YOYO* is unable to represent hypotheses in which some team-members take one transition, and others do not—unless these two different groups of members form different subteams that are represented in the team-hierarchy, and the different transitions are tagged as being allowed for the different subteams.

The value of pxy is also interpreted differently, in a very critical way. Where in the previous sections it was taken to represent the probability that a specific individual will communicate when a transition X → Y is taken, in YOYO* its value represents instead the probability that one or more team-members will communicate when the transition is taken by the team. Thus it no longer refers to individual agents, but to a (sub-)team. In this way, YOYO* solves the issue of how to represent predictions of the type "at least one team-member will communicate when this step is reached", discussed previously.

For example, suppose YOYO* sets the belief that the team is executing the Landing-Zone-Maneuvers plan-step to some probability p. Landing-Zone-Maneuvers, in YOYO*, has two (first) children: Escort-Ops and Transport-Ops , to be executed by members of th ESCORT and TRANSPORT subteams, respectively.  Unlike in the individual agent case, the probability p should not be divided among these two ildren, but should be duplicated to them: A belief that the entire team is executing Landing-Zone-Maneuvers implies an equally-likely belief that the TRANSPORT subteam is executing Transport-Ops ,
and that the ESCORT subteam is executing Escort-Ops. We explain YOYO*'s operation in det ail below: No message is observed (lines 1–2).   Since no observations are available, the state of the entire team is jointly propagated forward in time by calling T EAM - P ROPAGATE - FORWARD
(Algorithm 7, Appendix A). This is a slightly modified version of the PROPAGATE-FORWARD
(Algorithm 3) that takes different subteams into account in propagating beliefs:  Given some total outgoing probability (either to a sibling or child transition), if the outgoing transitions are to be taken by different teams where one team is not an ancestor of another (such as the TRANSPORT and ESCORT sub-teams), the same total probability would be used for each transition, instead of splitting the outgoing probability between the transitions. Appropriately, T EAM -P ROPAGATE -FORWARD relies on a modified version of the P ROPAGATE - DOWN algorithm (Algorithm 2), called TEAM-PROPAGATE-DOWN (Algorithm 6, Appendix A). This latter algorithm is also used in the incorporation of evidence (lines 3–27). The run-time complexity of the propagation process is O ( M ).

One or more messages are observed (lines 3-7).  If one or more messages are observed (since YOYO* is a single algorithm monitoring multiple potential message senders, more than one message may be observed at once), YOYO* begins to incorporate these observations into the maintained beliefs about the team. This process is somewhat similar to the I NCORPORATE - E VIDENCE algorithm, described earlier (Algorithm 1), but takes into account multiple observations (since all N agents may have sent a message). Multiple messages
(from different agents) may all refer to the same plan, but YOYO* must not incorporate evidence for them multiple times.

The simple loop (lines 5–7) builds the set I (of initialized plans) and E (of terminated plans) by going over all incoming messages that have arrived at time t.  The run-time complexity of this process (in the worst case) is O(N). Here, YOYO* does better than the