Interval Temporal Logic Decision Tree Learning

Brunello, Andrea; Sciavicco, Guido; Stan, Ionel Eduard

doi:10.1007/978-3-030-19570-0_50

Cited by 12 publications

(8 citation statements)

References 26 publications

(32 reference statements)

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“…Other approaches include signal temporal logic (Asarin et al 2011;Jin et al 2015;Bombara et al 2016;Kong, Jones, and Belta 2016), past time linear temporal logic (Arif et al 2020), interval temporal logic (Brunello, Sciavicco, and Stan 2020), property specification language (Roy, Fisman, and Neider 2020), and graph temporal logic (Xu et al 2019b), etc. Expressiveness of GNNs.…”

Section: Related Workmentioning

confidence: 99%

Bridging LTLf Inference to GNN Inference for Learning LTLf Formulae

Luo

Liang

et al. 2022

AAAI

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Learning linear temporal logic on finite traces (LTLf) formulae aims to learn a target formula that characterizes the high-level behavior of a system from observation traces in planning. Existing approaches to learning LTLf formulae, however, can hardly learn accurate LTLf formulae from noisy data. It is challenging to design an efficient search mechanism in the large search space in form of arbitrary LTLf formulae while alleviating the wrong search bias resulting from noisy data. In this paper, we tackle this problem by bridging LTLf inference to GNN inference. Our key theoretical contribution is showing that GNN inference can simulate LTLf inference to distinguish traces. Based on our theoretical result, we design a GNN-based approach, GLTLf, which combines GNN inference and parameter interpretation to seek the target formula in the large search space. Thanks to the non-deterministic learning process of GNNs, GLTLf is able to cope with noise. We evaluate GLTLf on various datasets with noise. Our experimental results confirm the effectiveness of GNN inference in learning LTLf formulae and show that GLTLf is superior to the state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Bridging LTLf Inference to GNN Inference for Learning LTLf Formulae

Luo

Liang

et al. 2022

AAAI

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“…Based on the primitive relationship Meets [25,44,45], Figure 1 depicts the interval relationships between τ i and τ j (τ i and τ j are time intervals of events ε i and ε j ) [17]:…”

Section: Complete Temporal Classesmentioning

confidence: 99%

Improving Temporal Event Scheduling through STEP Perpetual Learning

et al. 2022

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Currently, most machine learning applications follow a one-off learning process: given a static dataset and a learning algorithm, generate a model for a task. These applications can neither adapt to a dynamic and changing environment, nor accomplish incremental task performance improvement continuously. STEP perpetual learning, by continuous knowledge refinement through sequential learning episodes, emphasizes the accomplishment of incremental task performance improvement. In this paper, we describe how a personalized temporal event scheduling system SmartCalendar, can benefit from STEP perpetual learning. We adopt the interval temporal logic to represent events’ temporal relationships and determine if events are temporally inconsistent. To provide strategies that approach user preferences for handling temporal inconsistencies, we propose SmartCalendar to recognize, resolve and learn from temporal inconsistencies based on STEP perpetual learning. SmartCalendar has several cornerstones: similarity measures for temporal inconsistency; a sparse decomposition method to utilize historical data; and a loss function based on cross-entropy to optimize performance. The experimental results on the collected dataset show that SmartCalendar incrementally improves its scheduling performance and substantially outperforms comparison methods.

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“…While their work can infer STL formulas with arbitrary misclassification error on the data, the STL primitives used for the decision nodes in their trees are derived only from a predefined set. Another work is that of Brunello et al [5] which infers decision trees over Interval Temporal Logic. The decision nodes in their trees, as well, are simple formulas; usually consisting of a single temporal relation with a proposition.…”

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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Interval Temporal Logic Decision Tree Learning

Cited by 12 publications

References 26 publications

Bridging LTLf Inference to GNN Inference for Learning LTLf Formulae

Bridging LTLf Inference to GNN Inference for Learning LTLf Formulae

Improving Temporal Event Scheduling through STEP Perpetual Learning

Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach

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