

![0_image_0.png](0_image_0.png)

![0_image_1.png](0_image_1.png)

though from Figure 5 we know that due to the alleviated uncertainty, the use of communications predictions leads to overall higher probability of success (i.e., the Coherent, Comm technique results in fewer alternative hypotheses, and thus has a better chance of being correct). However, in Figure 6-b we see that in run J the situation has changed dramatically. First, we see that the two lines are no longer similar. The line marked Coherent, Comm has greater slope than in run I, indicating that the communications predictions are not able to reduce the uncertainty, resulting in lower average accuracy. Second, we see that the temporal model results in many less errors, as evidenced by the much slower-rising slope of the line marked Coherent, Temporal. Thus in this case, the actual duration of plans matched the temporal model more accurately than in other runs.

In trying to understand this difference between runs J, B and the other runs of the system, we discovered that runs J and B involved relatively more failures on the part of team-members, including agents crashing or not responding at all. The communications predictions, however, were learned based on successful runs—and thus did not correctly predict the communication messages that would result as the team detected and recovered from the failures. Thus the uncertainty was not alleviated, and the arbitrary selection was made among relatively many hypotheses. This explains the relatively lower accuracy of the
(Coherent, Comm) technique in run J and B. This clearly shows a limitation of the simple learning approach we took, and we intend to address it in future work. However, there are other factors that influence the accuracy of the communication models, since this lower accuracy did not occur in other runs where failures have occurred.

The results of the Coherent, Temporal technique vary as well. We have been able to determine that failures cause a relative increase in the relative accuracy of the Coherent ,
Temporal technique. However, variance in the results is due to additional factors. In run C,
for instance, this technique results in relatively higher accuracy, but no failure has occurred.

Certainly, the mission specifications themselves differ between runs, machine loads cause the mission execution to run slower or faster, etc. The great variance in the temporal behavior of the system was the principal reason for our using the communication prediction. This variance is obvious in the graphs