The DOI matches its Crossref record; 34 of the 39 references it cites were found in a registry or an index.
Emergence of Grounded Compositional Language in Multi-Agent Populations
Open accessAbstract
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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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017) · arXiv · doi:10.48550/arxiv.1706.02275 · Cited by 3,377
Self-citationCounterfactual Multi-Agent Policy Gradients
Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11794 · Cited by 1,873
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Zhang, K., Yang, Z., & Başar, T. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-60990-0_12 · Cited by 1,561
Multi-agent deep reinforcement learning: a survey
Gronauer, S., & Diepold, K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-09996-w · Cited by 957
A survey and critique of multiagent deep reinforcement learning
Hernández-Leal, P., Kartal, B., & Taylor, M. E. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09421-1 · Cited by 799
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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