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Image to InterpretationAn Intelligent System to Aid Historians in Reading the Vindolanda Texts$
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Melissa Terras

Print publication date: 2006

Print ISBN-13: 9780199204557

Published to Oxford Scholarship Online: September 2007

DOI: 10.1093/acprof:oso/9780199204557.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 19 September 2021



(p.153) 6 Conclusion
Image to Interpretation

Melissa M. Terras (Contributor Webpage)

Oxford University Press

This research has demonstrated that an MDL architecture such as the GRAVA system can be appropriated to generate possible solutions to interpretation problems, which span different semantic levels and incorporate different types of data. In doing so, a system has been constructed which has an emergent behaviour similar to that of human experts and generates possible, reasonable interpretations of texts to aid the experts in their task. Much can be done to improve the system described in this book, and to incorporate other types of information into the architecture to improve its functionality. There is scope for further research in almost every facet. Considerations of future work presented in this chapter focuses on two main areas: other possible approaches to knowledge elicitation to enable further understanding of how experts read ancient documents, and the enhancement and development of the MDL-based GRAVA system to increase its accuracy, and eventually deliver an application to the papyrologists. This chapter also highlights the overall contribution the research has made to its many constituent fields. An evaluation of the research is presented: the cognitive visual architecture developed being a testament to the value of interdisciplinary research.

Keywords:   artificial intelligence, knowledge elicitation, cognitive visual architecture, interdisciplinary research, Minimum Description Length

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