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We present BioMASS, a new model to implement a spatially explicit environment that supports constant-time sensory (neighborhood search) and locomotion functions for situated multiagent systems (MAS). In contrast, the spatial models currently provided by agent-based modeling and computer simulation (ABMS) platforms have computational costs that grow quadratically with perception range and linearly with the number of agents. To conserve computation, existing ABMS models of complex systems are oversimplified, by limiting the environment size, perception ranges, or the number of agents. BioMASS achieves constant time search and locomotion for the majority of function calls by implementing a linked list, nearest-neighbor data structure. This model makes the functions largely independent of the environment size, perception range, and the number of agents. We conduct a theoretical and experimental study of BioMASS compared to other spatial models. Experiments performed using a prey-predator swarm model show that BioMASS significantly outperforms the continuous and hybrid models in terms of execution time, allowing for higher-resolution modeling and simulation of complex systems.

BioMASS, a Spatial Model for Situated Multiagent Systems That Optimizes Neighborhood Search