Emergent Systematic Generalization in a Situated Agent

By Felix Hill, Andrew Kyle Lampinen, R. Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro. 2019

39 citations recorded by the source corpus.

Subfields: Natural Language Processing

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Citations in the corpus

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020, background): “Likewise, as one recent paper (Hill et al., 2019) put it, “the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated””
Language Conditioned Imitation Learning Over Unstructured Data (2020, methodology): “However, prior work has often studied 2D environments [24, 32, 27, 52] and simpliﬁed actuators [48, 18, 12].”
Grounding Language in Play (2020, background): “Prior work typically studies restricted observation spaces (e.g. games (Goyal et al., 2019), 2D gridworlds (Yu et al., 2018)), simpliﬁed actuators, (e.g. binary pick and place primitives (Hill et al., 2019)), and synthetic language data (Hermann et al., 2017).”
The NetHack Learning Environment (2020, methodology): “We apply a ConvNet (red) to all visible glyph embeddings as well as another ConvNet (blue) to the 9×9 crop of glyphs around the agent to create a dedicated egocentric representation for improved generalization [32, 71].”
A Benchmark for Systematic Generalization in Grounded Language Understanding (2020, background): “2017a; Hill et al., 2019), but they do not speciﬁcally test for a model’s ability to perform rule-based generalization, or do so only in limited contexts.”
Interactive Language: Talking to Robots in Real Time (2022, background): “prior work in broader AI research [1]–[6], [11] has sought a more convenient form of specification in the form of natural language conditioning (survey [27]), with some results on physical robots [7]–[9], [12].”
Extending Machine Language Models toward Human-Level Language Understanding (2019, background): “In (66), an agent was trained to identify, lift, carry and place objects relative to other objects in a virtual room, as speciﬁed by simpliﬁed language instructions.”
Neuroevolution of self-interpretable agents (2020, background): “[3, 32, 45, 46, 52, 58, 70, 87, 102, 106]) that approach the generalization problem, and future work will continue to develop on these ideas to push the generalization abilities proposed in more general domains [7, 16, 17, 49, 59, 70, 105].”
Vygotskian Autotelic Artificial Intelligence: Language and Culture Internalization for Human-Like AI (2022, background): “12,86 Red is an abstraction over a continuous space of colors.”