Graph Temporal Logic Inference for Classification and Identification

Xu, Zhe; Nettekoven, Alexander; Julius, A. Agung; Topcu, Ufuk

doi:10.1109/cdc40024.2019.9029181

Cited by 26 publications

(23 citation statements)

References 23 publications

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“…The inference problem of temporal logic, in general, has gained popularity in the recent years. Apart from LTL, this problem has been looked at for a variety of logics, including Past Time Linear Temporal Logic (PLTL) [1], Signal Temporal Logic (STL) [19,10,20,12,2,9], Property Specification Language (PSL) [17] and several others [21,22,24].…”

Section: Related Workmentioning

confidence: 99%

Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach

Gaglione,

Neider,

Roy

et al. 2021

Preprint

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We address the problem of inferring descriptions of system behavior using Linear Temporal Logic (LTL) from a finite set of positive and negative examples. Most of the existing approaches for solving such a task rely on predefined templates for guiding the structure of the inferred formula. The approaches that can infer arbitrary LTL formulas, on the other hand, are not robust to noise in the data. To alleviate such limitations, we devise two algorithms for inferring concise LTL formulas even in the presence of noise. Our first algorithm infers minimal LTL formulas by reducing the inference problem to a problem in maximum satisfiability and then using off-the-shelf MaxSAT solvers to find a solution. To the best of our knowledge, we are the first to incorporate the usage of MaxSAT solvers for inferring formulas in LTL. Our second learning algorithm relies on the first algorithm to derive a decision tree over LTL formulas based on a decision tree learning algorithm. We have implemented both our algorithms and verified that our algorithms are efficient in extracting concise LTL descriptions even in the presence of noise.

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Section: Related Workmentioning

confidence: 99%

Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach

Gaglione,

Neider,

Roy

et al. 2021

Preprint

Self Cite

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“…Definition 2.8 (Graph-temporal trajectory). [35] A graph-temporal trajectory on a graph G is a tuple д = (s, y), where s : T → S assigns a node label for each node i ∈ V at each time index t ∈ T, and y : T → Y assigns a edge label for each edge e i ∈ E at each time index t ∈ T. The first item in the labels is for time index 1, and the second item in the labels is for time index 2. Definition 2.9 (Node and edge propositions).…”

Section: Graph Temporal Logicmentioning

confidence: 99%

“…Our first contribution is to represent spatial-temporal tasks in a specification language called graph temporal logic (GTL) [35]. GTL formulas represent tasks such as "the police officer at node 3 or their neighboring officers should visit intersection labeled as blue in every two hours".…”

Section: Introductionmentioning

confidence: 99%

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Policy Synthesis for Factored MDPs with Graph Temporal Logic Specifications

Cubuktepe,

Xu,

Topcu

2020

Preprint

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We study the synthesis of policies for multi-agent systems to implement spatial-temporal tasks. We formalize the problem as a factored Markov decision process subject to so-called graph temporal logic specifications. The transition function and the spatial-temporal task of each agent depend on the agent itself and its neighboring agents. The structure in the model and the specifications enable to develop a distributed algorithm that, given a factored Markov decision process and a graph temporal logic formula, decomposes the synthesis problem into a set of smaller synthesis problems, one for each agent. We prove that the algorithm runs in time linear in the total number of agents. The size of the synthesis problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. We demonstrate the algorithm in case studies on disease control and urban security. The numerical examples show that the algorithm can scale to hundreds of agents.

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“…Similarly, a method for learning signal classifiers from data was presented in [52], by means of incrementally learning an STL formulae. Other algorithms were introduced to infer GTL formulas from data in [123], which were used for classification and identification.…”

Section: B Classificationmentioning

confidence: 99%

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

et al. 2020

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Formal methods play an important role in testing and verifying software quality, especially in modern society with rapid technological updates. Learning-based techniques have been extensively applied to learn (a model or model-free) for formal verification and to learn system specifications, and resulted in numerous contributions. Due to the fact that adequate system models are often difficult to design manually and manual definition of specifications for such software systems gets infeasible, which motivate new research directions in learning models and/or specifications from observed system behaviors automatically. This paper mainly concentrates on learning-based techniques in formal methods area. An up-to-date overview of the current state-of-the-art in learning-based formal methods is provided in the paper. This paper is not a comprehensive survey of learning-based techniques in formal methods area, but rather as a survey of the taxonomy, applications and possible future directions in learning-based formal methods.

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Graph Temporal Logic Inference for Classification and Identification

Cited by 26 publications

References 23 publications

Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach

Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach

Policy Synthesis for Factored MDPs with Graph Temporal Logic Specifications

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

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