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Emergence of Grounded Compositional Language in Multi-Agent Populations
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By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations. Towards this end, we propose a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language. This language is represented as streams of abstract discrete symbols uttered by agents over time, but nonetheless has a coherent structure that possesses a defined vocabulary and syntax. We also observe emergence of non-verbal communication such as pointing and guiding when language communication is unavailable.
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“While some studies in the machine-learning emergent communication literature have focused on other reinforcement learning settings (Foerster et al., 2016;Mordatch & Abbeel, 2018, e.g.,), they have not made direct contact with general, cognitively-motivate principles that are believed to underlie human language, such as IB.”
From Towards Human-Like Emergent Communication via Utility, Informativeness, and Complexity (2025) · cites this paper as Mordatch & Abbeel, 2018
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“…some studies in the machine-learning emergent communication literature have focused on other reinforcement learning settings (Foerster et al., 2016; Mordatch & Abbeel, 2018, e.g.,), they have not made direct contact with general, cognitively-motivate principles that are believed to underlie human…”
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Zhang, Y., Shi, D., Wu, Y., Zhang, Y., Wang, L., & She, F. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-67537-0_36 · Cited by 2
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Metadata
- Authors
- Mordatch, I., & Abbeel, P.
- Affiliations
- OpenAI (United States) (US)
- University of California, Berkeley (US)
- ORCID
- Abbeel, Pieter
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-25
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2602275733
- Cited by
- 440
- References
- 39
- Citations by year
- 2026: 24 · 2025: 43 · 2024: 60 · 2023: 68 · 2022: 55 · 2021: 45 · 2020: 57 · 2019: 43 · 2018: 27
- FWCI
- 397.13
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Social Sciences › Cultural Studies › Language and cultural evolution
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- compositional language, language emergence, multi-agent systems, nonverbal communication, pointing, guiding, human-agent interaction, goal-oriented communication
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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