State transitions are asserted within the DEDS observer model according to the probability value of the occurrence of an event. Events are thus defined as ranges for the different parameters. The problem then reduces to computing the corresponding areas under the refined distribution curves. An obvious way of using those probability values is to establish some threshold values and assert transitions according to those thresholds. It might be the case that none of the obtained probability values exceeds the set threshold value and/or all values are very low. In that case, there is a good chance that we are at either the wrong automata state. The remedy to such problems can be implemented through time proximity, that is, wait for a while (which is to be preset) till a strong probability value is registered and/or backtrack in the automaton model for the observer till a high enough probability value is asserted, a fail state is reached or the initial ambiguity is asserted. The backtracking strategy can be implemented using a stack-like structure associated with each state that has already been traversed, which includes a sorted list of the computed event probabilities and a father-state variable.