## 3 Results
### Adaptive Learning Integration with ABMS
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Rel. Mean Cumulative Difference (\(\tilde{c}\tilde{d}_{\text{v}}\))** & **Spline Difference** \\ \hline
1 & 16.49 & 835,190 \\
2 & 0.30 & \(-\)19,820 \\
3 & 1.49 & \(-\)12,080 \\
4 & 0.35 & \(-\)25,933 \\
5 & \(-\)2.04 & \(-\)13,349 \\
6 & 1.19 & \(-\)30,501 \\
7 & \(-\)2.16 & \(-\)21,410 \\
8 & 0.16 & \(-\)4282 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Experiments’ key epidemic figures. shows, most experiments reject [PERSON]'s \(H_{0}\), alluding to the median cumulative cases are distinct. Notwithstanding, the remaining tests are never rejected, denoting that no solid evidence confirms the experiments and the reference scenario are statistically different on average. Thereupon, formal methods have proven that although RL-based learning has a meaningful impact on campus densities, nothing suggests the same happens with the epidemic. In addition, the ANOVA procedure in Table 13 confirms the verdict. In sync with earlier resolutions, Table 13 unfolds that no factor or combination of factors is significant in the epidemic outcomes, the reason being that all the \(p\)-values are higher than the 0.05 significance level. Consequently, all evidence points in the same direction. As an illustration, Figure 15 displays the estimated effect of each parameter. Unsurprisingly, these effects are quite similar to each other, as the ANOVA technique is unable to determine each factor's influence and, therefore, distributes the impact across the parameters space. Overall, these results indicate that RL-based learning successfully reduces crowds on campus to the point that the social distancing policy is obeyed on average. However, no statistically significant proof was found of the effect of adaptive learning regarding the epidemic results. The current hypothesis on that matter is that COVID-19 is so contagious that physical distancing in a small semi-enclosed community helps, but is not enough to render a substantial decline in the number of infected subjects. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **DF** & **Sum Sq.** & **Mean Sq.** & **F Stat.** & \(p\)**-Value** \\ \hline \(\alpha\) & 1 & 2,029,108 & 2,029,108 & 0.946 & 0.337 \\ \(\epsilon\) & 1 & 2,078,753 & 2,078,753 & 0.969 & 0.331 \\ \(\gamma\) & 1 & 2,165,376 & 2,165,376 & 1.01 & 0.321 \\ \(\alpha+\epsilon\) & 1 & 2,143,343 & 2,143,343 & 1 & 0.323 \\ \(\alpha+\gamma\) & 1 & 1,981,688 & 1,981,688 & 0.924 & 0.342 \\ \(\epsilon+\gamma\) & 1 & 2,219,230 & 2,219,230 & 1.035 & 0.315 \\ \(\alpha+\epsilon+\gamma\) & 1 & 2,153,498 & 2,153,498 & 1.004 & 0.322 \\ \hline Residuals & 40 & 85,767,210 & 2,144,180 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Analysis of variance of factor’s effects. Figure 15: Estimation of factor effects. ## 4 Discussion and Conclusions

This study explored the impact of introducing an adaptive learning system into an ABMS, simulating a university campus during an epidemic. Our primary aim was to assess whether this mechanism could effectively reduce gatherings and stationary infections on campus. It is important to note that our study used the COVID-19 pandemic as a case study. However, we do not aim to provide a very precise representation of the disease itself. Instead, our model's main objective is to provide a simplified yet informative portrayal of the progression of COVID-19. We draw inspiration from traditional models based on the Susceptible, Exposed, Infected, Recovered, Discharged (SEIRD) framework. To achieve this, we rely on the existing literature available during the early stages of the virus' emergence. Our ABMS involves three core elements: daily campus routines, transmission dynamics, and adaptive learning. For modeling disease transmission, we rely on established research, which shapes our assumptions about how COVID-19 spreads across the campus. When it comes to capturing human behavior in daily routines on campus, we take a comprehensive approach.