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24th International Conference on Business Information Systems (BIS)

作者： Azzolini, Damiano Riguzzi, Fabrizio Bellodi, Elena Lamma, Evelina Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat Via Saragat 1 I-44122 Ferrara Italy

ISBN: (纸本)9783031042164;9783031042157

One of the main limitations of blockchain systems based on Proof of Work is scalability, making them unsuitable for e-commerce and small payments. Currently, one of the principal directions to overcome the scalability issue is to use the so-called "layer two" solutions, like Lightning Network, where users can open channels and send payments through them. In this paper, we propose a probabilistic logic model of Lightning Network, and we show how it can be adopted to compute several properties of it. We conduct some experiments to prove the applicability of the model, rather than providing a comprehensive analysis of the network.

关键词： probabilistic logic programming Blockchain Lightning Network

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Consistent Scene Graph Generation by Constraint Optimization 7

Consistent Scene Graph Generation by Constraint Optimization

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37th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Chen, Boqi Marussy, Kristof Pilarski, Sebastian Semerath, Oszkar Varro, Daniel McGill Univ Elect & Comp Engn Montreal PQ Canada Budapest Univ Technol & Econ Dept Measurement & Informat Syst Budapest Hungary

ISBN: (纸本)9781450394758

Scene graph generation takes an image and derives a graph representation of key objects in the image and their relations. This core computer vision task is often used in autonomous driving, where traditional software and machine learning (ML) components are used in tandem. However, in such a safety-critical context, valid scene graphs can be further restricted by consistency constraints captured by domain or safety experts. Existing ML approaches for scene graph generation focus exclusively on relation-level accuracy but provide little to no guarantee that consistency constraints are satisfied in the generated scene graphs. In this paper, we aim to complement existing ML-based approaches by a post-processing step using constraint optimization over probabilistic scene graphs that can (1) guarantee that no consistency constraints are violated and (2) improve the overall accuracy of scene graph generation by fixing constraint violations. We evaluate the effectiveness of our approach using well-known, and novel metrics in the context of two popular ML datasets augmented with consistency constraints and two ML-based scene graph generation approaches as baselines.

关键词： scene graph generation constraint optimization consistency constraints machine learning probabilistic logic programming

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Learning hierarchical probabilistic logic programs

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MACHINE LEARNING 2021年第7期110卷 1637-1693页

作者： Fadja, Arnaud Nguembang Riguzzi, Fabrizio Lamma, Evelina Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat Via Machiavelli 30 I-44121 Ferrara Italy

probabilistic logic programming (PLP) combines logic programs and probabilities. Due to its expressiveness and simplicity, it has been considered as a powerful tool for learning and reasoning in relational domains characterized by uncertainty. Still, learning the parameter and the structure of general PLP is computationally expensive due to the inference cost. We have recently proposed a restriction of the general PLP language called hierarchical PLP (HPLP) in which clauses and predicates are hierarchically organized. HPLPs can be converted into arithmetic circuits or deep neural networks and inference is much cheaper than for general PLP. In this paper we present algorithms for learning both the parameters and the structure of HPLPs from data. We first present an algorithm, called parameter learning for hierarchical probabilistic logic programs (PHIL) which performs parameter estimation of HPLPs using gradient descent and expectation maximization. We also propose structure learning of hierarchical probabilistic logic programming (SLEAHP), that learns both the structure and the parameters of HPLPs from data. Experiments were performed comparing PHIL and SLEAHP with PLP and Markov logic Networks state-of-the art systems for parameter and structure learning respectively. PHIL was compared with EMBLEM, ProbLog2 and Tuffy and SLEAHP with SLIPCOVER, PROBFOIL+, MLB-BC, MLN-BT and RDN-B. The experiments on five well known datasets show that our algorithms achieve similar and often better accuracies but in a shorter time.

关键词： probabilistic logic programming Distribution semantics Arithmetic circuits Gradient descent Back-propagation

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Optimizing Probabilities in probabilistic logic Programs

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THEORY AND PRACTICE OF logic programming 2021年第5期21卷 543-556页

作者： Azzolini, Damiano Riguzzi, Fabrizio Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat Via Saragat 1 I-44122 Ferrara Italy

probabilistic logic programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probabilistic logic programs, namely probabilistic optimizable logic programs, and we provide an effective algorithm to find the best assignment to probabilities of random variables, such that a set of constraints is satisfied and an objective function is optimized.

关键词： probabilistic logic programming optimization constraints

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Parameter Learning in ProbLog with Annotated Disjunctions 20th

Parameter Learning in ProbLog with Annotated Disjunctions

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20th International Symposium on Intelligent Data Analysis (IDA)

作者： Yang, Wen-Chi Jain, Arcchit De Raedt, Luc Meert, Wannes Katholieke Univ Leuven Dept Comp Sci Leuven AI Celestijnenlaan 200a Box 2402 B-3001 Leuven Belgium Orebro Univ Ctr Appl Autonomous Sensor Syst Orebro Sweden

ISBN: (纸本)9783031013331;9783031013324

In parameter learning, a partial interpretation most often contains information about only a subset of the parameters in the program. However, standard EM-based algorithms use all interpretations to learn all parameters, which significantly slows down learning. To tackle this issue, we introduce EMPLiFI, an EM-based parameter learning technique for probabilistic logic programs, that improves the efficiency of EM by exploiting the rule-based structure of logic programs. In addition, EMPLiFI enables parameter learning of multi-head annotated dis-junctions in ProbLog programs, which was not yet possible in previous methods. Theoretically, we show that EMPLiFI is correct. Empirically, we compare EMPLiFI to LFI-ProbLog and EMBLEM. The results show that EMPLiFI is the most efficient in learning single-head annotated disjunctions. In learning multi-head annotated disjunctions, EMPLiFI is more accurate than EMBLEM, while LFI-ProbLog cannot handle this task.

关键词： Learning from interpretations probabilistic logic programming Expectation maximization

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Online event recognition over noisy data streams

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2023年第1期161卷

作者： Mantenoglou, Periklis Artikis, Alexander Paliouras, Georgios NCSR Demokritos Inst Informat & Telecommun Athens Greece Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens Greece Univ Piraeus Dept Maritime Studies Piraeus Greece Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens Greece

Composite event recognition (CER) systems process streams of sensor data and infer composite events of interest by means of pattern matching. Data uncertainty is frequent in CER applications and results in erroneous detection. To support streaming applications, we present oPIECbd, an extension of oPIEC with a bounded memory, leveraging interval duration statistics to resolve memory conflicts. oPIECbd may achieve comparable predictive accuracy to batch reasoning, avoiding the prohibitive cost of such reasoning. Furthermore, the use of interval duration statistics allows oPIECbd to outperform significantly earlier versions of bounded oPIEC. The empirical evaluation demonstrates the efficacy of oPIECbd on a benchmark activity recognition dataset, as well as real data streams from the field of maritime situational awareness. & COPY;2023 Elsevier Inc. All rights reserved.

关键词： Event calculus Temporal pattern matching probabilistic logic programming Uncertainty Human activity recognition Maritime situational awareness

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A probabilistic interval-based event calculus for activity recognition

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2021年第1-2期89卷 29-52页

作者： Artikis, Alexander Makris, Evangelos Paliouras, Georgios Univ Piraeus Dept Maritime Studies Piraeus Greece NCSR Demokritos Inst Informat & Telecommun Athens Greece

Activity recognition refers to the detection of temporal combinations of 'low-level' or 'short-term' activities on sensor data. Various types of uncertainty exist in activity recognition systems and this often leads to erroneous detection. Typically, the frameworks aiming to handle uncertainty compute the probability of the occurrence of activities at each time-point. We extend this approach by defining the probability of a maximal interval and the credibility rate for such intervals. We then propose a linear-time algorithm for computing all probabilistic temporal intervals of a given dataset. We evaluate the proposed approach using a benchmark activity recognition dataset, and outline the conditions in which our approach outperforms time-point-based recognition.

关键词： Action languages Complex event recognition probabilistic logic programming

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probabilistic Rule Induction for Transparent CBR Under Uncertainty 41st

Probabilistic Rule Induction for Transparent CBR Under Uncer...

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41st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI)

作者： Jedwabny, Martin Bisquert, Pierre Croitoru, Madalina Univ Montpellier CNRS INRIA LIRMM Montpellier France Univ Montpellier Inst Agro INRAE IATE Montpellier France

ISBN: (纸本)9783030911003;9783030910990

CBR systems leverage past experiences to make decisions. Recently, the AI community has taken an interest in making CBR systems explainable. logic-based frameworks make answers straightforward to explain. However, they struggle in the face of conflicting information, unlike probabilistic techniques. We show how probabilistic inductive logic programming (PILP) can be applied in CBR systems to make transparent decisions combining logic and probabilities. Then, we demonstrate how our approach can be applied in scenarios presenting uncertainty.

关键词： Case-based reasoning probabilistic logic programming Inductive logic programming Explanability

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Consistent Scene Graph Generation by Constraint Optimization 22

Consistent Scene Graph Generation by Constraint Optimization

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： Boqi Chen Kristóf Marussy Sebastian Pilarski Oszkár Semeráth Daniel Varro Electrical and Computer Engineering McGill University Canada Deptartment of Measurement and Information Systems Budapest University of Technology and Economics Hungary Electrical and Computer Engineering McGill University Hungary

ISBN: (纸本)9781450394758

关键词： scene graph generation probabilistic logic programming machine learning constraint optimization consistency constraints

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Symbolic DNN-Tuner: A Python and ProbLog-based system for optimizing Deep Neural Networks hyperparameters

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SOFTWAREX 2022年 17卷

作者： Fraccaroli, Michele Lamma, Evelina Riguzzi, Fabrizio Univ Ferrara Dept Engn Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dept Math & Comp Sci Via Saragat 1 I-44122 Ferrara Italy

The application of deep learning models to increasingly complex contexts has led to a rise in the complexity of the models themselves. Due to this, there is an increase in the number of hyper-parameters (HPs) to be set and Hyper-Parameter Optimization (HPO) algorithms occupy a fundamental role in deep learning. Bayesian Optimization (BO) is the state-of-the-art of HPO for deep learning models. BO keeps track of past results and uses them to build a probabilistic model, building a probability density of HPs. This work aims to improve BO applied to Deep Neural Networks (DNNs) by an analysis of the results of the network on training and validation sets. This analysis is obtained by applying symbolic tuning rules, implemented in probabilistic logic programming (PLP). The resulting system, called Symbolic DNN-Tuner, logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, leading to improved performance. In this paper, we present the general system and its implementation. We also show its graphical interface and a simple example of execution. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Deep learning probabilistic logic programming Hyper-parameters tuning Neural-symbolic integration

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