Long-Term Interaction and Persistence of Engagement for Musical Interaction using a Genetic Algorithm
Pages 239 - 241
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.
Supplementary Material
Long-Term Interaction and Persistence of Engagement for Musical Interaction using a Genetic Algorithm presentation video. The video describes the concept and reasoning, the methodology and focuses on the choice of fitness function. It also includes two short musical excerpts created by the system and proposes future work as a post processor.
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References
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Published In
November 2020
304 pages
ISBN:9781450380546
DOI:10.1145/3406499
- General Chairs:
- Mohammad Obaid,
- Omar Mubin,
- Yukie Nagai,
- Program Chairs:
- Hirotaka Osawa,
- Yomna Abdelrahman,
- Morten Fjeld
Copyright © 2020 Owner/Author.
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Published: 10 November 2020
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HAI '20
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HAI '20: 8th International Conference on Human-Agent Interaction
November 10 - 13, 2020
Virtual Event, USA
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