Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

• URL: http://arxiv.org/abs/2205.11558v1
• Date: Mon, 23 May 2022 18:17:58 GMT
• Title: Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
• Authors: Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Nathaniel D. Daw, Jonathan D. Cohen, Karthik Narasimhan, Thomas L. Griffiths
• Abstract summary: We show that agents trained by meta-learning may acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and from programs induced to generate such tasks guides them toward human-like inductive biases.
• Score: 27.79626958016208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Strong inductive biases are a key component of human intelligence, allowing people to quickly learn a variety of tasks. Although meta-learning has emerged as an approach for endowing neural networks with useful inductive biases, agents trained by meta-learning may acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and from programs induced to generate such tasks guides them toward human-like inductive biases. Human-generated language descriptions and program induction with library learning both result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without library learning), suggesting that the abstraction supported by these representations is key.

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