TY - GEN
T1 - A massively multi-agent system for discovering HIV-immune interaction dynamics
AU - Zhang, Shiwu
AU - LIU, Jiming
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - In MMAS-based biological system simulation, it is a challenging task to deal with numerous interactions among a vast number of autonomous agents. In our work, a hybrid massively multi-agent systems (MMAS) model is developed, and it incorporates the characteristics of cellular automaton (CA) and system-level mathematical equation modeling to simulate HIV-immune interaction dynamics. The mathematical equations are adopted within the site of a two-dimensional lattice. As the average high density, agent interactions can be calculated according to the equations without significantly affecting the performance of the systems studied. In the mean time, the CA model keeps the spatial characteristics of HIV evolution among the sites. The simulation based on the implemented MMAS discovers the dynamics of HIV evolution over different temporal and spatial scales, and reproduces the typical three-stage dynamics of HIV infection.
AB - In MMAS-based biological system simulation, it is a challenging task to deal with numerous interactions among a vast number of autonomous agents. In our work, a hybrid massively multi-agent systems (MMAS) model is developed, and it incorporates the characteristics of cellular automaton (CA) and system-level mathematical equation modeling to simulate HIV-immune interaction dynamics. The mathematical equations are adopted within the site of a two-dimensional lattice. As the average high density, agent interactions can be calculated according to the equations without significantly affecting the performance of the systems studied. In the mean time, the CA model keeps the spatial characteristics of HIV evolution among the sites. The simulation based on the implemented MMAS discovers the dynamics of HIV evolution over different temporal and spatial scales, and reproduces the typical three-stage dynamics of HIV infection.
UR - http://www.scopus.com/inward/record.url?scp=26844461542&partnerID=8YFLogxK
U2 - 10.1007/11512073_12
DO - 10.1007/11512073_12
M3 - Conference proceeding
AN - SCOPUS:26844461542
SN - 3540269746
SN - 9783540269748
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 173
BT - Massively Multi-Agent Systems I - First International Workshop, MMAS 2004, Revised Selected and Invited Papers
PB - Springer Verlag
T2 - 1st International Workshop on Massively Multi-Agent Systems, MMAS 2004
Y2 - 10 December 2004 through 11 December 2004
ER -
