Abstract
Swarm-based computing models and multi-agent-based models have been investigated using parallel processing computation. Based on the preceding models and multiset computings, we propose new computing systems, called swarm systems, that are multiset rewriting systems to formalize swarms’ behaviors. A configuration in a swarm system is expressed by a multiset of agents to simulate swarm movements. Swarm automata are also introduced based on swarm systems, which accept strings by considering the configuration sequences. Transition rules in a swarm automaton are labeled by elements of an alphabet and when a configuration consists of final agents then the corresponding sequences of rule symbols are accepted. Position information for agents is added to those swarm models. We show that swarm automata with position information are universal if transition rules are applied in parallel. On the other hand, swarm automata without position information are computationally equivalent to finite state automata.
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Fujioka, K. (2019). Swarm-Based Multiset Rewriting Computing Models. In: McQuillan, I., Seki, S. (eds) Unconventional Computation and Natural Computation. UCNC 2019. Lecture Notes in Computer Science(), vol 11493. Springer, Cham. https://doi.org/10.1007/978-3-030-19311-9_8
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DOI: https://doi.org/10.1007/978-3-030-19311-9_8
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