Individual and Collective Autonomous Development
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Abstract (arXiv)
The increasing complexity and unpredictability of many ICT scenarios let us envision that future systems will have to dynamically learn how to act and adapt to face evolving situations with little or no a priori knowledge, both at the level of individual components and at the collective level. In other words, such systems should become able to autonomously develop models of themselves and of their environment. Autonomous development includes: learning models of own capabilities; learning how to act purposefully towards the achievement of specific goals; and learning how to act collectively, i.e., accounting for the presence of others. In this paper, we introduce the vision of autonomous development in ICT systems, by framing its key concepts and by illustrating suitable application domains. Then, we overview the many research areas that are contributing or can potentially contribute to the realization of the vision, and identify some key research challenges.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 41 references that are in the map. First posted on arXiv 23 Sep 2021.
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References · 9
- 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
- 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 313
- 2018Large-Scale Study of Curiosity-Driven Learningcited by 280
- 2019Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Researchcited by 61
- 2020What can I do here? A Theory of Affordances in Reinforcement Learningcited by 35
- 2021Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systemscited by 24
- 2020Learning Causal Models Onlinecited by 18
- 2020Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systemscited by 7
- 2019Winning an Election: On Emergent Strategic Communication in Multi-Agent Networkscited by 1
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