Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699033
Title: Situated creativity-inspired problem-solving
Author: Byrne, William Frederick
ISNI:       0000 0004 5994 2254
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2016
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Abstract:
Creativity is a useful attribute for people to have. It allows them to solve unfamiliar problems, introduce novelty to established domains, and to understand and assimilate new information and situations - all things we would like computers to be able to do too. However, these creative attributes do not exist in isolation: they occur in a context in which people tend to solve problems routinely where possible rather than consider non-standard ideas. These more mundane attributes might also be useful for problem solving computers, for the same reasons they are useful for us. However, they are often ignored in attempts to implement systems capable of producing remarkable outputs. We explore how the study of both human and computational creativity can inform an approach to help computers to display useful, complete problem-solving behaviour similar to our own: that is, robust, exible and, where possible and appropriate, surprising. We describe a knowledge-based model that incorporates a genetic algorithm with some characteristics of our own approach to knowledge reuse. The model is driven by direct interactions with problem scenarios. Descriptions of the role or appearance of key themes and concepts in literature in functioning problem-solving systems is lacking; we suggest that they appear as artefacts of the operation of our model. We demonstrate that it is capable of solving routine problems flexibly and effectively. We also demonstrate that it can solve problems that would be effectively impossible for a genetic algorithm operating without the benefit of knowledge-driven biasing. Artefacts of the behaviour of the model could, in certain scenarios, lead to the appearance of non-routine or surprising solutions.
Supervisor: Not available Sponsor: Centre of Excellence for Research in Computational Intelligence and Applications
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.699033  DOI: Not available
Keywords: QA75 Electronic computers. Computer science ; QA76 Computer software
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