David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Minds and Machines 17 (3):311-329 (2007)
The evolutionary circuit design is an approach allowing engineers to realize computational devices. The evolved computational devices represent a distinctive class of devices that exhibits a specific combination of properties, not visible and studied in the scope of all computational devices up till now. Devices that belong to this class show the required behavior; however, in general, we do not understand how and why they perform the required computation. The reason is that the evolution can utilize, in addition to the “understandable composition of elementary components”, material-dependent constructions and properties of environment (such as temperature, electromagnetic field etc.) and, furthermore, unknown physical behaviors to establish the required functionality. Therefore, nothing is known about the mapping between an abstract computational model and its physical implementation. The standard notion of computation and implementation developed in computer science as well as in cognitive science has become very problematic with the existence of evolved computational devices. According to the common understanding, the evolved devices cannot be classified as computing mechanisms
|Keywords||Computing mechanism Evolutionary design Evolvable hardware Implementation problem|
|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
No references found.
Citations of this work BETA
Achim Hoffmann (2010). Can Machines Think? An Old Question Reformulated. Minds and Machines 20 (2):203-212.
Similar books and articles
Gordana Dodig-Crnkovic, Semantics of Information as Interactive Computation. Proceedings of the Fifth International Workshop on Philosophy and Informatics 2008.
Matthias Scheutz (2001). Computational Vs. Causal Complexity. Minds And Machines 11 (4):543-566.
Rodrick Wallace (2006). Pitfalls in Biological Computing: Canonical and Idiosyncratic Dysfunction of Conscious Machines. Mind and Matter 4 (1):91-113.
Matthias Scheutz (1999). When Physical Systems Realize Functions. Minds and Machines 9 (2):161-196.
James H. Fetzer (1997). Thinking and Computing: Computers as Special Kinds of Signs. [REVIEW] Minds and Machines 7 (3):345-364.
Michael Rescorla (2013). Against Structuralist Theories of Computational Implementation. British Journal for the Philosophy of Science 64 (4):681-707.
Clark H. Barrett (2005). Enzymatic Computation and Cognitive Modularity. Mind and Language 20 (3):259-287.
H. Clark Barrett (2005). Enzymatic Computation and Cognitive Modularity. Mind and Language 20 (3):259-87.
Ronald Chrisley (1995). Quantum Learning. In P. Pyllkkänen & P. Pyllkkö (eds.), New Directions in Cognitive Science. Finnish Society for Artificial Intelligence.
Gordana Dodig-Crnkovic (2011). Significance of Models of Computation, From Turing Model to Natural Computation. Minds and Machines 21 (2):301-322.
Added to index2009-01-28
Total downloads11 ( #132,630 of 1,096,831 )
Recent downloads (6 months)5 ( #53,739 of 1,096,831 )
How can I increase my downloads?