Learning to Draw: Emergent Communication through Sketching
Learning to Draw: Emergent Communication through Sketching
Evidence that visual communication preceded written language and provided a basis for it goes back to prehistory, in forms such as cave and rock paintings depicting traces of our distant ancestors. Emergent communication research has sought to explore how agents can learn to communicate in order to collaboratively solve tasks. Existing research has focused on language, with a learned communication channel transmitting sequences of discrete tokens between the agents. In this work, we explore a visual communication channel between agents that are allowed to draw with simple strokes. Our agents are parameterised by deep neural networks, and the drawing procedure is differentiable, allowing for end-to-end training. In the framework of a referential communication game, we demonstrate that agents can not only successfully learn to communicate by drawing, but with appropriate inductive biases, can do so in a fashion that humans can interpret. We hope to encourage future research to consider visual communication as a more flexible and directly interpretable alternative of training collaborative agents.
Neural Information Processing Systems Foundation
Mihai, Daniela
f8910fe1-18e7-45b3-8923-b34b5cd136fa
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
6 December 2021
Mihai, Daniela
f8910fe1-18e7-45b3-8923-b34b5cd136fa
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Mihai, Daniela and Hare, Jonathon
(2021)
Learning to Draw: Emergent Communication through Sketching.
In Advances in Neural Information Processing Systems 34.
vol. 34,
Neural Information Processing Systems Foundation.
29 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Evidence that visual communication preceded written language and provided a basis for it goes back to prehistory, in forms such as cave and rock paintings depicting traces of our distant ancestors. Emergent communication research has sought to explore how agents can learn to communicate in order to collaboratively solve tasks. Existing research has focused on language, with a learned communication channel transmitting sequences of discrete tokens between the agents. In this work, we explore a visual communication channel between agents that are allowed to draw with simple strokes. Our agents are parameterised by deep neural networks, and the drawing procedure is differentiable, allowing for end-to-end training. In the framework of a referential communication game, we demonstrate that agents can not only successfully learn to communicate by drawing, but with appropriate inductive biases, can do so in a fashion that humans can interpret. We hope to encourage future research to consider visual communication as a more flexible and directly interpretable alternative of training collaborative agents.
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Accepted/In Press date: 28 September 2021
Published date: 6 December 2021
Venue - Dates:
35th Conference on Neural Information Processing Systems, virtual, 2021-12-06 - 2021-12-14
Identifiers
Local EPrints ID: 453107
URI: http://eprints.soton.ac.uk/id/eprint/453107
PURE UUID: 0d3cebd3-920d-4f86-8ed3-b4ab24fbbfbf
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Date deposited: 08 Jan 2022 21:29
Last modified: 10 Apr 2024 01:42
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Contributors
Author:
Daniela Mihai
Author:
Jonathon Hare
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