Abstract
Based on swarm movements and computing models using multisets, a swarm automaton was introduced to construct a new computing system using swarm behavior in the computing process. In a swarm automaton, each agent changes by input and the interaction between agents, which leads to change the swarm represented by the multiset of agents. An input string is accepted by a swarm automaton depending on the conditions of the agents in the swarm. That is, an input string that leads the swarm to a specified condition is accepted. When we introduce position information for agents in a swarm automaton, the agent not only changes but also moves according to the nearby agents. In this paper, we introduce a language accepted by a swarm automaton based on the position of agents in a swarm. That is, a string is accepted when it leads to the swarm consisting of agents on the designated position. We focus on the number of agents in a swarm and consider the computing power of that swarm automaton. We show that any recursively enumerable language is accepted by a swarm automaton with only five agents using parallel transition.






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Bargmann Cornelia I, Erika Hartwieg HRH (1993) Odorant-selective genes and neurons mediate olfaction in C. Elegans. Cell 74(3):515–527
Camazine S, Franks NR, Sneyd J, Bonabeau E, Deneubourg JL, Theraula G (2001) Self-organization in biological systems. Princeton University Press, Princeton
Ehrenfeucht A, Rozenberg G (2007) Reaction systems. Fundam Inform 75(1–4):263–280
Fujioka K (2019) Swarm-based multiset rewriting computing models. In: McQuillan I, Seki S (eds) Unconventional computation and natural computation, vol 11493. Lecture notes in computer science. Springer, Cham, pp 79–93
Hopcroft JE, Ullman JD (2001) Introduction to automata theory, languages, and computation. Addison-Wesley Publishing Company, New York
Ibarra OH (2005) On membrane hierarchy in P systems. Theor Comput Sci 334(1):115–129
Kusumoto H, TKSSTe (2020) Efficiency of gastrointestinal cancer detection by nematode-nose (n-nose). J Theor Biol 34(1):73–80
Lopes YK, Trenkwalder SM, Leal AB, Dodd TJ, Groß R (2016) Supervisory control theory applied to swarm robotics. Swarm Intell 10:65–97
Minsky ML (1967) Computation: finite and infinite machines. Prentice-Hall, Hoboken
Okubo F (2014) Reaction automata working in sequential manner. RAIRO Theor Inform Appl 48(1):23–38
Okubo F, Yokomori T (2019) Decomposition and factorization of chemical reaction transducers. Theor Comput Sci 777:431–442
Okubo F, Kobayashi S, Yokomori T (2012) Reaction automata. Theor Comput Sci 429:247–257
Păun G, Rozenberg G, Salomaa A (2010) The Oxford handbook of membrane computing. Oxford University Press Inc, New York
Ramadge PJ, Wonham WM (1987) Modular feedback logic for discrete event systems. SIAM J Control Optim 25(5):1202–1218
Ramadge PJ, Wonham WM (1987) Supervisory control of a class of discrete event processes. SIAM J Control Optim 25(1):206–230
Rozenberg G, Salomaa A (eds) (1997) Handbook of formal languages. Springer, Berlin
von Mammen S, Jacob C (2007) Genetic swarm grammar programming: Ecological breeding like a gardener. In: 2007 IEEE Congress on Evolutionary Computation. IEEE Press, Singapore, pp 851–858
von Mammen S, Jacob C (2008) Evolutionary swarm design of architectural idea models. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO ’08. ACM, New York, NY, USA, pp 143–150
von Mammen S, Phillips D, Davison T, Jacob C (2010) A graph-based developmental swarm representation and algorithm. In: International Conference on Swarm intelligence. Springer, Berlin, pp 1–12
Wonham W, Cai K, Rudie K (2017) Supervisory control of discrete-event systems: a brief history - 1980–2015. IFAC PapersOnLine 50(1):1791–1797
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Fujioka, K. On the computational power of swarm automata using agents with position information. Nat Comput 21, 605–614 (2022). https://doi.org/10.1007/s11047-022-09881-7
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DOI: https://doi.org/10.1007/s11047-022-09881-7