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Intrinsically motivated collective motion
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Charlesworth, Henry J. and Turner, Matthew S. (2019) Intrinsically motivated collective motion. Proceedings of the National Academy of Sciences of the United States of America, 116 (31). pp. 15362-15367. doi:10.1073/pnas.1822069116 ISSN 0027-8424.
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WRAP-intrinsically-motivated-collective-motion-Turner-2019.pdf - Accepted Version - Requires a PDF viewer. Download (2469Kb) | Preview |
Official URL: https://doi.org/10.1073/pnas.1822069116
Abstract
Collective motion is found in various animal systems, active suspensions and robotic or virtual agents. This is often understood using high level models that directly encode selected empirical features, such as co-alignment and cohesion. Can these features be shown to emerge from an underlying, low-level principle? We find that they emerge naturally under Future State Maximisation (FSM). Here agents perceive a visual representation of the world around them, suchasmightberecordedonasimpleretina, andthenmovetomaximise the numer of different visual environments that they expect to be able to access in the future. Such a control principle may confer evolutionary fitness in an uncertain world by enabling agents to deal with a wide variety of future scenarios. The collective dynamics that spontaneously emerge under FSM resemble animal systems in several qualitative aspects, including cohesion, co-alignment and collision suppression, none of which are explicitly encoded in the model. A multi-layered neural network trained on simulated trajectories is shown to represent a heuristic mimicking FSM. Similar levels of reasoning would seem to be accessible under animal cognition, demonstrating a possible route to the emergence of collective motion in social animals directly from the control principle underlying FSM. Such models may also be good candidates for encoding into possible future realisations of artificial “intelligent" matter, able to sense light, process information and move.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||
| Library of Congress Subject Headings (LCSH): | Artificial intelligence, Animal locomotion -- Mathematical models, Animal behavior -- Adaptation -- Simulation methods, Robotics | ||||||
| Journal or Publication Title: | Proceedings of the National Academy of Sciences of the United States of America | ||||||
| Publisher: | National Academy of Sciences | ||||||
| ISSN: | 0027-8424 | ||||||
| Official Date: | 17 July 2019 | ||||||
| Dates: |
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| Volume: | 116 | ||||||
| Number: | 31 | ||||||
| Page Range: | pp. 15362-15367 | ||||||
| DOI: | 10.1073/pnas.1822069116 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 15 July 2019 | ||||||
| Date of first compliant Open Access: | 18 July 2019 | ||||||
| RIOXX Funder/Project Grant: |
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