# A Rule-Based AI Method for an Agent Playing Big Two
## 4 Experimental Results and Discussion
### Experiment 2: Rule-Based AI versus Conventional AI  
In the second experiment, two rule-based AI agents played 2000 games against two conventional AI agents. We have gathered 4000 play logs from each set--4000 from the two rule-based AI agents and 4000 from the two conventional AI agents. Table 3 shows the statistics of play log data. The rule-based AI won 1460 games (73.00%) and lost 540 games (27.00%). Rule-based AI finished with a positive score 1460 times and a negative score 2540 times. Rule-based AI's mean score when winning was 16.08 (\(SD\) = 6.16) and when losing it was \(-4.22\) (\(SD\) = 2.55). In contrast, conventional AI finished with a positive score 540 times and a negative score 3460 times. Conventional AI's mean score when winning was 15.16 (\(SD\) = 6.98), and when losing it was \(-4.63\) (\(SD\) = 2.51). When testing the second hypothesis at a confidence level of 95%, we obtained a \(p\)-value of 2.5233 \(\times\) 10\({}^{-100}\). The statistics confirm that the results are statistically significant (\(p\)-value \(<\) 0.01). Thus, rule-based AI significantly outperforms conventional AI. The effect size (\(r\)) was 0.3545, which is a medium effect (0.30 \(<r<\) 0.50).  
Figure 7 shows the frequency distribution of rule-based AI agents and conventional AI agents from 4000 scores. In Figure 7a, rule-based AI agents finished with a positive score in 1460 games and a negative score in 2540 games. In Figure 7b, conventional AI  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistic** & **Rule-Based AI** & **Conventional AI** \\ \hline Win rate & 73.00\% (1460 games) & 27.00\% (540 games) \\ \hline \hline \multicolumn{4}{c}{Scores in the positive zone} \\ \hline \(f\) & 1460 & 540 \\ Mean score & 16.08 & 15.16 \\ \(SD\) score & 6.16 & 6.98 \\ \hline \multicolumn{4}{c}{Scores in the negative zone} \\ \hline \(f\) & 2540 & 3460 \\ Mean score & \(-4.22\) & \(-4.63\) \\ \(SD\) score & 2.55 & 2.51 \\ \hline \multicolumn{4}{c}{Overall statistics concerning all scores} \\ \hline Total & 4000 scores & 4000 scores \\ \(p\)-value & & \(2.5233\times 10^{-100}\) \\ Effect size (\(r\)) & & 0.3545 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of play log data from 2000 games of rule-based AI versus conventional AI.  
agents finished with a positive score in 540 games and a negative score in 3460 games. We compared the frequency distribution of all game scores between rule-based AI and conventional AI, as shown in Figure 7c. In the positive zone, the percentage of rule-based AI was 73.00%, and the percentage of conventional AI was 27.00%. The rule-based AI agents ended the game with a positive score (won) 46% more often than the conventional AI agents. The rule-based AI agents tended to end a game with a positive score of 10 to 23, higher than the conventional AI agents, which had positive scores of 8 to 21. The strategies to maximize the winning score worked because rule-based AI agents often achieved a high winning score. In the negative zone, the percentage of rule-based AI was 42.33%, and the percentage of conventional AI was 57.66%. The conventional AI agents lost with many cards left 15.33% more often than the rule-based AI agents. The rule-based AI agents tended to end a game with a negative score of \(-7\) to \(-1\), better than the conventional AI agents, which had negative scores of \(-8\) to \(-1\). The rule-based AI agents could minimize the number of cards left when the chance of winning was low. We can conclude that rule-based AI significantly outperforms conventional AI.