Zero-Shot Generalization using Intrinsically Motivated Compositional Emergent Protocols

• URL: http://arxiv.org/abs/2105.05069v1
• Date: Tue, 11 May 2021 14:20:26 GMT
• Title: Zero-Shot Generalization using Intrinsically Motivated Compositional Emergent Protocols
• Authors: Rishi Hazra, Sonu Dixit, Sayambhu Sen
• Abstract summary: We show how compositionality can enable agents to not only interact with unseen objects but also transfer skills from one task to another in a zero-shot setting. We demonstrate how compositionality can enable agents to not only interact with unseen objects but also transfer skills from one task to another in a zero-shot setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human language has been described as a system that makes \textit{use of finite means to express an unlimited array of thoughts}. Of particular interest is the aspect of compositionality, whereby, the meaning of a compound language expression can be deduced from the meaning of its constituent parts. If artificial agents can develop compositional communication protocols akin to human language, they can be made to seamlessly generalize to unseen combinations. Studies have recognized the role of curiosity in enabling linguistic development in children. In this paper, we seek to use this intrinsic feedback in inducing a systematic and unambiguous protolanguage. We demonstrate how compositionality can enable agents to not only interact with unseen objects but also transfer skills from one task to another in a zero-shot setting: \textit{Can an agent, trained to `pull' and `push twice', `pull twice'?}.

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