Exploration on the modeling method of complex dynamic system integrating multi-agent and hypergraph

doi:10.11996/JG.j.2095-302X.2023030599

Abstract

Abstract:

Most systems can be abstracted as complex systems in nature and human society. Given the increasing complexity of today’s complex systems, there is an urgent need for advanced and mature complex system theories and methods for modeling research and processing. However, the current graph-based modeling methods used in complex systems encounter difficulties in depicting the extremely complex connections between nodes and the higher-order relationships between them. Additionally, these methods face challenges in effectively portraying the intelligent perception, decision-making, and control of complex systems. A modeling method for complex dynamic systems integrating multi-agents and hypergraphs was proposed to address these issues. This model dynamically evolved from several different evolutionary angles to specifically describe complex dynamic systems. This model enabled perception, decision-making, and control by conferring agent nodes with intelligent features in complex systems and better describing the higher-order relationships between agent nodes. As a result, this modeling approach provided novel ideas and methods for the study of the intelligence theory of complex systems.

Key words: multi-agents, hypergraphs, complex systems, modeling, complex networks, hypernetworks

CLC Number:

TP391

WANG Peng-fei, TAO Ti-wei, JIAO Dian, SHEN Yan-ming, ZHOU Dong-sheng, ZHANG Qiang. Exploration on the modeling method of complex dynamic system integrating multi-agent and hypergraph[J]. Journal of Graphics, 2023, 44(3): 599-608.

Figures/Tables 19

Fig. 1 Multi-agent system

Fig. 2 Traditional graph and hypergraph ((a) Traditional graph; (b) Hypergraph)

Fig. 3 A model that integrating multi-agent and hypergraph

Fig. 4 A traffic model that integrating multi-agent and hypergraph

Fig. 5 The initial state of the model

Fig. 6 The evolution process (1) of the model

Fig. 7 The evolution process (2) of the model

Fig. 8 The evolution process (3) of the model ((a) Add new node; (b) Pick orphaned node)

Fig. 9 The evolution process (4) of the model

Fig. 10 Taxi paths in Shanghai ((a) With satellite map; (b) Without satellite map)

Fig. 11 Simplified hypergraph traffic model

Fig. 12 A hypergraph traffic model with marked nodes (local)

Fig. 13 The distribution of node hyperdegree

Fig. 14 The distribution of hyperedge degree

Fig. 15 The distribution of node intensity

Fig. 16 The relationship of node hyperdegree and node intensity

Fig. 17 The distribution of hyperedge intensity

Fig. 18 The relationship between hyperedge degree and hyperedge intensity

Fig. 19 The distribution of hyperedge cardinality

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