cognitive architectures include ACT-R (Adaptive Control of Thought – Rational) and SOAR
. The research on cognitive architectures as software instantiation of cognitive theories was initiated by Allen Newell in 1990.[2]

The Institute for Creative Technologies defines cognitive architecture as: "hypothesis about the fixed structures that provide a mind, whether in natural or artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments."[3]

History

human memory and human learning
).

John R. Anderson started research on human memory in the early 1970s and his 1973 thesis with Gordon H. Bower provided a theory of human associative memory.[5] He included more aspects of his research on long-term memory and thinking processes into this research and eventually designed a cognitive architecture he eventually called ACT. He and his students were influenced by Allen Newell's use of the term "cognitive architecture". Anderson's lab used the term to refer to the ACT theory as embodied in a collection of papers and designs (there was not a complete implementation of ACT at the time).

In 1983 John R. Anderson published the seminal work in this area, entitled The Architecture of Cognition.

intelligent under some definition. Cognitive architectures form a subset of general agent architectures
. The term 'architecture' implies an approach that attempts to model not only behavior, but also structural properties of the modelled system.

Distinctions

Cognitive architectures can be

modular
structure.

In traditional

top-down
on the basis of observations of what humans and other animals can do, rather than on observations of brain mechanisms, are also biologically inspired, though in a different way.

Notable examples

Some well-known cognitive architectures, in alphabetical order:

Name Description
4CAPS developed at Carnegie Mellon University by Marcel A. Just and Sashank Varma.
4D-RCS Reference Model Architecture developed by
NIST is a reference model architecture that provides a theoretical foundation for designing, engineering, integrating intelligent systems software for unmanned ground vehicles.[8]
ACT-R developed at Carnegie Mellon University under John R. Anderson.
ASMO[9]
developed by Rony Novianto, Mary-Anne Williams and Benjamin Johnston at the University of Technology Sydney. This cognitive architecture is based on the idea that actions/behaviours compete for an agents resources.
CHREST
developed under
Brunel University and Peter C. Lane at the University of Hertfordshire
.
CLARION the cognitive architecture, developed under Ron Sun at Rensselaer Polytechnic Institute and University of Missouri.
CMAC The Cerebellar Model Articulation Controller (CMAC) is a type of neural network based on a model of the mammalian
classification in the machine learning
community.
Copycat by
Indiana University
.
DAYDREAMER developed by Erik Mueller at the University of California in Los Angeles under Michael G. Dyer
DUAL
developed at the New Bulgarian University under Boicho Kokinov.
FORR developed by Susan L. Epstein at
The City University of New York
.
Framsticks a connectionist distributed neural architecture for simulated creatures or robots, where modules of neural networks composed of heterogenous neurons (including receptors and effectors) can be designed and evolved.
Google DeepMind The company has created a
video games in a similar fashion to humans[11] and a neural network that may be able to access an external memory like a conventional Turing machine,[12] resulting in a computer that appears to possibly mimic the short-term memory of the human brain. The underlying algorithm is based on a combination of Q-learning with multilayer recurrent neural network.[13] (Also see an overview by Jürgen Schmidhuber on earlier related work in deep learning.[14][15]
)
Holographic associative memory This architecture is part of the family of correlation-based associative memories, where information is mapped onto the phase orientation of complex numbers on a Riemann plane. It was inspired by holonomic brain model by Karl H. Pribram. Holographs have been shown to be effective for associative memory tasks, generalization, and pattern recognition with changeable attention.
Hierarchical temporal memory This architecture is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world.
CoJACK An ACT-R inspired extension to the JACK multi-agent system that adds a cognitive architecture to the agents for eliciting more realistic (human-like) behaviors in virtual environments.
IDA and LIDA implementing
Global Workspace Theory, developed under Stan Franklin at the University of Memphis
.
MANIC (Cognitive Architecture)
Michael S. Gashler, University of Arkansas.
PRS
'Procedural Reasoning System', developed by Michael Georgeff and Amy Lansky at SRI International.
Psi-Theory
developed under
Otto-Friedrich University in Bamberg, Germany
.
Spaun (Semantic Pointer Architecture Unified Network) by Chris Eliasmith at the Centre for Theoretical Neuroscience at the
spiking neurons, which uses groups of these neurons to complete cognitive tasks via flexibile coordination. Components of the model communicate using spiking neurons that implement neural representations called "semantic pointers" using various firing patterns. Semantic pointers can be understood as being elements of a compressed neural vector space.[16]
Soar developed under Allen Newell and John Laird at Carnegie Mellon University and the University of Michigan.
Society of Mind proposed by Marvin Minsky.
The Emotion Machine proposed by Marvin Minsky.
Sparse distributed memory was proposed by Pentti Kanerva at NASA Ames Research Center as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs.[17]
Subsumption architectures developed e.g. by Rodney Brooks (though it could be argued whether they are cognitive).

See also

References

  1. .
  2. ^ Newell, Allen. 1990. Unified Theories of Cognition. Harvard University Press, Cambridge, Massachusetts.
  3. ^ "Cognitive Architecture". Institute for Creative Technologies. 2024. Retrieved 11 February 2024.
  4. ^ "The Feigenbaum Papers". Stanford University. Retrieved 11 February 2024.
  5. ^ "This Week's Citation Classic: Anderson J R & Bower G H. Human associative memory. Washington," in: CC. Nr. 52 Dec 24–31, 1979.
  6. ^ John R. Anderson. The Architecture of Cognition, 1983/2013.
  7. S2CID 9709702
    .
  8. ^ Douglas Whitney Gage (2004). Mobile robots XVII: 26–28 October 2004, Philadelphia, Pennsylvania, USA. Society of Photo-optical Instrumentation Engineers. page 35.
  9. ^ Novianto, Rony (2014). Flexible Attention-based Cognitive Architecture for Robots (PDF) (Thesis).
  10. .
  11. ].
  12. ].
  13. .
  14. ^ "DeepMind's Nature Paper and Earlier Related Work".
  15. S2CID 11715509
    .
  16. .
  17. ^ Denning, Peter J. "Sparse distributed memory." (1989).Url: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19920002425.pdf

External links