Add to ORKGTemporal logics are a well established formal specification paradigm to specify the behavior of systems, and serve as inputs to industrial-strength verification tools. We report on current advances in applying deep learning to temporal logical reasoning tasks, showing that models can even solve instances where competitive classical algorithms timed out
AbstractWhile specifications and verifications of concurrent systems employ Linear Temporal Logic (L...
The effective integration of knowledge representation, reasoning and learning into a robust computat...
Verifying that systems behave as expected is a cornerstone of computing. In formal verification app...
We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging...
We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from examples. ...
In this thesis, we study logical and deep learning methods for the temporal reasoning of reactive sy...
In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language...
International audienceThis chapter illustrates two aspects of automata theory related to linear-time...
Time is a fascinating subject that has captured mankind's imagination from ancient times to the pres...
Recently, formal methods have gained significant traction for describing, checking, and synthesizing...
This paper presents an inference algorithm that can discover temporal logic properties of a system f...
International audienceSince the early 1990's, classical temporal logics have been extended with timi...
Understanding temporal commonsense concepts, such as times of occurrence and durations is crucial fo...
Formal verification techniques such as theorem proving, runtime verification, and model checking hav...
Most of AI research on temporal reasoning has been devoted to either exploring constraint-based temp...
AbstractWhile specifications and verifications of concurrent systems employ Linear Temporal Logic (L...
The effective integration of knowledge representation, reasoning and learning into a robust computat...
Verifying that systems behave as expected is a cornerstone of computing. In formal verification app...
We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging...
We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from examples. ...
In this thesis, we study logical and deep learning methods for the temporal reasoning of reactive sy...
In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language...
International audienceThis chapter illustrates two aspects of automata theory related to linear-time...
Time is a fascinating subject that has captured mankind's imagination from ancient times to the pres...
Recently, formal methods have gained significant traction for describing, checking, and synthesizing...
This paper presents an inference algorithm that can discover temporal logic properties of a system f...
International audienceSince the early 1990's, classical temporal logics have been extended with timi...
Understanding temporal commonsense concepts, such as times of occurrence and durations is crucial fo...
Formal verification techniques such as theorem proving, runtime verification, and model checking hav...
Most of AI research on temporal reasoning has been devoted to either exploring constraint-based temp...
AbstractWhile specifications and verifications of concurrent systems employ Linear Temporal Logic (L...
The effective integration of knowledge representation, reasoning and learning into a robust computat...
Verifying that systems behave as expected is a cornerstone of computing. In formal verification app...