Fuzzy Time in LTL

In the last years, the adoption of active systems has increased in many fields of computer science, such as databases, sensor networks, and software engineering. These systems are able to automatically react to events, by collecting information from outside and internally generating new events. However, the collection of data is often hampered by uncertainty and vagueness that can arise from the imprecision of the monitoring infrastructure, unreliable data sources, and networks. The decision making mechanism used to produce a reaction is also imprecise, and cannot be evaluated in a crisp way. It depends on the evaluation of vague temporal constraints, which are expressed on the collected data by humans. Despite fuzzy logic has been mainly conceived as a mathematical abstraction to express vagueness, no attempt has been made to fuzzify the temporal modalities. Existing fuzzy languages do not allow us to represent temporal properties, such as “almost always” and “soon”. Indeed, the semantics of existing fuzzy temporal operators is based on the idea of replacing classical connectives or propositions with their fuzzy counterparts. To overcome these limitations, we propose a temporal framework, FTL (Fuzzy-time Temporal Logic), to express vagueness on time. This framework formally defines a set of fuzzy temporal modalities, which can be customized by choosing a specific semantics for the connectives. The semantics of the language is sound, and the introduced modalities respect a set of expected mutual relations. We also prove that under the assumption that all events are crisp, FTL reduces to LTL. Finally, for some of the possible fuzzy interpretations of the connectives, we identify adequate sets of temporal operators, from which it is possible to derive all the others.