Learning to solve complex tasks by growing knowledge culturally across generations
Michael Henry Tessler, Jason Madeano, Pedro A. Tsividis, Brin Harper,, Noah D. Goodman, Joshua B. Tenenbaum

TL;DR
This study demonstrates how cultural knowledge can be transmitted and accumulated across generations through language in a simulated environment, enabling AI systems to learn complex tasks more effectively.
Contribution
We introduce a novel multi-generational learning framework using minimalist video games and natural language communication to model cultural knowledge transfer.
Findings
Knowledge accumulated across generations improved game performance.
Language conveyed environmental dynamics, goals, risks, and strategies.
Multigenerational learning mirrored individual learning with unlimited attempts.
Abstract
Knowledge built culturally across generations allows humans to learn far more than an individual could glean from their own experience in a lifetime. Cultural knowledge in turn rests on language: language is the richest record of what previous generations believed, valued, and practiced, and how these evolved over time. The power and mechanisms of language as a means of cultural learning, however, are not well understood, and as a result, current AI systems do not leverage language as a means for cultural knowledge transmission. Here, we take a first step towards reverse-engineering cultural learning through language. We developed a suite of complex tasks in the form of minimalist-style video games, which we deployed in an iterated learning paradigm. Human participants were limited to only two attempts (two lives) to beat each game and were allowed to write a message to a future…
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Taxonomy
TopicsLanguage and cultural evolution · Topic Modeling
