Autonomous behaviors may raise ethical issues that agents must consider in their reasoning. Some approaches use deontic logics, while others consider a value-based argumentation framework. However, no work combines both modal logic and argumentation to reason about ethics. Hence, we propose a new argumentation framework where arguments are built from a n-ary multi-modal logic. It allows to express different kinds of operators, e.g. nullary choice or moral worth operators, dyadic deontic operators or mental states. However the standard attacks based on logical contradictions are no longer sufficient to catch an intuitive meaning for attacks. Hence, we enrich standard attacks by characterizing how oppositions between modal operators arise. Furthermore we show the standard logic-based attacks have a quasi-symmetry property, i.e. when an argument attacks another, this argument is necessarily attacked by another one. Our modal attacks do not have this property, which is highly relevant to decide a dilemma.
{"title":"Using n-ary multi-modal logics in argumentation frameworks to reason about ethics","authors":"Christopher Leturc, Grégory Bonnet","doi":"10.3233/aic-220301","DOIUrl":"https://doi.org/10.3233/aic-220301","url":null,"abstract":"Autonomous behaviors may raise ethical issues that agents must consider in their reasoning. Some approaches use deontic logics, while others consider a value-based argumentation framework. However, no work combines both modal logic and argumentation to reason about ethics. Hence, we propose a new argumentation framework where arguments are built from a n-ary multi-modal logic. It allows to express different kinds of operators, e.g. nullary choice or moral worth operators, dyadic deontic operators or mental states. However the standard attacks based on logical contradictions are no longer sufficient to catch an intuitive meaning for attacks. Hence, we enrich standard attacks by characterizing how oppositions between modal operators arise. Furthermore we show the standard logic-based attacks have a quasi-symmetry property, i.e. when an argument attacks another, this argument is necessarily attacked by another one. Our modal attacks do not have this property, which is highly relevant to decide a dilemma.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"136 22","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139003996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Entity alignment is the task of identifying entities from different knowledge graphs (KGs) that point to the same item and is important for KG fusion. In the real world, due to the heterogeneity between different KGs, equivalent entities often have different relations around them, so it is difficult for Graph Convolutional Network (GCN) to accurately learn the relation information in the KGs. Moreover, to solve the problem regarding inadequate utilisation of relation information in entity alignment, a novel GCN-based model, joint Unsupervised Relation Alignment for Entity Alignment (URAEA), is proposed. The model first employs a novel method for calculating relation embeddings by using entity embeddings, then constructs unsupervised seed relation alignments through these relation embeddings, and finally performs entity alignment together with relation alignment. In addition, the seed entity alignments are expanded based on the generated seed relation alignments. Experiments conducted on three real-world datasets show that this approach outperforms state-of-the-art methods.
{"title":"Entity alignment with fusing relation representation","authors":"Li Feng Ying, Li Jia Peng, Dong Rong Sheng","doi":"10.3233/aic-220214","DOIUrl":"https://doi.org/10.3233/aic-220214","url":null,"abstract":"Entity alignment is the task of identifying entities from different knowledge graphs (KGs) that point to the same item and is important for KG fusion. In the real world, due to the heterogeneity between different KGs, equivalent entities often have different relations around them, so it is difficult for Graph Convolutional Network (GCN) to accurately learn the relation information in the KGs. Moreover, to solve the problem regarding inadequate utilisation of relation information in entity alignment, a novel GCN-based model, joint Unsupervised Relation Alignment for Entity Alignment (URAEA), is proposed. The model first employs a novel method for calculating relation embeddings by using entity embeddings, then constructs unsupervised seed relation alignments through these relation embeddings, and finally performs entity alignment together with relation alignment. In addition, the seed entity alignments are expanded based on the generated seed relation alignments. Experiments conducted on three real-world datasets show that this approach outperforms state-of-the-art methods.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"50 5","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The exponential growth of technology in recent decades has led to the emergence of some challenges inherent to this growth. One of these challenges is the enormous amount of data collected by the different sensors in our society, namely in management processes such as Wastewater Treatment Plants (WWTPs). These infrastructures comprise several processes to treat wastewater and discharge clean water in water courses. Therefore, the concentration of pollutants must be below the allowable emissions limits. In this work, anomaly detection models were conceived, tuned and evaluated to monitor essential parameters such as nitrate and ammonia concentrations and pH to improve WWTP management. Four Machine Learning models were considered, particularly Local Outlier Fraction, Isolation Forest, One-Class Support Vector Machines and Long Short-Term Memory-Autoencoders (LSTM-AE), to detect anomalies in the three parameters mentioned. Through the different experiments, it was possible to verify that, in terms of F1-Score, the best candidate model for the three analyzed parameters was LSTM-AE-based, with a value consistently higher than 97%.
近几十年来，技术呈指数级增长，由此带来了一些固有的挑战。其中一个挑战是社会中不同传感器收集的大量数据，即污水处理厂（WWTP）等管理过程中的数据。这些基础设施包括处理废水和向水道排放清洁水的多个过程。因此，污染物的浓度必须低于允许的排放限值。在这项工作中，对异常检测模型进行了构思、调整和评估，以监测硝酸盐、氨浓度和 pH 值等重要参数，从而改善污水处理厂的管理。考虑了四种机器学习模型，特别是局部离群分数、隔离森林、单类支持向量机和长短期记忆自动编码器（LSTM-AE），以检测上述三个参数中的异常情况。通过不同的实验，可以验证就 F1 分数而言，三个分析参数的最佳候选模型是基于 LSTM-AE 的，其值始终高于 97%。
{"title":"Conception and evaluation of anomaly detection models for monitoring analytical parameters in wastewater treatment plants","authors":"P. Oliveira, M. Salomé Duarte, Paulo Novais","doi":"10.3233/aic-230064","DOIUrl":"https://doi.org/10.3233/aic-230064","url":null,"abstract":"The exponential growth of technology in recent decades has led to the emergence of some challenges inherent to this growth. One of these challenges is the enormous amount of data collected by the different sensors in our society, namely in management processes such as Wastewater Treatment Plants (WWTPs). These infrastructures comprise several processes to treat wastewater and discharge clean water in water courses. Therefore, the concentration of pollutants must be below the allowable emissions limits. In this work, anomaly detection models were conceived, tuned and evaluated to monitor essential parameters such as nitrate and ammonia concentrations and pH to improve WWTP management. Four Machine Learning models were considered, particularly Local Outlier Fraction, Isolation Forest, One-Class Support Vector Machines and Long Short-Term Memory-Autoencoders (LSTM-AE), to detect anomalies in the three parameters mentioned. Through the different experiments, it was possible to verify that, in terms of F1-Score, the best candidate model for the three analyzed parameters was LSTM-AE-based, with a value consistently higher than 97%.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"53 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The majority of existing rotating target detectors inherit the horizontal detection paradigm and design the rotational regression loss based on the inductive paradigm. But the loss design limitation of the inductive paradigm makes these detectors hardly detect effectively tiny targets with high accuracy, particularly for large-aspect-ratio objects. Therefore, in view of the fact that horizontal detection is a special scenario of rotating target detection and based on the relationship between rotational and horizontal detection, we shift from an inductive to a deductive paradigm of design to develop a new regression loss function named Gauss–Wasserstein scattering (GWS). First, the rotating bounding box is transformed into a two-dimensional Gaussian distribution, and then the regression losses between Gaussian distributions are calculated as the Wasserstein scatter; By analyzing the gradient of centroid regression, centroid regression is shown to be able to adjust gradients dynamically based on object characteristics, and small targets requiring high accuracy detection rely on this mechanism, and more importantly, it is further demonstrated that GWS is scale-invariant while possessing an explicit regression logic. The method is performed on a large public remote sensing dataset DOTA and two popular detectors and achieves a large accuracy improvement in both large aspect ratio targets and small targets detection compared to similar methods.
{"title":"GWS: Rotation object detection in aerial remote sensing images based on Gauss–Wasserstein scattering","authors":"Ling Gan, Xiaodong Tan, Liuhui Hu","doi":"10.3233/aic-230135","DOIUrl":"https://doi.org/10.3233/aic-230135","url":null,"abstract":"The majority of existing rotating target detectors inherit the horizontal detection paradigm and design the rotational regression loss based on the inductive paradigm. But the loss design limitation of the inductive paradigm makes these detectors hardly detect effectively tiny targets with high accuracy, particularly for large-aspect-ratio objects. Therefore, in view of the fact that horizontal detection is a special scenario of rotating target detection and based on the relationship between rotational and horizontal detection, we shift from an inductive to a deductive paradigm of design to develop a new regression loss function named Gauss–Wasserstein scattering (GWS). First, the rotating bounding box is transformed into a two-dimensional Gaussian distribution, and then the regression losses between Gaussian distributions are calculated as the Wasserstein scatter; By analyzing the gradient of centroid regression, centroid regression is shown to be able to adjust gradients dynamically based on object characteristics, and small targets requiring high accuracy detection rely on this mechanism, and more importantly, it is further demonstrated that GWS is scale-invariant while possessing an explicit regression logic. The method is performed on a large public remote sensing dataset DOTA and two popular detectors and achieves a large accuracy improvement in both large aspect ratio targets and small targets detection compared to similar methods.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"14 2","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Viswanathan, V. S. kumar, M. Umamaheswari, V. Janarthanan, M. Jaganathan
Semantic segmentation has made tremendous progress in recent years. The development of large datasets and the regression of convolutional models have enabled effective training of very large semantic model. Nevertheless, higher capacity indicates a higher computational problem, thus preventing real-time operation. Yet, due to the limited annotations, the models may have relied heavily on the available contexts in the training data, resulting in poor generalization to previously unseen scenes. Therefore, to resolve these issues, Enhanced Gated Pyramid network (GPNet) with Lightweight Attention Module (LAM) is proposed in this paper. GPNet is used for semantic feature extraction and GPNet is enhanced by the pre-trained dilated DetNet and Dense Connection Block (DCB). LAM approach is applied to habitually rescale the different feature channels weights. LAM module can increase the accuracy and effectiveness of the proposed methodology. The performance of proposed method is validated using Google Colab environment with different datasets such as Cityscapes, CamVid and ADE20K. The experimental results are compared with various methods like GPNet-ResNet-101 and GPNet-ResNet-50 in terms of IoU, precision, accuracy, F1 score and recall. From the overall analysis cityscapes dataset achieves 94.82% pixel accuracy.
{"title":"Semantic segmentation based on enhanced gated pyramid network with lightweight attention module","authors":"A. Viswanathan, V. S. kumar, M. Umamaheswari, V. Janarthanan, M. Jaganathan","doi":"10.3233/aic-220254","DOIUrl":"https://doi.org/10.3233/aic-220254","url":null,"abstract":"Semantic segmentation has made tremendous progress in recent years. The development of large datasets and the regression of convolutional models have enabled effective training of very large semantic model. Nevertheless, higher capacity indicates a higher computational problem, thus preventing real-time operation. Yet, due to the limited annotations, the models may have relied heavily on the available contexts in the training data, resulting in poor generalization to previously unseen scenes. Therefore, to resolve these issues, Enhanced Gated Pyramid network (GPNet) with Lightweight Attention Module (LAM) is proposed in this paper. GPNet is used for semantic feature extraction and GPNet is enhanced by the pre-trained dilated DetNet and Dense Connection Block (DCB). LAM approach is applied to habitually rescale the different feature channels weights. LAM module can increase the accuracy and effectiveness of the proposed methodology. The performance of proposed method is validated using Google Colab environment with different datasets such as Cityscapes, CamVid and ADE20K. The experimental results are compared with various methods like GPNet-ResNet-101 and GPNet-ResNet-50 in terms of IoU, precision, accuracy, F1 score and recall. From the overall analysis cityscapes dataset achieves 94.82% pixel accuracy.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"42 21","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138597815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jorge Fernandez, Dominique Longin, Emiliano Lorini, Frédéric Maris
We present an epistemic language for representing an artificial player’s beliefs and actions in the context of the Yōkai board game. Yōkai is a cooperative game which requires a combination of Theory of Mind (ToM), temporal and spatial reasoning to be played effectively by an artificial agent. We show that the language properly accounts for these three dimensions and that its satisfiability problem is NP-complete. This opens up the possibility of exploiting SAT techniques for automating reasoning of an artificial player in the context of the Yōkai board-game.
我们以 "阳海 "棋盘游戏为背景，介绍了一种表示人工玩家信念和行动的认识论语言。Yōkai是一种合作游戏，需要结合心智理论（ToM）、时间推理和空间推理，人工代理才能有效地进行游戏。我们的研究表明，该语言恰当地考虑了这三个维度，而且其可满足性问题是NP完备的。这就为利用 SAT 技术在阳海棋盘游戏中实现人工棋手的自动推理提供了可能性。
{"title":"A logical modeling of the Yōkai board game","authors":"Jorge Fernandez, Dominique Longin, Emiliano Lorini, Frédéric Maris","doi":"10.3233/aic-230050","DOIUrl":"https://doi.org/10.3233/aic-230050","url":null,"abstract":"We present an epistemic language for representing an artificial player’s beliefs and actions in the context of the Yōkai board game. Yōkai is a cooperative game which requires a combination of Theory of Mind (ToM), temporal and spatial reasoning to be played effectively by an artificial agent. We show that the language properly accounts for these three dimensions and that its satisfiability problem is NP-complete. This opens up the possibility of exploiting SAT techniques for automating reasoning of an artificial player in the context of the Yōkai board-game.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"14 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a multi-objective optimization approach for developing efficient and environmentally friendly Machine Learning models. The proposed approach uses Genetic Algorithms to simultaneously optimize the accuracy, time-to-solution, and energy consumption simultaneously. This solution proposed to be part of an Automated Machine Learning pipeline and focuses on architecture and hyperparameter search. A customized Genetic Algorithm scheme and operators were developed, and its feasibility was evaluated using the XGBoost ML algorithm for classification and regression tasks. The results demonstrate the effectiveness of the Genetic Algorithm for multi-objective optimization, indicating that it is possible to reduce energy consumption while minimizing predictive performance losses.
本文提出了一种多目标优化方法，用于开发高效、环保的机器学习模型。建议的方法使用遗传算法同时优化准确性、解决问题的时间和能耗。该解决方案拟作为自动机器学习管道的一部分，重点关注架构和超参数搜索。我们开发了一种定制的遗传算法方案和算子，并使用 XGBoost ML 算法对其可行性进行了评估，以完成分类和回归任务。结果证明了遗传算法在多目标优化方面的有效性，表明它有可能在减少预测性能损失的同时降低能耗。
{"title":"Multi-objective hyperparameter optimization approach with genetic algorithms towards efficient and environmentally friendly machine learning","authors":"André M. Yokoyama, Mariza Ferro, Bruno Schulze","doi":"10.3233/aic-230063","DOIUrl":"https://doi.org/10.3233/aic-230063","url":null,"abstract":"This paper presents a multi-objective optimization approach for developing efficient and environmentally friendly Machine Learning models. The proposed approach uses Genetic Algorithms to simultaneously optimize the accuracy, time-to-solution, and energy consumption simultaneously. This solution proposed to be part of an Automated Machine Learning pipeline and focuses on architecture and hyperparameter search. A customized Genetic Algorithm scheme and operators were developed, and its feasibility was evaluated using the XGBoost ML algorithm for classification and regression tasks. The results demonstrate the effectiveness of the Genetic Algorithm for multi-objective optimization, indicating that it is possible to reduce energy consumption while minimizing predictive performance losses.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"15 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139103286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we explore the links between the Borda voting rule and belief merging operators. More precisely, we define two families of merging operators inspired by the definition of the Borda voting rule. We also introduce a notion of cancellation in belief merging, inspired by the axiomatization of the Borda voting rule proposed by Young. This allows us to provide a characterization of the drastic merging operator and of a family of merging operators defined in a way which is similar to the Borda rule.
{"title":"On the links between belief merging, the Borda voting method, and the cancellation property 1","authors":"Patricia Everaere, Chouaib Fellah, Sébastien Konieczny, Ramón Pino Pérez","doi":"10.3233/aic-220306","DOIUrl":"https://doi.org/10.3233/aic-220306","url":null,"abstract":"In this work, we explore the links between the Borda voting rule and belief merging operators. More precisely, we define two families of merging operators inspired by the definition of the Borda voting rule. We also introduce a notion of cancellation in belief merging, inspired by the axiomatization of the Borda voting rule proposed by Young. This allows us to provide a characterization of the drastic merging operator and of a family of merging operators defined in a way which is similar to the Borda rule.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"22 S1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Operations like belief change or merging have been adapted to the context of abstract argumentation. However, these operations may require to express some uncertainty or some disjunction in the result, which is not representable in classical AFs. For this reason, some of these earlier works requirea set of AFs or a set of extensions as the outcome of the operation, somehow to represent a “disjunction” of AFs or extensions. In parallel, the notion of Incomplete AFs (IAFs) has been developed recently. It corresponds to AFs where the existence of some arguments or attacks may be uncertain. Each IAF can be associated with a set of classical AFs called completions, that correspond to different ways of resolving the uncertainty. While these IAFs could be good candidates for a compact representation of a disjunction of AFs, we prove that this model is not expressive enough. Then we introduce Constrained IAFs, that include a propositional formula allowing to select the set of completions used for reasoning. We prove that this model is expressive enough for representing any set of AFs, or any set of extensions. Moreover, we study the complexity of various decision problems related to the verification of extensions and the acceptability of arguments. While some of them are one level higher in the polynomial hierarchy (compared to their counterpart with standard IAFs), most of them have the same complexity than in the case of IAFs. Finally, we show that CIAFs can be used to model a new form of extension enforcement, where the possible evolutions of an AF are taken into account and modeled by the completions of the CIAF.
{"title":"Constrained incomplete argumentation frameworks: Expressiveness, complexity and enforcement","authors":"Jean-Guy Mailly","doi":"10.3233/aic-220298","DOIUrl":"https://doi.org/10.3233/aic-220298","url":null,"abstract":"Operations like belief change or merging have been adapted to the context of abstract argumentation. However, these operations may require to express some uncertainty or some disjunction in the result, which is not representable in classical AFs. For this reason, some of these earlier works requirea set of AFs or a set of extensions as the outcome of the operation, somehow to represent a “disjunction” of AFs or extensions. In parallel, the notion of Incomplete AFs (IAFs) has been developed recently. It corresponds to AFs where the existence of some arguments or attacks may be uncertain. Each IAF can be associated with a set of classical AFs called completions, that correspond to different ways of resolving the uncertainty. While these IAFs could be good candidates for a compact representation of a disjunction of AFs, we prove that this model is not expressive enough. Then we introduce Constrained IAFs, that include a propositional formula allowing to select the set of completions used for reasoning. We prove that this model is expressive enough for representing any set of AFs, or any set of extensions. Moreover, we study the complexity of various decision problems related to the verification of extensions and the acceptability of arguments. While some of them are one level higher in the polynomial hierarchy (compared to their counterpart with standard IAFs), most of them have the same complexity than in the case of IAFs. Finally, we show that CIAFs can be used to model a new form of extension enforcement, where the possible evolutions of an AF are taken into account and modeled by the completions of the CIAF.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"22 S2","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ana L.C. Bazzan, Ivana Dusparic, Marin Lujak, Giuseppe Vizzari
{"title":"Agents in Traffic and Transportation (ATT 2022): Revised and Extended Papers","authors":"Ana L.C. Bazzan, Ivana Dusparic, Marin Lujak, Giuseppe Vizzari","doi":"10.3233/aic-239001","DOIUrl":"https://doi.org/10.3233/aic-239001","url":null,"abstract":"","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"48 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134901203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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