# Woc-Bots: An Agent-Based Approach to Decision-Making
## 3 Results  
We compared the results from our social, agent-based approach to the results produced by multiple configurations of a traditional, monolithic MLP developed in DL4]. All agent classifiers were trained on 4\(\times\) Nvidia GTX 1070 GPUs using Cuda (versions 10.0-10.1 update 2) through the DL4] library. Table 3 shows the configuration and accuracy under different training conditions for each of the 10 agents. All agent classifiers were trained in parallel, taking an average of 2.8 s for 5 epochs and 22 s for 50 epochs; there was no accuracy improvement beyond 50 epochs. Once trained, the agents can participate in any decision-making configuration without re-training their classifiers. The best performing agent classifier was the budget, vote_count, vote_average agent, with the most important feature being budget, followed by vote_count. The worst performing agent was the budget, runtime agent. We tested 10 MLP networks, with a variety of feature sets, and with one containing the final revenue. Figure 1(a) shows the accuracy for five classifier configurations when trained for 5 and 50 epochs. The figure shows the change in classifier accuracy when various features have been removed as inputs into the network. Figure 1: Agent training, initialization, movement, interaction, and voting. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **5 Epochs** & **50 Epochs** \\ \hline budget, revenue & 98\% & 100\% \\ \hline budget, vote\_average, vote\_count & 77.2\% & 77.6\% \\ \hline budget, tmdb\_popularity, vote\_average, vote\_count & 75.4\% & 75.7\% \\ \hline budget, vote\_count & 75.7\% & 75.5\% \\ \hline budget, tmdb\_popularity, tmdb\_vote\_average, tmdb\_vote\_count & 72.8\% & 74.9\% \\ \hline budget, tmdb\_vote\_count, ml\_vote\_count & 73\% & 73.4\% \\ \hline budget, ml\_vote\_average, ml\_vote\_count & 62.2\% & 64.1\% \\ \hline budget, ml\_vote\_count & 60.3\% & 61.9\% \\ \hline budget, tmdb\_vote\_average & 60.9\% & 61.4\% \\ \hline budget, runtime & 53.9\% & 56.4\% \\ \hline Average (budget, revenue agent removed) & 67.93\% & 68.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Agent Classifier Accuracy for 5 and 50 epochs. Figure 2: Comparison of MLP and Woc-Bots performance. All MLP classifiers were trained individually with an average training time of 2.6 s for 5 epochs and 21.2 s for 50 epochs. Comparing training times with the social agents, training 10 MLP classifiers in parallel took 1 min and 1 s (3 min and 32 s if computed sequentially) vs. 22 s to train 10 agents for 50 epochs. It should be noted that inference is slower using WoC-Bots; it takes an average of 260 milliseconds to test 740 examples using an MLP classifier incorporating all the features listed in Table 1, while it takes the WoC-Bots, encompassing the same feature set, an average of 13.4 s to test the same 740 examples. But, once trained, the agents can be reconfigured to compute new prediction results for different feature sets, without requiring retraining, unlike a monolithic MLP. Accuracy results from five configurations of our social, agent-based prediction system can be found in Figure 2b. We show results for three aggregation mechanisms after 50 epochs of training: (1) unweighted equal voting, (2) votes assigned based on initial classifier performance, and (3) votes assigned based on classifier performance and agent trust (described in Section 2.3), with method (3) consistently out-performing methods (1) and (2). Method (1) is represented by the blue bar, method (2) by the orange bar, and method (3) the grey bar. We tested similar configurations across our agents and MLP networks. Data from Movielens and TMDb was combined, as described in Section 2.1, with no agent receiving information from only one source. Similar to Figure 2a, Figure 2b's labels show which features were included in the interaction. Feature distribution across agents was optimized for accuracy, within the limits of available features.