Fuzzy Time in Linear Temporal Logic

Abstract

In the past years, the adoption of adaptive systems has increased in many fields of computer science, such as databases and software engineering. These systems are able to automatically react to events by collecting information from the external environment and generating new events. However, the collection of data is often hampered by uncertainty and vagueness. The decision-making mechanism used to produce a reaction is also imprecise and cannot be evaluated in a crisp way, as it depends on vague temporal constraints expressed by humans. Logic has been extensively used as an abstraction to express vagueness in the satisfaction of system properties, as well as to enrich existing modeling formalisms. However, existing attempts to fuzzify the temporal modalities still have some limitations. Existing fuzzy temporal languages are generally obtained from classical temporal logic by replacing classical connectives or propositions with their fuzzy counterparts. Hence, these languages do not allow us to represent temporal properties, such as "almost always" and "soon," in which the notion of time is inherently fuzzy. To overcome these limitations, we propose a temporal framework, fuzzy-time temporal logic (FTL), to express vagueness on time. This framework formally defines a set of fuzzy temporal modalities that 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 mutual relations. We also prove that under the assumption that all events are crisp, FTL reduces to linear temporal logic (LTL). Moreover, 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 of the other ones.

Quantitative model checking of linear-time properties based on generalized possibility measures

“…In this paper, we studied Future case study needs to be provided. Another direction is to study the expressiveness of GPoLTL formulae and the model checking for GPoLTL formulae in general, and fuzzy time in GPoLTL as discussed in [12,25].…”

“…Furthermore, for the application to quantitative models and quantitative specifications, quantitative model-checking approaches have been proposed recently. Different approaches are applicable to different models types including timed ( [2]), probabilistic and stochastic ( [14]), multi-valued ( [3][4][5]), quality of service or soft constraints ( [24]), discounted sources-restricted ( [1,6]), possibilistic ( [20][21][22]) or fuzzy ( [12,25,26], etc, methods.…”

Reasoning about Quality and Fuzziness of Strategic Behaviours

“…Also the fragment of SL[F ] with only temporal operators and functions ∨ and ¬ corresponds to Fuzzy Lineartime Temporal Logic [51,43]. Note that by equipping F with adequate functions, we can capture various classic fuzzy interpretations of boolean operators, such as the Zadeh, Gödel-Dummett or Łukasiewicz interpretations (see for instance [43]). However the interpretation of the temporal operators is fixed in our logic.…”

Formal model for user‐centred adaptive mobile devices

“…Instead non‐functional requirements maybe represented as soft goals or probabilistic patterns [22]. Fuzzy expression of requirements is adopted in [23]. Modelling and requirement‐based adaptation has been proposed in [24].…”