In Search of Emergent Problem Solving in Intelligent Active Matter
ORAL · Invited
Abstract
Active matter is a field where motile agents powered by energy dissipative mechanisms move out of thermal equilibrium and form novel, emergent patterns and dynamics, but typically there is no intelligence to the system, each agent simply behaves as an automaton. Typically the agents have no sensors or the ability to make decisions, their motions are entirely due to local physical laws. Concommitent with the lack of decision making ability, the agents cannot communicate with each other except physically via physical forces. We can call this dumb active matter. We are developing a form of intelligent active matter where the agents communicate with each other over extended distances, have memories and code running internally where their actions are based on previous experiences and their internal records of changing environments which both sustain them and are hostile. The agents are deliberately a mix of digital and analog computers, so we believe the collective dynamics cannot be easily simulated on a conventional digital computer. Perhaps by 5 March we will succeed in showing that such an ensemble of computationally complex, interacting agents can begin to show the problem solving abilities that wet life forms can exhibit.
*This work was supported by NSF PHY-1659940.
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Publication:10 Gao Wang,Trung V. Phan, Shengkai Li, Jing Wang, Yan Peng, Guo Chen, Junle Qu, Daniel I. Goldman, Simon A. Levin, Kenneth Pienta, Sarah Amend and Robert H. Austin and Liyu Liu , Robots as models of evolving systems, Proceedings of the National Academy of Sciences, 119:12, (2022)
2) Gao Wang , Trung V. Phan, Shengkai Li, Michael Wombacher, Junle Qu, Yan Peng, Guo Chen, Daniel I. Goldman, Simon A. Levin, Robert H. Austin, and Liyu Liu, Emergent Field-Driven Robot Swarm States, Physical Review Letters 126, 108002 (2021)
3) Trung V. Phan, Gao Wang, Liyu Liu, and Robert H. Austin, Bootstrapped Motion of an Agent on an Adaptive Resource Landscape, Symmetry, 13, 225. https://doi.org/10.3390/sym13020225 (2021)