CHREST (Chunks Hierarchies and REtrieval STructures) architecture, developed under Fernand R. Gobet at Brunel University, London, UK in collaboration with Peter C. Lane at the University of Hertfordshire, Hatfield, Hertfordshire, UK.
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CHREST (Chunks Hierarchies and REtrieval STructures) architecture
Когнитивная архитектура CHREST разрабатывается под руководством Фернана Гобе (Fernand Gobet) в Университете им. Брюнеля (Brunel University), Лондон, Великобритания совместно с Питером Лэйном (Peter C. Lane) в Хертфордширском университете (University of Hertfordshire), Хатфилд, графство Хертфордшир, Великобритания.
Общие сведения
CHREST model of human attention and learning (http://www.chrest.info/)
CHREST (Chunk Hierarchies and REtrieval STructures) is a cognitive architecture that models human perception, learning, memory, and problem solving. It is distinctive in its emphasis on the importance of perception and attention, and in following human constraints such as limitations on short-term memory and processing speed.
CHREST has been shown to accurately model many different aspects of human cognition across different domains.
It is currently the only cognitive architecture being developed in the United Kingdom.
An overview of the architecture of CHREST. See Design of CHREST for more details about how the CHREST model works.
CHREST is a symbolic cognitive architecture aimed at explaining the complex interaction between perception, attention, memory, learning and decision making. Learning is essential in the architecture and is modeled as the development of a network of nodes (chunks) that are connected in various ways.
CHREST combines low-level aspects of cognition (e.g., mechanisms monitoring information in short-term memory) with high-level aspects of cognition (e.g., use of strategies).
It consists of perception facilities for interacting with the external world, short-term memory stores (in particular, visual and verbal memory stores), a long-term memory store, and associated mechanisms for problem solving.
Short-term memory in CHREST contains references to chunks held in long-term memory, which are recognised through the discrimination network from information acquired by the perception system.
CHREST (http://chrest.info/software.html)
CHREST (version 4)
The CHREST software provides a graphical application and library for running a variety of CHREST models and experiments. Currently, the graphical interface supports a range of domain-types for demonstration and test purposes. More functionality for developing models is available using the scripting interface.
Detailed examples are provided for Lisp (using ABCL) and Ruby (using jruby), and illustrative examples given for groovy (a Java-like language), and clojure (a Lisp-like language).
Domains and Projects
CHREST has been used to closely simulate phenomena in several domains, including chess expertise, memory for computer programs, the use of multiple representations in physics, verbal learning, concept formation, children's acquisition of vocabulary and children's acquisition of syntactic categories in four different languages.
See CHREST projects for more details about these projects.
The following domains are currently supported:
- verbal learning
- classification
- pattern recognition and recall
- visual attention and memory
Requirements: Java 6.0. To develop your own models, you will need either a Java compiler or a scripting language for the Java platform.
Download: chrest-4.0.0-alpha-2.zip. Unpack the zip file and double-click on the 'jar' file; more information is contained in the documentation folder. (Last update: 6/2/13. MD5SUM e83ea295447368188611cafb3243a1f2)
Refer to the documentation within the download package for more information.
Source code: can be obtained from Github. License: OWL 0.9.2.
CHREST is derived from the earlier EPAM architecture, which was the first successful cognitive model, and the second ever learning algorithm implemented on a computer.
The timeline is:
- Early EPAM (1959-1962): Developed by Feigenbaum and Simon Focusses on Verbal Learning
- EPAM III (1963-1989): Major refinement of theory Still focus on Verbal Learning, but also Chess Reflections on the methodology of cognitive modelling
- EPAM IV and V (1995-1997): Extension to development of expertise Deliberate practice and strategic knowledge
- EPAM VI (2001): The last version on EPAM worked on by Simon. Covers a wide range of data in verbal learning, memory, and concept formation
- CHREST (1992-today): Developed by Fernand Gobet
- Chrest 1: written by Fernand Gobet whilst at Carnegie Mellon University
- Chrest 2: the main version used in previous simulations of chess expertise
- Chrest 3: an integration of memory and problem-solving, still focussed on chess
- MOSAIC: Model of syntax acquisition
Related EPAM/CHREST pages
EPAM VI
CHREST on Wikipedia
Fernand Gobet's CHREST page
Publications:
Selected publications
Fernand Gobet, Peter C.R. Lane. The CHREST Architecture of Cognition: The Role of Perception in General Intelligence
Freudenthal, D., Pine, J. M., Aguado-Orea, J., & Gobet, F. (2007). Modelling the developmental patterning of finiteness marking in English, Dutch, German and Spanish using MOSAIC, Cognitive Science, 31, 311-341.
Gobet, F. & Simon, H. A. (2000). Five seconds or sixty? Presentation time in expert memory. Cognitive Science, 24, 651-682.
Gobet, F., & Lane, P. C. R. (2010). The CHREST architecture of cognition: The role of perception in general intelligence. In Baum, E., Hutter, M., & Kitzelmann, E. (Eds), Proceedings of the Third Conference on Artificial General Intelligence (pp. 7-12). Amsterdam: Atlantis Press.
Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C-H., Jones, G., Oliver, I. & Pine, J. M. (2001). Chunking mechanisms in human learning. TRENDS in Cognitive Sciences, 5, 236-243.
Jones, G., Gobet, F., & Pine, J. M. (2007). Linking working memory and long-term memory: A computational model of the learning of new words. Developmental Science, 10, 853-873.
Lane, P.C.R., & Gobet, F. (2012). A theory-driven testing methodology for developing scientific software. Journal of Experimental and Theoretical Artificial Intelligence, 24, 421-456.
Waters, A. J., & Gobet, F. (2008). Mental imagery and chunks: Empirical and computational findings. Memory & Cognition, 36, 505-517
