Journal of Artificial Intelligence Research最新文献_第2页

A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection 轨迹相似性测量的原则性分布方法及其在异常检测中的应用

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-03-13 DOI: 10.1613/jair.1.15849

Yufan Wang, Zijing Wang, Kai Ming Ting, Yuanyi Shang

{"title":"A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection","authors":"Yufan Wang, Zijing Wang, Kai Ming Ting, Yuanyi Shang","doi":"10.1613/jair.1.15849","DOIUrl":"https://doi.org/10.1613/jair.1.15849","url":null,"abstract":"This paper aims to solve two enduring challenges in existing trajectory similarity measures: computational inefficiency and the absence of the ‘uniqueness’ property that should be guaranteed in a distance function: dist(X, Y ) = 0 if and only if X = Y , where X and Y are two trajectories. In this work, we present a novel approach utilizing a distributional kernel for trajectory representation and similarity measurement, based on the kernel mean embedding framework. It is the very first time a distributional kernel is used for trajectory representation and similarity measurement. Our method does not rely on point-to-point distances which are used in most existing distances for trajectories. Unlike prevalent learning and deep learning approaches, our method requires no learning. We show the generality of this new approach in anomalous trajectory and sub-trajectory detection. We identify that the distributional kernel has (i) a data-dependent property and the ‘uniqueness’ property which are the key factors that lead to its superior task-specific performance, and (ii) runtime orders of magnitude faster than existing distance measures.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Tradeoff Between System Profit and Income Equality Among Ride-hailing Drivers 探索系统利润与乘车司机收入平等之间的权衡

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-02-13 DOI: 10.1613/jair.1.15170

Evan Yifan Xu, Pan Xu

{"title":"Exploring the Tradeoff Between System Profit and Income Equality Among Ride-hailing Drivers","authors":"Evan Yifan Xu, Pan Xu","doi":"10.1613/jair.1.15170","DOIUrl":"https://doi.org/10.1613/jair.1.15170","url":null,"abstract":"This paper examines the income inequality among rideshare drivers resulting from discriminatory cancellations by riders, considering the impact of demographic factors such as gender, age, and race. We investigate the tradeoff between income inequality, referred to as the fairness objective, and system efficiency, known as the profit objective. To address this issue, we propose an online bipartite-matching model that captures the sequential arrival of riders according to a known distribution. The model incorporates the notion of acceptance rates between driver-rider types, which are defined based on demographic characteristics. Specifically, we analyze the probabilities of riders accepting or canceling their assigned drivers, reflecting the level of acceptance between different rider and driver types. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous analysis of online competitive ratios is conducted to illustrate the flexibility and efficiency of our algorithms in achieving a balance between fairness and profit. Furthermore, we present experimental results based on real-world and synthetic datasets, validating the theoretical predictions put forth in our study.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Tradeoff Between System Profit and Income Equality Among Ride-hailing Drivers 探索系统利润与乘车司机收入平等之间的权衡

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-02-13 DOI: 10.1613/jair.1.15170

Evan Yifan Xu, Pan Xu

{"title":"Exploring the Tradeoff Between System Profit and Income Equality Among Ride-hailing Drivers","authors":"Evan Yifan Xu, Pan Xu","doi":"10.1613/jair.1.15170","DOIUrl":"https://doi.org/10.1613/jair.1.15170","url":null,"abstract":"This paper examines the income inequality among rideshare drivers resulting from discriminatory cancellations by riders, considering the impact of demographic factors such as gender, age, and race. We investigate the tradeoff between income inequality, referred to as the fairness objective, and system efficiency, known as the profit objective. To address this issue, we propose an online bipartite-matching model that captures the sequential arrival of riders according to a known distribution. The model incorporates the notion of acceptance rates between driver-rider types, which are defined based on demographic characteristics. Specifically, we analyze the probabilities of riders accepting or canceling their assigned drivers, reflecting the level of acceptance between different rider and driver types. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous analysis of online competitive ratios is conducted to illustrate the flexibility and efficiency of our algorithms in achieving a balance between fairness and profit. Furthermore, we present experimental results based on real-world and synthetic datasets, validating the theoretical predictions put forth in our study.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weighted, Circular and Semi-Algebraic Proofs 加权、循环和半代数证明

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-02-11 DOI: 10.1613/jair.1.15075

Ilario Bonacina, Maria Luisa Bonet, Jordi Levy

引用次数: 0

Weighted, Circular and Semi-Algebraic Proofs 加权、循环和半代数证明

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-02-11 DOI: 10.1613/jair.1.15075

Ilario Bonacina, Maria Luisa Bonet, Jordi Levy

引用次数: 0

Query-driven Qualitative Constraint Acquisition 查询驱动的定性约束获取

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-01-26 DOI: 10.1613/jair.1.14752

M. Belaid, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker

引用次数: 0

Visually Grounded Language Learning: A Review of Language Games, Datasets, Tasks, and Models 以视觉为基础的语言学习：语言游戏、数据集、任务和模型综述

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-01-26 DOI: 10.1613/jair.1.15185

Alessandro Suglia, Ioannis Konstas, Oliver Lemon

{"title":"Visually Grounded Language Learning: A Review of Language Games, Datasets, Tasks, and Models","authors":"Alessandro Suglia, Ioannis Konstas, Oliver Lemon","doi":"10.1613/jair.1.15185","DOIUrl":"https://doi.org/10.1613/jair.1.15185","url":null,"abstract":"In recent years, several machine learning models have been proposed. They are trained with a language modelling objective on large-scale text-only data. With such pretraining, they can achieve impressive results on many Natural Language Understanding and Generation tasks. However, many facets of meaning cannot be learned by “listening to the radio” only. In the literature, many Vision+Language (V+L) tasks have been defined with the aim of creating models that can ground symbols in the visual modality. In this work, we provide a systematic literature review of several tasks and models proposed in the V+L field. We rely on Wittgenstein’s idea of ‘language games’ to categorise such tasks into 3 different families: 1) discriminative games, 2) generative games, and 3) interactive games. Our analysis of the literature provides evidence that future work should be focusing on interactive games where communication in Natural Language is important to resolve ambiguities about object referents and action plans and that physical embodiment is essential to understand the semantics of situations and events. Overall, these represent key requirements for developing grounded meanings in neural models.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139595218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty 正确的地点，正确的时间：时空不确定性下的主动式多机器人任务分配

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-01-11 DOI: 10.1613/jair.1.15057

Charlie Street, Bruno Lacerda, Manuel Mühlig, N. Hawes

{"title":"Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty","authors":"Charlie Street, Bruno Lacerda, Manuel Mühlig, N. Hawes","doi":"10.1613/jair.1.15057","DOIUrl":"https://doi.org/10.1613/jair.1.15057","url":null,"abstract":"For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Undesirable Biases in NLP: Addressing Challenges of Measurement NLP 中的不良偏差：应对测量挑战

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-01-10 DOI: 10.1613/jair.1.15195

Oskar van der Wal, Dominik Bachmann, Alina Leidinger, Leendert van Maanen, Willem Zuidema, Katrin Schulz

{"title":"Undesirable Biases in NLP: Addressing Challenges of Measurement","authors":"Oskar van der Wal, Dominik Bachmann, Alina Leidinger, Leendert van Maanen, Willem Zuidema, Katrin Schulz","doi":"10.1613/jair.1.15195","DOIUrl":"https://doi.org/10.1613/jair.1.15195","url":null,"abstract":"As Large Language Models and Natural Language Processing (NLP) technology rapidly develop and spread into daily life, it becomes crucial to anticipate how their use could harm people. One problem that has received a lot of attention in recent years is that this technology has displayed harmful biases, from generating derogatory stereotypes to producing disparate outcomes for different social groups. Although a lot of effort has been invested in assessing and mitigating these biases, our methods of measuring the biases of NLP models have serious problems and it is often unclear what they actually measure. In this paper, we provide an interdisciplinary approach to discussing the issue of NLP model bias by adopting the lens of psychometrics — a field specialized in the measurement of concepts like bias that are not directly observable. In particular, we will explore two central notions from psychometrics, the construct validity and the reliability of measurement tools, and discuss how they can be applied in the context of measuring model bias. Our goal is to provide NLP practitioners with methodological tools for designing better bias measures, and to inspire them more generally to explore tools from psychometrics when working on bias measurement tools.\u0000This article appears in the AI & Society track.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139535112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Principles and their Computational Consequences for Argumentation Frameworks with Collective Attacks 集体攻击论证框架的原则及其计算结果

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2024-01-10 DOI: 10.1613/jair.1.14879

Wolfgang Dvořák, Matthias König, Markus Ulbricht, S. Woltran

{"title":"Principles and their Computational Consequences for Argumentation Frameworks with Collective Attacks","authors":"Wolfgang Dvořák, Matthias König, Markus Ulbricht, S. Woltran","doi":"10.1613/jair.1.14879","DOIUrl":"https://doi.org/10.1613/jair.1.14879","url":null,"abstract":"Argumentation frameworks (AFs) are a key formalism in AI research. Their semantics have been investigated in terms of principles, which define characteristic properties in order to deliver guidance for analyzing established and developing new semantics. Because of the simple structure of AFs, many desired properties hold almost trivially, at the same time hiding interesting concepts behind syntactic notions. We extend the principle-based approach to argumentation frameworks with collective attacks (SETAFs) and provide a comprehensive overview of common principles for their semantics. Our analysis shows that investigating principles based on decomposing the given SETAF (e.g. directionality or SCC-recursiveness) poses additional challenges in comparison to usual AFs. We introduce the notion of the reduct as well as the modularization principle for SETAFs which will prove beneficial for this kind of investigation. We then demonstrate how our findings can be utilized for incremental computation of extensions and show how we can use graph properties of the frameworks to speed up these algorithms.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0