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Long-Term Interaction and Persistence of Engagement for Musical Interaction using a Genetic Algorithm

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Abstract

Current research in human-agent interaction primarily focuses on short term interaction and rarely addresses day to day use. We propose a prototype system based on a genetic algorithm that places long term interaction as the core design goal. The goal of this system is to develop stand alone long-term development and provide a platform for future post-processing of deep learning generations. This paper addresses these issues through the domain of musical interaction and improvisation, a field that incorporates dialogue-like interaction built on stylistic constraints. We contend that the objectives of continual knowledge development and building relationships are key to long-term human interaction, and design the genetic algorithm specifically around these concepts. Our eventual goal of the prototype is a future application of post processing for deep learning generative systems.

Original languageEnglish
Title of host publicationHAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction
EditorsMohammad Obaid, Omar Mubin, Yukie Nagai, Hirotaka Osawa, Yomna Abdelrahman, Morten Fjeld
PublisherAssociation for Computing Machinery, Inc
Pages239-241
Number of pages3
ISBN (Electronic)9781450380546
DOIs
Publication statusPublished - 10 Nov 2020
Externally publishedYes
Event8th International Conference on Human-Agent Interaction, HAI 2020 - Virtual, Online, Australia
Duration: 10 Nov 202013 Nov 2020

Publication series

NameHAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction

Conference

Conference8th International Conference on Human-Agent Interaction, HAI 2020
Country/TerritoryAustralia
CityVirtual, Online
Period10/11/2013/11/20

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