• Corpus ID: 231846703

Symbolic Behaviour in Artificial Intelligence

@article{Santoro2021SymbolicBI,
  title={Symbolic Behaviour in Artificial Intelligence},
  author={Adam Santoro and Andrew Kyle Lampinen and Kory Wallace Mathewson and Timothy P. Lillicrap and David Raposo},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.03406}
}
The human ability to use symbols has yet to be replicated in machines. Bridging the gap requires considering how symbol meaning is established: if it is symbol users who agree-upon symbol meaning, then symbol-use comprises behaviours that navigate agreements about meaning. We leverage this insight to articulate graded symbolic behaviours, including constructing new symbols, altering prior ones, and introspecting about meaning and reasoning processes. We then evaluate contemporary AI methods… 
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19 Citations
Background Citations
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19 Citations

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Language and culture internalization for human-like autotelic AI

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Explanatory Learning: Beyond Empiricism in Neural Networks

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Meta-Referential Games to Learn Compositional Learning Behaviours

A novel benchmark is proposed to investigate agents’ abilities to exhibit CLBs by solving a domain-agnostic version of the BP, and a meta-learning extension of referential games is proposed, entitled Meta-Referential Games, to build this benchmark, that is named Symbolic Behaviour Benchmark (S2B).

Meaning without reference in large language models

The widespread success of large language models (LLMs) has been met with skepticism that they possess anything like human concepts or meanings. Contrary to claims that LLMs possess no meaning…

Language models show human-like content effects on reasoning

This work hypothesized that language models would show human-like content content on abstract reasoning problems, and explored this hypothesis across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task.

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This work finds neural networks are able to learn basic equality (mathematical identity), sequential equality problems, and a complex, hierarchical equality problem with only basic equality training instances ("zero-shot" generalization).

Symbol Emergence and The Solutions to Any Task

It is argued that an agent which always constructs what is called an Intensional Solution would qualify as artificial general intelligence, and how natural language may emerge and be acquired by such an agent, conferring the ability to model the intent of other individuals labouring under similar compulsions.

Towards Teachable Autonomous Agents

The purpose of this paper is to elucidate the key obstacles standing in the way towards the design of teachable and autonomous agents and focus on autotelic agents, i.e. agents equipped with forms of intrinsic motivations that enable them to represent, self-generate and pursue their own goals.

Tell me why! - Explanations support learning of relational and causal structure

It is shown that language can help agents learn challenging relational tasks, and which aspects of language contribute to its benefits are examined, which suggest that language description and explanation may be powerful tools for improving agent learning and generalization.

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