Combinational Creativity and Computational Creativity By Ji HAN Dyson School of Design Engineering Imperial College London A thesis submitted for the degree of Doctor of Philosophy April 2018 Originality Declaration The coding of the Combinator and the Retriever was accomplished in collaboration with Feng SHI, a PhD colleague in the Creativity Design and Innovation Lab at the Dyson School of Design Engineering, Imperial College London, and co-author with me of relevant publications. Except where otherwise stated, this thesis is the result of my own research. This research was conducted in the Dyson School of Design Engineering at Imperial College London, between October 2014 and December 2017. I certify that this thesis has not been submitted in whole or in parts as consideration for any other degree or qualification at this or any other institute of learning. Ji HAN April 2018 II Copyright Declaration The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commericial No Derivitives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the license terms of this work. Ji HAN April 2018 III List of Publications The following journal and conference papers were published during this PhD research. Journal Publications 1. Han J., Shi F., Chen L., Childs P. R. N., 2018. The Combinator – A computer-based tool for creative idea generation based on a simulation approach. Design Science, Cambridge University Press, 4, p. e11. doi: 10.1017/dsj.2018.7. 2. Han J., Shi F., Chen L., Childs P. R. N., 2018. A computational tool for creative idea generation based on analogical reasoning and ontology. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 32(4), 462-477. doi:10.1017/ S0890060418000082. 3. Garvey B., Chen L., Shi F., Han J. (Communication Author), Childs P. R. N., 2018. New Directions in Computational, Combinational and Structural Creativity. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. doi: 10.1177/0954406218769919. 4. Han J., Park D., Shi F., Chen L., Hua M., Childs P. R. N., 2017. Three Driven Approaches to Combinational Creativity: Problem-, Similarity-, and Inspiration-Driven. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. doi: 10.1177/0954406217750189. 5. Shi F., Chen L., Han J., Childs P. R. N., 2017. A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design, 139(11), p111402. doi: 10.1115/1.4037649. 6. Chen L., Wang P., Dong H., Shi F., Han J., Guo Y., Childs P. R. N., 2018. An artificial intelligence based data-driven approach for design ideation. Journal of Visual Communication and Image Representation. (Pending) IV Peer-reviewed Conference Publications 1. Han J., Shi F., Park D., Chen L., Childs P. R. N., 2018. The Conceptual Distances between Ideas in Combinational Creativity. DS92: Proceedings of the DESIGN 2018 15th International Design Conference. doi: 10.21278/idc.2018.0264. (Outstanding Contribution Award) 2. Chen L., Wang P., Shi F., Han J., Childs P. R. N., 2018. A Computational Approach for Combinational Creativity in Design. DS92: Proceedings of the DESIGN 2018 15th International Design Conference. doi: 10.21278/idc.2018.0375. 3. Han J., Park D., Shi F., Chen L., Childs P. R. N., 2017. Three Driven Approaches to Combinational Creativity. DS 87: Proceedings of the 21st International Conference on Engineering Design (ICED17). p259-p268. 4. Han J., Shi F., Chen L., Childs P. R. N., 2017. The Analogy Retriever – An Idea Generation Tool. DS 87: Proceedings of the 21st International Conference on Engineering Design (ICED17). p11-p20. 5. Chen L., Shi F., Han J., Childs P. R. N., 2017. A Network-based Computational Model for Creative Knowledge Discovery Bridging Human-Computer Interaction and Data Mining. In: Proceedings of the 2017 ASME IDETC/CIE Conference. doi:10.1115/DETC2017-67228. 6. Shi F., Chen L., Han J., Childs P. R. N., 2017. Implicit Knowledge Discovery in Design Semantic Network by Applying Pythagorean Means on Shortest Path Searching. In: Proceedings of the 2017 ASME IDETC/CIE Conference. doi:10.1115/DETC2017-67230. 7. Han J., Shi F., Childs P. R. N., 2016. The Combinator: A Computer-Based Tool for Idea Generation. DS 84: Proceedings of the DESIGN 2016 14th International Design Conference. p639-p648. 8. Shi F., Han J., Childs P. R. N., 2016. A Data Mining Approach to Assist Design Knowledge Retrieval Based on Keyword Associations. DS 84: Proceedings of the DESIGN 2016 14th International Design Conference. p1125-p1134. V Abstract Creativity is a significant element of design. However, it can be challenging to produce creative ideas that will be of value to society and worthy of design effort. This thesis explores combinational creativity, which involves unfamiliar combinations of familiar ideas, and implements combinational creativity in computational tools to support design. Two computational tools, the Combinator and the Retriever, have been developed to support designers in creative idea generations during the early stages of design. The Combinator has been developed by imitating aspects of human cognition in achieving combinational creativity. This tool produces combinational prompts in text and image forms. The Retriever has been developed based on ontology by embracing aspects of cognitions of analogical reasoning. This tool constructs ontologies with sufficient richness and coverage to support reasoning over real-world datasets, and produces text-form outputs with correlated image mood boards. Case studies indicate that the Combinator and the Retriever are useful and effective in terms of supporting creative ideation. The case studies show that both of the tools can increase the fluency of idea generation and improve the flexibility, usefulness and originality of the ideas produced, for the datasets studied. In addition to computational creativity tools, this thesis has proposed three driven approaches to produce combinational creative ideas, which are problem-, similarity-, and inspiration-driven. A study indicates that these three approaches are used commonly in practical designs, of which the problem-driven approach is the dominant approach. In addition, the three approaches could be used individually as well as in groups. The conceptual distances between ideas in combinational creativity are also explored in this thesis. A study reveals that far-related ideas, which are used more commonly, could lead to more creative outcomes compared with closely-related ideas. The findings and outcomes of this thesis lead to a further understanding of creativity in the design context. The novel approaches used for developing the Combinator and the Retriever, as well as the outcomes of the theoretical studies could be adapted to develop new computational design support tools. VI Table of Contents Originality Declaration ................................................................................................................. II Copyright Declaration ................................................................................................................. III List of Publications ...................................................................................................................... IV Abstract ...................................................................................................................................... VI Table of Contents ....................................................................................................................... VII List of Figures .............................................................................................................................. XI List of Tables ............................................................................................................................. XIII Acknowledgements................................................................................................................... XIV Chapter 1. Introduction ................................................................................................................ 1 1.1. Background .................................................................................................................................. 1 1.2. Research Aim ............................................................................................................................... 3 1.3. Research Questions ..................................................................................................................... 4 1.4. Research Objectives ..................................................................................................................... 4 1.5. Thesis Structure ........................................................................................................................... 4 1.6. Chapter Conclusions .................................................................................................................... 6 References .......................................................................................................................................... 8 Chapter 2. Methodology ...........................................................................................................