How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles
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- Olaf Witkowski
- Tokyo Institute of Technology, Earth Life Science Institute
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- Takashi Ikegami
- University of Tokyo
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説明
<jats:p>We propose an approach to open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three important factors to create novelty and diversity: (a) communication generates combinatorial cooperative dynamics, (b) concurrency allows for separation of time scales, and (c) complexity and size increases push the system towards transitions in innovation. We illustrate these three components in a model computing the continuous evolution of a swarm of agents. The results, divided into three distinct applications, show how emergent structures are capable of filtering information through the bottleneck of their memory, to produce meaningful novelty and diversity within their simulated environment.</jats:p>
収録刊行物
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- Artificial Life
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Artificial Life 25 (2), 178-197, 2019-05
MIT Press
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キーワード
- FOS: Computer and information sciences
- Communication
- Intelligence
- Computer Science - Neural and Evolutionary Computing
- FOS: Physical sciences
- Biological Evolution
- Nonlinear Sciences - Adaptation and Self-Organizing Systems
- Animal Communication
- Computer Science - Distributed, Parallel, and Cluster Computing
- Animals
- Microbial Interactions
- Computer Science - Multiagent Systems
- Computer Simulation
- Distributed, Parallel, and Cluster Computing (cs.DC)
- Neural and Evolutionary Computing (cs.NE)
- Cooperative Behavior
- Adaptation and Self-Organizing Systems (nlin.AO)
- Multiagent Systems (cs.MA)
詳細情報 詳細情報について
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- CRID
- 1360286996445688832
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- ISSN
- 15309185
- 10645462
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- PubMed
- 31150290
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- 資料種別
- journal article
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- データソース種別
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- Crossref
- KAKEN
- OpenAIRE