An object-oriented view on problem representation as a search-efficiency facet: Minds vs. machines [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Minds and Machines 20 (1):103-117 (2010)
From an object-oriented perspective, this paper investigates the interdisciplinary aspects of problem representation as well the differences between representation of problems in the mind and that in the machine. By defining an object as a combination of a symbol-structure and its associated operations, it shows how the representation of problems can become related to control, which conducts the search in finding a solution. Different types of representation of problems in the machine are classified into four categories, and in a similar way four distinct models are distinguished for the representation of problems in the mind. The concept of layered hierarchies, as the main theme of the object-oriented paradigm, is used to examine the implications of problem representation in the mind for improving the representation of problems in the machine
|Keywords||Artificial intelligence Cognitive theory Machine learning Mind Problem representation Search|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Anthony Chemero (2007). Asking What's Inside the Head: Neurophilosophy Meets the Extended Mind. [REVIEW] Minds and Machines 17 (3):345-351.
Stan Franklin (2007). Walter J. Freeman, How Brains Make Up Their Minds. Minds and Machines 17 (3):353-356.
Kevin B. Korb (2004). Introduction: Machine Learning as Philosophy of Science. Minds and Machines 14 (4):433-440.
Shane Legg & Marcus Hutter (2007). Universal Intelligence: A Definition of Machine Intelligence. [REVIEW] Minds and Machines 17 (4):391-444.
Yury P. Shimansky (2004). The Concept of a Universal Learning System as a Basis for Creating a General Mathematical Theory of Learning. Minds and Machines 14 (4):453-484.
Citations of this work BETA
No citations found.
Similar books and articles
David Kirsh (2009). Problem Solving and Situated Cognition. In Philip Robbins & M. Aydede (eds.), The Cambridge Handbook of Situated Cognition. Cambridge. 264--306.
Adam Toon (2010). Models as Make-Believe. In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.
Dale Jacquette (1996). Lloyd on Intrinsic Natural Representation in Simple Mechanical Minds. Minds and Machines 6 (1):47-60.
Robert van Gulick (1982). Mental Representation: A Functionalist View. Pacific Philosophical Quarterly 63 (January):3-20.
Giovanni Pezzulo (2008). Coordinating with the Future: The Anticipatory Nature of Representation. [REVIEW] Minds and Machines 18 (2):179-225.
Benjamin A. Gorman (2006). Review of “The Mechanical Mind: A Philosophical Introduction to Minds, Machines, and Mental Representation, 2nd Edition”. [REVIEW] Essays in Philosophy 7 (2):1-3.
Peter Slezak (2004). The World Gone Wrong? Images, Illusions, Mistakes and Misrepresentations. In Hugh Clapin, Phillip Staines & Peter Slezak (eds.), Representation in Mind: New Approaches to Mental Representation. Elsevier.
Michael Della Rocca (1996). Representation and the Mind-Body Problem in Spinoza. Oxford University Press.
Brendan Kitts (1999). Representation Operators and Computation. Minds and Machines 9 (2):223-240.
Added to index2009-10-17
Total downloads26 ( #65,687 of 1,098,987 )
Recent downloads (6 months)9 ( #22,488 of 1,098,987 )
How can I increase my downloads?