New directions in computational, combinational and structural creativity

Garvey, Bruce, Chen, Liuqing, Shi, Feng, Han, Ji ORCID: 0000-0003-3240-4942 and Childs, Peter RN (2019) New directions in computational, combinational and structural creativity. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 233 (2). pp. 425-431.

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Abstract

<jats:p> This paper examines how new and creative relationships in datasets, not easily revealed by conventional information retrieval methods and technologies, can be identified using a mix of established and new methods. The authors present how the integration of computerised morphological analysis with new computational models, incorporating web crawler, data processing networking and data mining algorithms, can help facilitate the identification of new ideas. Boden’s concept of ‘Combinational Creativity’ indicates a structured process, which generates unfamiliar combinations of familiar concepts and constructs allowing creative styles of thought. This structured approach has been constrained by the resultant combinatorial explosion and the dearth of easily accessible computer software and supporting methodologies, to help identify viable new solutions. Feature-enhanced computerised morphological analysis provides a new structural support tool for creativity and innovation. Morphological analysis systematically structures and examines all the possible relationships in a multidimensional, highly complex, usually non-quantifiable problem space. Computerisation of the process now permits large number of configurations (millions) in the problem space to be majorly reduced (typically &gt; 95%), identifying only internally consistent solutions. These solutions are likely to embrace configurations containing something, which has not previously been considered, thus increasing the probability of some form of technological or design breakthrough and hence truly creative. </jats:p>

Item Type:	Article
Uncontrolled Keywords:	Morphological analysis, creativity, idea generation, data analysis, artificial intelligence
Depositing User:	Symplectic Admin
Date Deposited:	24 May 2018 15:03
Last Modified:	19 Jan 2023 01:33
DOI:	10.1177/0954406218769919
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3021720