# Cognitive architecture

A cognitive architecture can refer to a theory about the structure of the human mind. One of the main goals of a cognitive architecture is to summarize the various results of cognitive psychology in a comprehensive computer model. However, the results need to be in a formalized form so far that they can be the basis of a computer program. The formalized models can be used to further refine a comprehensive theory of cognition, and more immediately, as a commercially usable model. Successful cognitive architectures include ACT-R (Adaptive Control of Thought, ACT), SOAR and OpenCog.

## History

Herbert A. Simon, one of the founders of the field of artificial intelligence, stated that the 1960 thesis by his student Ed Feigenbaum, EPAM provided a possible "architecture for cognition"[1] because it included some commitments for how more than one fundamental aspect of the human mind worked. In EPAM's case, 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.[2] 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 student used the term "cognitive architecture" in his lab to refer to the ACT theory as embodied in the collection of papers and designs since they didn't yet have any sort of complete implementation at the time.

In 1983 John R. Anderson published the seminal work in this area, entitled The Architecture of Cognition.[3] One can distinguish between the theory of cognition and the implementation of the theory. The theory of cognition outlined the structure of the various parts of the mind and made commitments to the use of rules, associative networks, and other aspects. The cognitive architecture implements the theory on computers. The software used to implement the cognitive architectures were also "cognitive architectures". Thus, a cognitive architecture can also refer to a blueprint for intelligent agents. It proposes (artificial) computational processes that act like certain cognitive systems, most often, like a person, or acts 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 symbolic, connectionist, or hybrid. Some cognitive architectures or models are based on a set of generic rules, as, e.g., the Information Processing Language (e.g., Soar based on the unified theory of cognition, or similarly ACT-R). Many of these architectures are based on the-mind-is-like-a-computer analogy. In contrast subsymbolic processing specifies no such rules a priori and relies on emergent properties of processing units (e.g. nodes). Hybrid architectures combine both types of processing (such as CLARION). A further distinction is whether the architecture is centralized with a neural correlate of a processor at its core, or decentralized (distributed). The decentralized flavor, has become popular under the name of parallel distributed processing in mid-1980s and connectionism, a prime example being neural networks. A further design issue is additionally a decision between holistic and atomistic, or (more concrete) modular structure. By analogy, this extends to issues of knowledge representation.

In traditional AI, intelligence is often programmed from above: the programmer is the creator, and makes something and imbues it with its intelligence, though many traditional AI systems were also designed to learn (e.g. improving their game-playing or problem-solving competence). Biologically inspired computing, on the other hand, takes sometimes a more bottom-up, decentralised approach; bio-inspired techniques often involve the method of specifying a set of simple generic rules or a set of simple nodes, from the interaction of which emerges the overall behavior. It is hoped to build up complexity until the end result is something markedly complex (see complex systems). However, it is also arguable that systems designed 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

A comprehensive review of implemented cognitive architectures has been undertaken in 2010 by Samsonovish et al.[4] and is available as an online repository.[5] Some well-known cognitive architectures, in alphabetical order:

## References

1. ^ https://saltworks.stanford.edu/catalog/druid:st035tk1755
2. ^ "This Week’s Citation Classic: Anderson J R & Bower G H. Human associative memory. Washington," in: CC. Nr. 52 Dec 24-31, 1979.
3. ^ John R. Anderson. The Architecture of Cognition, 1983/2013.
4. ^ Samsonovich, Alexei V. "Toward a Unified Catalog of Implemented Cognitive Architectures." BICA 221 (2010): 195-244.
5. ^ http://bicasociety.org/cogarch/
6. ^ Douglas Whitney Gage (2004). Mobile robots XVII: 26–28 October 2004, Philadelphia, Pennsylvania, USA. Society of Photo-optical Instrumentation Engineers. page 35.
7. ^ J.S. Albus (1979). "Mechanisms of Planning and Problem Solving in the Brain". In: Mathematical Biosciences. Vol. 45, pp. 247293, 1979.
8. ^ Anwar, Ashraf, and Stan Franklin. "Sparse distributed memory for ‘conscious’ software agents." Cognitive Systems Research 4.4 (2003): 339-354.
9. ^ Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).
10. ^ Graves, Alex, Greg Wayne, and Ivo Danihelka. "Neural Turing Machines." arXiv preprint arXiv:1410.5401 (2014).
11. ^ Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533.
12. ^ http://people.idsia.ch/~juergen/naturedeepmind.html
13. ^ Schmidhuber, Jürgen. "Deep learning in neural networks: An overview." Neural Networks 61 (2015): 85-117.
14. ^ An Intelligent Architecture for Integrated Control and Asset Management for Industrial Processes Taylor, J.H. Sayda, A.F. in Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation. pp 1397–1404
15. ^ A Framework for comparing agent architectures, Aaron Sloman and Matthias Scheutz, in Proceedings of the UK Workshop on Computational Intelligence, Birmingham, UK, September 2002.
16. ^ Weston, Jason, Sumit Chopra, and Antoine Bordes. "Memory networks." arXiv preprint arXiv:1410.3916 (2014).
17. ^ Eliasmith, Chris, et al. "A large-scale model of the functioning brain." science 338.6111 (2012): 1202-1205.
18. ^ Denning, Peter J. "Sparse distributed memory." (1989).Url: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19920002425.pdf
19. ^ Kanerva, Pentti (1988). Sparse Distributed Memory. The MIT Press. ISBN 978-0-262-11132-4.
20. ^ Mendes, Mateus, Manuel Crisóstomo, and A. Paulo Coimbra. "Robot navigation using a sparse distributed memory." Robotics and automation, 2008. ICRA 2008. IEEE international conference on. IEEE, 2008.
21. ^ Jockel, Sascha, Felix Lindner, and Jianwei Zhang. "Sparse distributed memory for experience-based robot manipulation." Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on. IEEE, 2009.
22. ^ Rinkus, Gerard J. "Sparsey™: event recognition via deep hierarchical sparse distributed codes." Frontiers in computational neuroscience 8 (2014).
23. ^ Snaider, Javier, and Stan Franklin. "Integer sparse distributed memory." Twenty-fifth international FLAIRS conference. 2012.
24. ^ Snaider, Javier, and Stan Franklin. "Vector LIDA." Procedia Computer Science 41 (2014): 188-203.
25. ^ Rolls, Edmund T. "Invariant visual object and face recognition: neural and computational bases, and a model, VisNet." Frontiers in computational neuroscience 6 (2012).