Addis, M., Sozou, Peter D., Gobet, F. and Lane, Philip R. (2016) Computational scientific discovery and cognitive science theories. In: Müller, Vincent C., (ed.) Computing and Philosophy. Synthese Library series. Springer International (Firm), Springer International Publishing, Switzerland, pp. 83-87. ISBN 9783319232904
Full text not available from this repository.Abstract
This study is concerned with processes for discovering new theories in science. It considers a computational approach to scientific discovery, as applied to the discovery of theories in cognitive science. The approach combines two ideas. First, a process-based scientific theory can be represented as a computer program. Second, an evolutionary computational method, genetic programming, allows computer programs to be improved through a process of computational trialand-error. Putting these two ideas together leads to a system that can automatically generate and improve scientific theories. The application of this method to the discovery of theories in cognitive science is examined. Theories are built up from primitive operators. These are contained in a theory language that defines the space of possible theories. An example of a theory generated by this method is described. These results support the idea that scientific discovery can be achieved through a heuristic search process, even for theories involving a sequence of steps. However, this computational approach to scientific discovery does not eliminate the need for human input. Human judgment is needed to make reasonable prior assumptions about the characteristics of operators used in the theory generation process, and to interpret and provide context for the computationally generated theories.
| Item Type: | Book Section |
|---|---|
| Official URL: | http://www.springer.com/ |
| Additional Information: | © 2016 Springer International Publishing |
| Divisions: | Philosophy, Logic and Scientific Method |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Date Deposited: | 18 Apr 2016 15:02 |
| Last Modified: | 24 Nov 2024 19:48 |
| URI: | http://eprints.lse.ac.uk/id/eprint/66168 |
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