Utilizing Multi Agent Simulation to Explore Cities

Cities are complex systems, with many dynamically changing parameters and large numbers of discrete actors. The heterogeneous nature of cities, make it difficult to generalize localized problems from that of city-wide problems. Computer modeling is becoming an increasingly important tool when examining how cities operate. Agent-based models allow for testing of different hypotheses and theories for urban change, thus leading to a greater understanding of how cities work. This paper propose a multi-agents systems/simulation model  based on integration among parallel processing, coupling with vector GIS,temporal logic analysis,multi-objective evolution optimization and the integration with the rule engine. The efficiency of our approach is shown through some experimental results, which highlights how different theories can be incorporated into one model and demonstrates how the well-known principle that local action can give rise to global pattern but also how such pattern emerges as the consequence of positive feedback and learned behavior.