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The ideal ReaderBot:: Machine readers and narrative analytics

The ideal ReaderBot:: Machine readers and narrative analytics
The ideal ReaderBot:: Machine readers and narrative analytics
When Artificial Intelligence is considered in the context of creative activities, it is normally in the role of the creator (e.g. deep learning algorithms applied to poetry or painting). In this paper we instead propose casting the machine in the role of a reader (a ReaderBot), to help authors see how their work was likely to be experienced by an audience. This seems especially useful with interactive narratives, where multiple paths can produce many thousands of variations on that experience. We present an exploratory experiment based on the StoryPlaces locative narrative system, showing how a Simple Heuristic ReaderBot can simulate readings of a located hypertext. We then provide Narrative Analytics of those readings in the form of structural, experiential/dramatic, and locative feedback. We also present three Machine Learning ReaderBots (Linear Regression, Logistic Regression, and a Feed Forward Neural Network), trained on real reading logs, and using distance, prior visits, altitude, proximity to POIs, and text similarity as an input vector to predict next node decisions with precision substantially better than random, and comparable to the Heuristic Reader. We argue that ReaderBots can create an instant audience of thousands that could give authors valuable insights into the potential experiences of their readers.
Hypertext, Machine Learning, creative writing
ACM
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
West-Taylor, Charlie
93b5d46d-c900-49ae-90d1-f97b5c9125ba
Howard, Yvonne
8aecbf0f-ed6a-4ce6-9530-5fa43226a3b0
Packer, Heather
0e86c31f-6460-4bbd-b6ac-c717ee2cbd96
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
West-Taylor, Charlie
93b5d46d-c900-49ae-90d1-f97b5c9125ba
Howard, Yvonne
8aecbf0f-ed6a-4ce6-9530-5fa43226a3b0
Packer, Heather
0e86c31f-6460-4bbd-b6ac-c717ee2cbd96

Millard, David, West-Taylor, Charlie, Howard, Yvonne and Packer, Heather (2018) The ideal ReaderBot:: Machine readers and narrative analytics. In NHT’18, July 2018, Baltimore, USA. ACM. 5 pp.

Record type: Conference or Workshop Item (Paper)

Abstract

When Artificial Intelligence is considered in the context of creative activities, it is normally in the role of the creator (e.g. deep learning algorithms applied to poetry or painting). In this paper we instead propose casting the machine in the role of a reader (a ReaderBot), to help authors see how their work was likely to be experienced by an audience. This seems especially useful with interactive narratives, where multiple paths can produce many thousands of variations on that experience. We present an exploratory experiment based on the StoryPlaces locative narrative system, showing how a Simple Heuristic ReaderBot can simulate readings of a located hypertext. We then provide Narrative Analytics of those readings in the form of structural, experiential/dramatic, and locative feedback. We also present three Machine Learning ReaderBots (Linear Regression, Logistic Regression, and a Feed Forward Neural Network), trained on real reading logs, and using distance, prior visits, altitude, proximity to POIs, and text similarity as an input vector to predict next node decisions with precision substantially better than random, and comparable to the Heuristic Reader. We argue that ReaderBots can create an instant audience of thousands that could give authors valuable insights into the potential experiences of their readers.

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More information

Published date: July 2018
Keywords: Hypertext, Machine Learning, creative writing

Identifiers

Local EPrints ID: 422387
URI: https://eprints.soton.ac.uk/id/eprint/422387
PURE UUID: 1eeb4ee8-9570-4d84-b3e3-b18f91b8a425
ORCID for David Millard: ORCID iD orcid.org/0000-0002-7512-2710

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Date deposited: 23 Jul 2018 16:30
Last modified: 24 Jul 2018 00:37

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