41 Search Results for "Sciavicco, Guido"

Volume

LIPIcs, Volume 147

26th International Symposium on Temporal Representation and Reasoning (TIME 2019)

TIME 2019, October 16-19, 2019, Málaga, Spain

Editors: Johann Gamper, Sophie Pinchinat, and Guido Sciavicco

Document
DOI: 10.4230/LIPIcs.TIME.2025.13
Temporal Ensemble Logic for Integrative Representation of the Entirety of Clinical Trials

Authors: Xiaojin Li, Yan Huang, Rashmie Abeysinghe, Zenan Sun, Hongyu Chen, Pengze Li, Xing He, Shiqiang Tao, Cui Tao, Jiang Bian, Licong Cui, and Guo-Qiang Zhang

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
Clinical trials are typically specified with protocols that define eligibility criteria, treatment regimens, follow-up schedules, and outcome assessments. Temporality is a hallmark of all clinical trials, reflected within and across trial components, with complex dependencies unfolding across multiple time points. Despite their importance, clinical trial protocols are described in free-text format, limiting their semantic precision and the ability to support automated reasoning, leverage data across studies and sites, or simulate trial execution under varying assumptions using Real-World Data. This paper introduces a formalized representation of clinical trials using Temporal Ensemble Logic (TEL). TEL incorporates metricized modal operators, such as "always until t" (□_t) and "possibly until t" (◇_t), where t is a time-length parameter, to offer a logical framework for capturing phenotypes in biomedicine. TEL is more expressive in syntax than classical linear temporal logic (LTL) while maintaining the simplicity of semantic structures. The attributes of TEL are exploited in this paper to formally represent not only individual clinical trial components, but also the timing and sequential dependencies of these components as a whole. Modeling strategies and demonstration case studies are provided to show that TEL can represent the entirety of clinical trials, whereby providing a formal logical framework that can be used to represent the intricate temporal dependencies in trial structure specification. Since clinical trials are a cornerstone of evidence-based medicine, serving as the scientific basis for evaluating the safety, efficacy, and comparative effectiveness of therapeutic interventions, results reported here can serve as a stepping stone that leads to scalable, consistent, and reproducible representation and simulation of clinical trials across all disease domains.
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Xiaojin Li, Yan Huang, Rashmie Abeysinghe, Zenan Sun, Hongyu Chen, Pengze Li, Xing He, Shiqiang Tao, Cui Tao, Jiang Bian, Licong Cui, and Guo-Qiang Zhang. Temporal Ensemble Logic for Integrative Representation of the Entirety of Clinical Trials. In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 13:1-13:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{li_et_al:LIPIcs.TIME.2025.13,
  author =	{Li, Xiaojin and Huang, Yan and Abeysinghe, Rashmie and Sun, Zenan and Chen, Hongyu and Li, Pengze and He, Xing and Tao, Shiqiang and Tao, Cui and Bian, Jiang and Cui, Licong and Zhang, Guo-Qiang},
  title =	{{Temporal Ensemble Logic for Integrative Representation of the Entirety of Clinical Trials}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{13:1--13:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.13},
  URN =		{urn:nbn:de:0030-drops-244595},
  doi =		{10.4230/LIPIcs.TIME.2025.13},
  annote =	{Keywords: Temporal ensemble logic, Clinical trials, Logic-based modeling}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.TIME.2025.19
Temporal Association Rules from Motifs (Short Paper)

Authors: Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
A motif is defined as a frequently occurring pattern within a (multivariate) time series. In recent years, various techniques have been developed to mine time series data. However, only a few studies have explored the idea of using motif discovery in temporal association rule mining. Interval-based temporal association rules have been recently defined and studied, along with the temporal version of the known frequent patterns, and therefore, association rule extraction algorithms (such as APRIORI and FP-Growth). In this work, we define a vocabulary of propositional letters wrapping motifs, and show how to extract temporal association rules starting from such a vocabulary. We apply our methodology to time series datasets in the fields of hand signs execution and gait recognition, and we discuss how they capture curious insights within data, keeping a high level of interpretability.
Cite as

Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan. Temporal Association Rules from Motifs (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 19:1-19:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{milella_et_al:LIPIcs.TIME.2025.19,
  author =	{Milella, Mauro and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Temporal Association Rules from Motifs}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{19:1--19:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.19},
  URN =		{urn:nbn:de:0030-drops-244653},
  doi =		{10.4230/LIPIcs.TIME.2025.19},
  annote =	{Keywords: Motifs, Interval Temporal Logic, Association Rules}
}
Document
DOI: 10.4230/LIPIcs.TIME.2025.4
Assessing the (In)Ability of LLMs to Reason in Interval Temporal Logic

Authors: Pietro Bellodi, Pietro Casavecchia, Alberto Paparella, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
The logical reasoning skills of Large Language Models (LLMs) is poorly understood and often overstated. Current evaluation suites rely on algebraic or commonsense puzzles that mix reasoning with symbolic manipulation and/or provide static datasets that quickly saturate or leak into pretraining corpora. In purely logical terms, the most relevant reasoning skill is the meta-mathematical task of valid formula recognition, which is at the foundation of higher-level reasoning tasks (including deduction and minimization of assertions, to name just a few). In the current landscape of LLMs benchmarking, puzzles are most often stated in propositional or first-order logic, with a few exceptions for point-based temporal logic, such as LTL; yet, in the real world, event-based temporal statements are prevalent, and they are more naturally expressed in interval-based temporal logic. Interval temporal logic offers a much richer (w.r.t. point-based temporal logic, for example) variety of problems, and not only do different languages present different expressive powers, but also the computational complexity of the validity problem can vary widely. In this paper, we tackle the problem of assessing the ability of LLMs to reason about interval-based statements in the form of validity recognition. We explore whether their accuracy is sensible to the underlying language, the computational complexity of the associated validity problem, and the intrinsic hardness of the problem in terms of formula length and modal depth of the problem. We benchmark several frontier LLMs (Gemma 3 27b It, Llama 4 Maverick, DeepSeek Chat V3 release 0324, Qwen 3 32b, and Qwen 3 235b) and show that, despite apparently impressive performance on algebraic or commonsense benchmarks, they falter on logically rigorous tasks.
Cite as

Pietro Bellodi, Pietro Casavecchia, Alberto Paparella, Guido Sciavicco, and Ionel Eduard Stan. Assessing the (In)Ability of LLMs to Reason in Interval Temporal Logic. In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bellodi_et_al:LIPIcs.TIME.2025.4,
  author =	{Bellodi, Pietro and Casavecchia, Pietro and Paparella, Alberto and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Assessing the (In)Ability of LLMs to Reason in Interval Temporal Logic}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{4:1--4:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.4},
  URN =		{urn:nbn:de:0030-drops-244504},
  doi =		{10.4230/LIPIcs.TIME.2025.4},
  annote =	{Keywords: Large Language Models, Benchmarking, Interval Temporal Logic}
}
Artifact
Software
DOI: 10.4230/artifacts.24782
Sole.jl

Authors: Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Abstract
Cite as

Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, Ionel Eduard Stan. Sole.jl (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24782,
   title = {{Sole.jl}}, 
   author = {Milella, Mauro and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
   note = {Software, version 0.6.2., swhId: \href{https://archive.softwareheritage.org/swh:1:dir:dd723aee72578208606649ff12168e891cdae221;origin=https://github.com/aclai-lab/Sole.jl;visit=swh:1:snp:921c0e3817509d813e0ea03398f093fbd48ca539;anchor=swh:1:rev:8b79f0b7e41c91745a780262e11c7d07be660084}{\texttt{swh:1:dir:dd723aee72578208606649ff12168e891cdae221}} (visited on 2025-10-13)},
   url = {https://github.com/aclai-lab/Sole.jl},
   doi = {10.4230/artifacts.24782},
}
Artifact
Software
DOI: 10.4230/artifacts.24783
ModalAssociationRules.jl

Authors: Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Abstract
Cite as

Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, Ionel Eduard Stan. ModalAssociationRules.jl (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24783,
   title = {{ModalAssociationRules.jl}}, 
   author = {Milella, Mauro and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
   note = {Software, version 0.1.0., swhId: \href{https://archive.softwareheritage.org/swh:1:dir:697da0b30a22cd23450ab445a887ebf1a602db8f;origin=https://github.com/aclai-lab/ModalAssociationRules.jl;visit=swh:1:snp:72b4fb9d69583cb16dd357b5c9de2a2359b80727;anchor=swh:1:rev:4c69384c9ff2e0cf401e27ae9874b1c728962829}{\texttt{swh:1:dir:697da0b30a22cd23450ab445a887ebf1a602db8f}} (visited on 2025-10-13)},
   url = {https://github.com/aclai-lab/ModalAssociationRules.jl},
   doi = {10.4230/artifacts.24783},
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.TIME.2024.1
A General Logical Approach to Learning from Time Series (Invited Talk)

Authors: Guido Sciavicco

Published in: LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024)

Abstract
Machine learning from multivariate time series is a common task, and countless different approaches to typical learning problems have been proposed in recent years. In this talk, we review some basic ideas towards logic-based learning methods, and we sketch a general framework.
Cite as

Guido Sciavicco. A General Logical Approach to Learning from Time Series (Invited Talk). In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{sciavicco:LIPIcs.TIME.2024.1,
  author =	{Sciavicco, Guido},
  title =	{{A General Logical Approach to Learning from Time Series}},
  booktitle =	{31st International Symposium on Temporal Representation and Reasoning (TIME 2024)},
  pages =	{1:1--1:2},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-349-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{318},
  editor =	{Sala, Pietro and Sioutis, Michael and Wang, Fusheng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.1},
  URN =		{urn:nbn:de:0030-drops-212088},
  doi =		{10.4230/LIPIcs.TIME.2024.1},
  annote =	{Keywords: Machine learning, temporal logic, general approach}
}
Document
DOI: 10.4230/LIPIcs.TIME.2024.7
Fitting’s Style Many-Valued Interval Temporal Logic Tableau System: Theory and Implementation

Authors: Guillermo Badia, Carles Noguera, Alberto Paparella, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024)

Abstract
Many-valued logics, often referred to as fuzzy logics, are a fundamental tool for reasoning about uncertainty, and are based on truth value algebras that generalize the Boolean one; the same logic can be interpreted on algebras from different varieties, for different purposes and pose different challenges. Although temporal many-valued logics, that is, the many-valued counterpart of popular temporal logics, have received little attention in the literature, the many-valued generalization of Halpern and Shoham’s interval temporal logic has been recently introduced and studied, and a sound and complete tableau system for it has been presented for the case in which it is interpreted on some finite Heyting algebra. In this paper, we take a step further in this inquiry by exploring a tableau system for Halpern and Shoham’s interval temporal logic interpreted on some finite {FL_{ew}}-algebra, therefore generalizing the Heyting case, and by providing its open-source implementation.
Cite as

Guillermo Badia, Carles Noguera, Alberto Paparella, Guido Sciavicco, and Ionel Eduard Stan. Fitting’s Style Many-Valued Interval Temporal Logic Tableau System: Theory and Implementation. In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 7:1-7:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{badia_et_al:LIPIcs.TIME.2024.7,
  author =	{Badia, Guillermo and Noguera, Carles and Paparella, Alberto and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Fitting’s Style Many-Valued Interval Temporal Logic Tableau System: Theory and Implementation}},
  booktitle =	{31st International Symposium on Temporal Representation and Reasoning (TIME 2024)},
  pages =	{7:1--7:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-349-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{318},
  editor =	{Sala, Pietro and Sioutis, Michael and Wang, Fusheng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.7},
  URN =		{urn:nbn:de:0030-drops-212145},
  doi =		{10.4230/LIPIcs.TIME.2024.7},
  annote =	{Keywords: Interval temporal logic, many-valued logic, tableau system}
}
Document
DOI: 10.4230/LIPIcs.TIME.2023.9
A Sound and Complete Tableau System for Fuzzy Halpern and Shoham’s Interval Temporal Logic

Authors: Willem Conradie, Riccardo Monego, Emilio Muñoz-Velasco, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 278, 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)

Abstract
Interval temporal logic plays a critical role in various applications, including planning, scheduling, and formal verification; recently, interval temporal logic has also been successfully applied to learning from temporal data. Halpern and Shoham’s interval temporal logic, in particular, stands out as a very intuitive, yet expressive, interval-based formalism. To address real-world scenarios involving uncertainty and imprecision, Halpern and Shoham’s logic has been recently generalized to the fuzzy (many-valued) case. The resulting language capitalizes on many-valued modal logics, allowing for a range of truth values that reflect multiple expert perspectives, but inherits the bad computational behaviour of its crisp counterpart. In this work, we investigate a sound and complete tableau system for fuzzy Halpern and Shoham’s logic, which, although possibly non-terminating, offers a semi-decision procedure for the finite case.
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Willem Conradie, Riccardo Monego, Emilio Muñoz-Velasco, Guido Sciavicco, and Ionel Eduard Stan. A Sound and Complete Tableau System for Fuzzy Halpern and Shoham’s Interval Temporal Logic. In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{conradie_et_al:LIPIcs.TIME.2023.9,
  author =	{Conradie, Willem and Monego, Riccardo and Mu\~{n}oz-Velasco, Emilio and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{A Sound and Complete Tableau System for Fuzzy Halpern and Shoham’s Interval Temporal Logic}},
  booktitle =	{30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
  pages =	{9:1--9:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-298-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{278},
  editor =	{Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2023.9},
  URN =		{urn:nbn:de:0030-drops-190996},
  doi =		{10.4230/LIPIcs.TIME.2023.9},
  annote =	{Keywords: Interval temporal logic, many-valued logic, tableau system}
}
Document
DOI: 10.4230/LIPIcs.TIME.2022.13
Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification

Authors: Giovanni Pagliarini, Simone Scaboro, Giuseppe Serra, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 247, 29th International Symposium on Temporal Representation and Reasoning (TIME 2022)

Abstract
Multivariate time series classification is a widely known problem, and its applications are ubiquitous. Due to their strong generalization capability, neural networks have been proven to be very powerful for the task, but their applicability is often limited by their intrinsic black-box nature. Recently, temporal decision trees have been shown to be a serious alternative to neural networks for the same task in terms of classification performances, while attaining higher levels of transparency and interpretability. In this work, we propose an initial approach to neural-symbolic temporal decision trees, that is, an hybrid method that leverages on both the ability of neural networks of capturing temporal patterns and the flexibility of temporal decision trees of taking decisions on intervals based on (possibly, externally computed) temporal features. While based on a proof-of-concept implementation, in our experiments on public datasets, neural-symbolic temporal decision trees show promising results.
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Giovanni Pagliarini, Simone Scaboro, Giuseppe Serra, Guido Sciavicco, and Ionel Eduard Stan. Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification. In 29th International Symposium on Temporal Representation and Reasoning (TIME 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 247, pp. 13:1-13:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{pagliarini_et_al:LIPIcs.TIME.2022.13,
  author =	{Pagliarini, Giovanni and Scaboro, Simone and Serra, Giuseppe and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification}},
  booktitle =	{29th International Symposium on Temporal Representation and Reasoning (TIME 2022)},
  pages =	{13:1--13:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-262-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{247},
  editor =	{Artikis, Alexander and Posenato, Roberto and Tonetta, Stefano},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2022.13},
  URN =		{urn:nbn:de:0030-drops-172607},
  doi =		{10.4230/LIPIcs.TIME.2022.13},
  annote =	{Keywords: Machine learning, neural-symbolic, temporal logic, hybrid temporal decision trees}
}
Document
DOI: 10.4230/LIPIcs.TIME.2021.7
Interval Temporal Random Forests with an Application to COVID-19 Diagnosis

Authors: Federico Manzella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
Symbolic learning is the logic-based approach to machine learning. The mission of symbolic learning is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. In the context of temporal data, interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. Building on it, we study here its natural generalization to interval temporal random forests, mimicking the corresponding schema at the propositional level. Interval temporal random forests turn out to be a very performing multivariate time series classification method, which, despite the introduction of a functional component, are still logically interpretable to some extent. We apply this method to the problem of diagnosing COVID-19 based on the time series that emerge from cough and breath recording of positive versus negative subjects. Our experiment show that our models achieve very high accuracies and sensitivities, often superior to those achieved by classical methods on the same data. Although other recent approaches to the same problem (based on different and more numerous data) show even better statistical results, our solution is the first logic-based, interpretable, and explainable one.
Cite as

Federico Manzella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan. Interval Temporal Random Forests with an Application to COVID-19 Diagnosis. In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{manzella_et_al:LIPIcs.TIME.2021.7,
  author =	{Manzella, Federico and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Interval Temporal Random Forests with an Application to COVID-19 Diagnosis}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{7:1--7:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.7},
  URN =		{urn:nbn:de:0030-drops-147837},
  doi =		{10.4230/LIPIcs.TIME.2021.7},
  annote =	{Keywords: Interval temporal logic, decision trees, random forests, sound-based diagnosis}
}
Document
DOI: 10.4230/LIPIcs.TIME.2020.5
The Horn Fragment of Branching Algebra

Authors: Alessandro Bertagnon, Marco Gavanelli, Alessandro Passantino, Guido Sciavicco, and Stefano Trevisani

Published in: LIPIcs, Volume 178, 27th International Symposium on Temporal Representation and Reasoning (TIME 2020)

Abstract
Branching Algebra is the natural branching-time generalization of Allen’s Interval Algebra. As in the linear case, the consistency problem for Branching Algebra is NP-hard. Being relatively new, however, not much is known about the computational behaviour of the consistency problem of its sub-algebras, except in the case of the recently found subset of convex branching relations, for which the consistency of a network can be tested via path consistency and it is therefore deterministic polynomial. In this paper, following Nebel and Bürckert, we define the Horn fragment of Branching Algebra, and prove that it is a sub-algebra of the latter, being closed under inverse, intersection, and composition, that it strictly contains both the convex fragment of Branching Algebra and the Horn fragment of Interval Algebra, and that its consistency problem can be decided via path consistency. Finally, we experimentally prove that the Horn fragment of Branching Algebra can be used as an heuristic for checking the consistency of a generic network with a considerable improvement over the convex subset.
Cite as

Alessandro Bertagnon, Marco Gavanelli, Alessandro Passantino, Guido Sciavicco, and Stefano Trevisani. The Horn Fragment of Branching Algebra. In 27th International Symposium on Temporal Representation and Reasoning (TIME 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 178, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{bertagnon_et_al:LIPIcs.TIME.2020.5,
  author =	{Bertagnon, Alessandro and Gavanelli, Marco and Passantino, Alessandro and Sciavicco, Guido and Trevisani, Stefano},
  title =	{{The Horn Fragment of Branching Algebra}},
  booktitle =	{27th International Symposium on Temporal Representation and Reasoning (TIME 2020)},
  pages =	{5:1--5:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-167-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{178},
  editor =	{Mu\~{n}oz-Velasco, Emilio and Ozaki, Ana and Theobald, Martin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2020.5},
  URN =		{urn:nbn:de:0030-drops-129736},
  doi =		{10.4230/LIPIcs.TIME.2020.5},
  annote =	{Keywords: Constraint programming, Consistency, Branching time, Horn Fragment}
}
Document
DOI: 10.4230/LIPIcs.TIME.2020.9
Knowledge Extraction with Interval Temporal Logic Decision Trees

Authors: Guido Sciavicco and Ionel Eduard Stan

Published in: LIPIcs, Volume 178, 27th International Symposium on Temporal Representation and Reasoning (TIME 2020)

Abstract
Multivariate temporal, or time, series classification is, in a way, the temporal generalization of (numeric) classification, as every instance is described by multiple time series instead of multiple values. Symbolic classification is the machine learning strategy to extract explicit knowledge from a data set, and the problem of symbolic classification of multivariate temporal series requires the design, implementation, and test of ad-hoc machine learning algorithms, such as, for example, algorithms for the extraction of temporal versions of decision trees. One of the most well-known algorithms for decision tree extraction from categorical data is Quinlan’s ID3, which was later extended to deal with numerical attributes, resulting in an algorithm known as C4.5, and implemented in many open-sources data mining libraries, including the so-called Weka, which features an implementation of C4.5 called J48. ID3 was recently generalized to deal with temporal data in form of timelines, which can be seen as discrete (categorical) versions of multivariate time series, and such a generalization, based on the interval temporal logic HS, is known as Temporal ID3. In this paper we introduce Temporal C4.5, that allows the extraction of temporal decision trees from undiscretized multivariate time series, describe its implementation, called Temporal J48, and discuss the outcome of a set of experiments with the latter on a collection of public data sets, comparing the results with those obtained by other, classical, multivariate time series classification methods.
Cite as

Guido Sciavicco and Ionel Eduard Stan. Knowledge Extraction with Interval Temporal Logic Decision Trees. In 27th International Symposium on Temporal Representation and Reasoning (TIME 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 178, pp. 9:1-9:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{sciavicco_et_al:LIPIcs.TIME.2020.9,
  author =	{Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Knowledge Extraction with Interval Temporal Logic Decision Trees}},
  booktitle =	{27th International Symposium on Temporal Representation and Reasoning (TIME 2020)},
  pages =	{9:1--9:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-167-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{178},
  editor =	{Mu\~{n}oz-Velasco, Emilio and Ozaki, Ana and Theobald, Martin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2020.9},
  URN =		{urn:nbn:de:0030-drops-129776},
  doi =		{10.4230/LIPIcs.TIME.2020.9},
  annote =	{Keywords: Interval Temporal Logic, Decision Trees, Explainable AI, Time series}
}
Document
DOI: 10.4230/LIPIcs.TIME.2020.11
Mining Significant Temporal Networks Is Polynomial

Authors: Guido Sciavicco, Matteo Zavatteri, and Tiziano Villa

Published in: LIPIcs, Volume 178, 27th International Symposium on Temporal Representation and Reasoning (TIME 2020)

Abstract
A Conditional Simple Temporal Network with Uncertainty and Decisions (CSTNUD) is a formalism that tackles controllable and uncontrollable durations as well as controllable and uncontrollable choices simultaneously. In the classic top-down model-based engineering approach, a designer builds a CSTNUD to model, validate and execute some temporal plan of interest. Instead, in this paper, we investigate the bottom-up approach by providing a deterministic polynomial time algorithm to mine a CSTNUD from a set of execution traces (i.e., a log). This paper paves the way for the design of controllable temporal networks mined from traces that also contain information on uncontrollable events.
Cite as

Guido Sciavicco, Matteo Zavatteri, and Tiziano Villa. Mining Significant Temporal Networks Is Polynomial. In 27th International Symposium on Temporal Representation and Reasoning (TIME 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 178, pp. 11:1-11:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{sciavicco_et_al:LIPIcs.TIME.2020.11,
  author =	{Sciavicco, Guido and Zavatteri, Matteo and Villa, Tiziano},
  title =	{{Mining Significant Temporal Networks Is Polynomial}},
  booktitle =	{27th International Symposium on Temporal Representation and Reasoning (TIME 2020)},
  pages =	{11:1--11:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-167-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{178},
  editor =	{Mu\~{n}oz-Velasco, Emilio and Ozaki, Ana and Theobald, Martin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2020.11},
  URN =		{urn:nbn:de:0030-drops-129792},
  doi =		{10.4230/LIPIcs.TIME.2020.11},
  annote =	{Keywords: Mining temporal constraints, cstnud, uncertainty, significant temporal network}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.TIME.2019
LIPIcs, Volume 147, TIME'19, Complete Volume

Authors: Johann Gamper, Sophie Pinchinat, and Guido Sciavicco

Published in: LIPIcs, Volume 147, 26th International Symposium on Temporal Representation and Reasoning (TIME 2019)

Abstract
LIPIcs, Volume 147, TIME'19, Complete Volume
Cite as

26th International Symposium on Temporal Representation and Reasoning (TIME 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 147, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Proceedings{gamper_et_al:LIPIcs.TIME.2019,
  title =	{{LIPIcs, Volume 147, TIME'19, Complete Volume}},
  booktitle =	{26th International Symposium on Temporal Representation and Reasoning (TIME 2019)},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-127-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{147},
  editor =	{Gamper, Johann and Pinchinat, Sophie and Sciavicco, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2019},
  URN =		{urn:nbn:de:0030-drops-113887},
  doi =		{10.4230/LIPIcs.TIME.2019},
  annote =	{Keywords: Theory of computation, Logic; Information systems, Temporal data; Computing methodologies, Knowledge representation and reasoning}
}
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