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
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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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|
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References found in this work BETA
Shane Legg & Marcus Hutter (2007). Universal Intelligence: A Definition of Machine Intelligence. Minds and Machines 17 (4):391-444.
Anthony Chemero (2007). Asking What's Inside the Head: Neurophilosophy Meets the Extended Mind. [REVIEW] Minds and Machines 17 (3):345-351.
Kevin B. Korb (2004). Introduction: Machine Learning as Philosophy of Science. Minds and Machines 14 (4):433-440.
Stan Franklin (2007). Walter J. Freeman, How Brains Make Up Their Minds. Minds and Machines 17 (3):353-356.
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.
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