  Learning linear temporal logic (LTL) formulas from examples labeled as
positive or negative has found applications in inferring descriptions of system
behavior. We summarize two methods to learn LTL formulas from examples in two
different problem settings. The first method assumes noise in the labeling of
the examples. For that, they define the problem of inferring an LTL formula
that must be consistent with most but not all of the examples. The second
method considers the other problem of inferring meaningful LTL formulas in the
case where only positive examples are given. Hence, the first method addresses
the robustness to noise, and the second method addresses the balance between
conciseness and specificity (i.e., language minimality) of the inferred
formula. The summarized methods propose different algorithms to solve the
aforementioned problems, as well as to infer other descriptions of temporal
properties, such as signal temporal logic or deterministic finite automata.
