# Visualization of Emergency Evacuation Physical Behavior under Multi-Agent Decision-Making
## 7 Conclusions  
In response to the problems of large randomness of pedestrian movement and the many factors affecting their behavior and complex walking behavior in evacuation research, this study proposes a multi-agent design method based on decision theory, using the multi-agent modeling simulation method to model the evacuated individual subsystem and set up the panic value, physiological state, and personality of evacuees. Based on the multi-agent type and internal state module, the multi-agent decision-making problem in the emergency evacuation environment was solved by the \(A^{*}\) algorithm, which enables the multi-agent model to simulate the decision-making of evacuees in real situations and makes the evacuation more realistic. Finally, JAVA code was coded to embed the improved multi-agent into the social force model via AnyLogic software to realize the evacuation process. The data from the evacuation process was fed back in real-time through line graphs and pictures by setting up functions, events, etc. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Floor** & **The Situation of Each Floor** & **Option 1** & **Option 2** & **Option 3** & **Option 4** & **Option 5** \\ \hline \multirow{4}{*}{Firth floor} & Number of evacuees at No. 1 stair & 154 & 184 & 167 & 184 & 124 \\  & Consumed time & 260.81 & 290.89 & 276.59 & 285.93 & 240.73 \\  & Number of evacuees at No. 2 stair & 214 & 184 & 201 & 184 & 121 \\  & Consumed time & 305.40 & 282.71 & 298.73 & 276.87 & 231.91 \\  & Number of evacuees at No. 1 stair & 240 & 276 & 259 & 276 & 186 \\  & Consumed time & 372.16 & 439.01 & 403.98 & 430.73 & 320.79 \\  & Number of evacuees at No. 2 stair & 312 & 276 & 293 & 276 & 183 \\  & Consumed time & 509.31 & 443.20 & 481.30 & 436.91 & 316.67 \\  & Number of evacuees at No. 1 stair & 319 & 368 & 338 & 368 & 248 \\  & Consumed time & 447.73 & 498.48 & 482.85 & 489.68 & 405.16 \\  & Number of evacuees at No. 2 stair & 417 & 368 & 398 & 368 & 244 \\  & Consumed time & 514.28 & 487.67 & 498.92 & 478.36 & 400.92 \\  & Number of evacuees at No. 1 stair & 398 & 460 & 430 & 460 & 310 \\  & Consumed time & 500.30 & 579.24 & 544.75 & 565.43 & 468.85 \\  & Number of evacuees at No. 2 stair & 522 & 460 & 490 & 460 & 305 \\  & Consumed time & 600.57 & 558.97 & 573.49 & 553.90 & 459.09 \\  & Number of evacuees & 483 & 543 & 510 & 543 & 310 \\  & Consumed time & 523.60 & 589.87 & 575.94 & 579.68 & 497.94 \\  & Number of evacuees & 613 & 553 & 586 & 553 & 786 \\  & Consumed time & 626.17 & 586.96 & 609.42 & 577.57 & 520.29 \\
**Total** & Total evacuees & & & 1096 & & \\  & Total evacuation time & 626.17 & 589.87 & 609.42 & 579.68 & 520.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._The simulation reproduces the pedestrian evacuation process in an emergency evacuation situation on a constructed platform, representing three types of behavior typical of pedestrians in groups, obstacle evasion, and exit competition. In addition, an exemplary analysis of a large student apartment building was carried out in this study, with appropriate optimization solutions proposed by the simulation results to improve its evacuation capacity. However, the multi-agent setting for evacuation managers was missing during the study. Therefore, the second step will be to step up the research on multi-agent and strengthen the multi-agent design of evacuation managers to strengthen the evacuation capacity. Additionally, we should try to combine multi-agent with other evacuation models to compare the evacuation simulation capability between different models. This study considers the problem of evacuee behavior and decision-making and, using decision theory carries out a multi-agent design based on human intelligence to explore the impact of multiple basic behaviors on the evacuation process. However, there is still a lack of behavioral fusion, so further exploration of behavioral fusion and behavioral selection mechanisms is needed to achieve both behavioral complexity and high intelligence to achieve a more efficient evacuation process.