22 research outputs found
Parametric Identification of Temporal Properties
Given a dense-time real-valued signal and a parameterized temporal logic formula with both magnitude and timing parameters, we compute the subset of the parameter space that renders the formula satisfied by the trace. We provide two preliminary implementations, one which follows the exact semantics and attempts to compute the validity domain by quantifier elimination in linear arithmetics and one which conducts adaptive search in the parameter space
An Efficient Formula Synthesis Method with Past Signal Temporal Logic
In this work, we propose a novel method to find temporal properties that lead
to the unexpected behaviors from labeled dataset. We express these properties
in past time Signal Temporal Logic (ptSTL). First, we present a novel approach
for finding parameters of a template ptSTL formula, which extends the results
on monotonicity based parameter synthesis. The proposed method optimizes a
given monotone criteria while bounding an error. Then, we employ the parameter
synthesis method in an iterative unguided formula synthesis framework. In
particular, we combine optimized formulas iteratively to describe the causes of
the labeled events while bounding the error. We illustrate the proposed
framework on two examples.Comment: 8 pages, 5 figures, conference pape
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
Inferring Properties in Computation Tree Logic
We consider the problem of automatically inferring specifications in the
branching-time logic, Computation Tree Logic (CTL), from a given system.
Designing functional and usable specifications has always been one of the
biggest challenges of formal methods. While in recent years, works have focused
on automatically designing specifications in linear-time logics such as Linear
Temporal Logic (LTL) and Signal Temporal Logic (STL), little attention has been
given to branching-time logics despite its popularity in formal methods. We
intend to infer concise (thus, interpretable) CTL formulas from a given finite
state model of the system in consideration. However, inferring specification
only from the given model (and, in general, from only positive examples) is an
ill-posed problem. As a result, we infer a CTL formula that, along with being
concise, is also language-minimal, meaning that it is rather specific to the
given model. We design a counter-example guided algorithm to infer a concise
and language-minimal CTL formula via the generation of undesirable models. In
the process, we also develop, for the first time, a passive learning algorithm
to infer CTL formulas from a set of desirable and undesirable Kripke
structures. The passive learning algorithm involves encoding a popular CTL
model-checking procedure in the Boolean Satisfiability problem