人工智能每日论文速递[07.23]
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cs.AI 方向,今日共计38篇
[cs.AI]:
【1】 Paracoherent Answer Set Semantics meets Argumentation Frameworks
标题:Parcoherent答案集语义满足论证框架
作者: Giovanni Amendola, Francesco Ricca
备注:Paper presented at the 35th International Conference on Logic Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019, 16 pages
链接:https://arxiv.org/abs/1907.09426
【2】 Sensitivity study of ANFIS model parameters to predict the pressure gradient with combined input and outputs hydrodynamics parameters in the bubble column reactor
标题:ANFIS模型参数预测鼓泡塔反应器压力梯度的敏感性研究
作者: Shahaboddin Shamshirband, Kwok-wing Chau
链接:https://arxiv.org/abs/1907.09309
【3】 DREAMT -- Embodied Motivational Conversational Storytelling
标题:Dreamt-具体化动机对话讲故事
作者: David M W Powers
备注:12 pages; to be presented as lightning talk plus poster at StoryNLP on 1 August 2019 at ACL in Florence - poster pdf and powerpoint available
链接:https://arxiv.org/abs/1907.09293
【4】 ParaFIS:A new online fuzzy inference system based on parallel drift anticipation
标题:ParaFIS:一种新的基于并行漂移预测的在线模糊推理系统
作者: Clement Leroy, Nathalie Girard
链接:https://arxiv.org/abs/1907.09285
【5】 A Sufficient Statistic for Influence in Structured Multiagent Environments
标题:结构化多Agent环境中影响的充分统计量
作者: Frans A. Oliehoek, Leslie P. Kaelbling
链接:https://arxiv.org/abs/1907.09278
【6】 Why Build an Assistant in Minecraft?
标题:为什么要在“我的世界”中建立一个助手?
作者: Arthur Szlam, Jason Weston
链接:https://arxiv.org/abs/1907.09273
【7】 Orometric Methods in Bounded Metric Data
标题:有界度量数据中的度量方法
作者: Maximilian Stubbemann, Gerd Stumme
链接:https://arxiv.org/abs/1907.09239
【8】 Incremental Answer Set Programming with Overgrounding
标题:带过接地的增量答案集编程
作者: Francesco Calimeri, Jessica Zangari
备注:Paper presented at the 35th International Conference on Logic Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019, 16 pages
链接:https://arxiv.org/abs/1907.09212
【9】 Aggregating Probabilistic Judgments
标题:聚集概率判断
作者: Magdalena Ivanovska, Marija Slavkovik
备注:In Proceedings TARK 2019, arXiv:1907.08335
链接:https://arxiv.org/abs/1907.09111
【10】 Open Problems in a Logic of Gossips
标题:八卦逻辑中的开放问题
作者: Krzysztof R. Apt (Centrum Wiskunde & Informatica, UK)
备注:In Proceedings TARK 2019, arXiv:1907.08335
链接:https://arxiv.org/abs/1907.09097
【11】 Quantifying Similarity between Relations with Fact Distribution
标题:用事实分布量化关系之间的相似性
作者: Weize Chen, Maosong Sun
备注:ACL 2019
链接:https://arxiv.org/abs/1907.08937
【12】 Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests
标题:动态出行-具有预定和按需请求的车辆调度
作者: Taoan Huang, Fei Fang
链接:https://arxiv.org/abs/1907.08739
【13】 Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory
标题:基于累积前景理论的交互式驾驶场景中驾驶行为的可解释建模
作者: Liting Sun, Masayoshi Tomizuka
备注:accepted to the 2019 IEEE Intelligent Transportation System Conference (ITSC2019)
链接:https://arxiv.org/abs/1907.08707
【14】 Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking
标题:仓库订单拣选广义旅行商问题的条件马尔可夫链搜索
作者: Olegs Nalivajevs, Daniel Karapetyan
链接:https://arxiv.org/abs/1907.08647
【15】 Abstract Solvers for Computing Cautious Consequences of ASP programs
标题:计算ASP程序谨慎后果的抽象求解器
作者: Giovanni Amendola, Marco Maratea
备注:Paper presented at the 35th International Conference on Logic Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019, 20 pages
链接:https://arxiv.org/abs/1907.09402
【16】 Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs
标题:基于神经网络的知识图问答方法介绍
作者: Nilesh Chakraborty, Asja Fischer
链接:https://arxiv.org/abs/1907.09361
【17】 VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications
标题:VRLS:车辆对车辆通信的统一强化学习调度器
作者: Taylan Şahin, Adam Wolisz
备注:Article accepted to IEEE CAVS 2019
链接:https://arxiv.org/abs/1907.09319
【18】 The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
标题:后特例可解释性的危险:不合理的反事实解释
作者: Thibault Laugel, Marcin Detyniecki
链接:https://arxiv.org/abs/1907.09294
【19】 Almost Group Envy-free Allocation of Indivisible Goods and Chores
标题:几乎不受集团嫉妒的不可分割的商品和家务活的分配
作者: Haris Aziz, Simon Rey
链接:https://arxiv.org/abs/1907.09279
【20】 Founded (Auto)Epistemic Equilibrium Logic Satisfies Epistemic Splitting
标题:建立(自动)认知平衡逻辑满足认知分裂
作者: Jorge Fandinno
备注:Paper presented at the 35th International Conference on Logic Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019, 16 pages
链接:https://arxiv.org/abs/1907.09247
【21】 Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm
标题:基于质量多样性算法的斑马鱼群体行为人工神经网络自动校准
作者: Leo Cazenille, José Halloy
链接:https://arxiv.org/abs/1907.09209
【22】 Today Me, Tomorrow Thee: Efficient Resource Allocation in Competitive Settings using Karma Games
标题:今天的我,明天的你:使用因果游戏在竞争环境中进行有效的资源分配
作者: Andrea Censi, Emilio Frazzoli
链接:https://arxiv.org/abs/1907.09198
【23】 Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems
标题:多智能体学习算法在一组不同的Ad Hoc团队问题中的比较评估
作者: Stefano V. Albrecht, Subramanian Ramamoorthy
备注:Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2012. This arXiv version of the original paper published in AAMAS 2012 uses an expanded title to spell out "MAL", and is otherwise identical
链接:https://arxiv.org/abs/1907.09189
【24】 FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
标题:FedHealth:可穿戴式医疗的联邦转移学习框架
作者: Yiqiang Chen, Xin Qin
备注:IJCAI-19 Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (IJCAI (FML)) 2019
链接:https://arxiv.org/abs/1907.09173
【25】 Measuring Belief and Risk Attitude
标题:测量信念和风险态度
作者: Sven Neth (University of California, Berkeley)
备注:In Proceedings TARK 2019, arXiv:1907.08335
链接:https://arxiv.org/abs/1907.09115
【26】 Exploiting Belief Bases for Building Rich Epistemic Structures
标题:开发信念基础构建丰富的认知结构
作者: Emiliano Lorini (IRIT-CNRS, France)
备注:In Proceedings TARK 2019, arXiv:1907.08335
链接:https://arxiv.org/abs/1907.09114
【27】 Aggregation in Value-Based Argumentation Frameworks
标题:基于价值的论证框架中的聚合
作者: Grzegorz Lisowski (University of Warwick), Umberto Grandi (University of Toulouse)
备注:In Proceedings TARK 2019, arXiv:1907.08335
链接:https://arxiv.org/abs/1907.09113
【28】 A Conceptually Well-Founded Characterization of Iterated Admissibility Using an "All I Know" Operator
标题:使用“我所知道的”算子对迭代可容许性进行概念上完善的表征
作者: Joseph Y. Halpern (Cornell University), Rafael Pass (Cornell University)
备注:In Proceedings TARK 2019, arXiv:1907.08335
链接:https://arxiv.org/abs/1907.09106
【29】 A Unified Algebraic Framework for Non-Monotonicity
标题:非单调性的统一代数框架
作者: Nourhan Ehab, Haythem O. Ismail
备注:In Proceedings TARK 2019, arXiv:1907.08335
链接:https://arxiv.org/abs/1907.09103
【30】 Accelerating Experimental Design by Incorporating Experimenter Hunches
标题:通过加入实验者预感加速实验设计
作者: Cheng Li, Ian Gibson
备注:IEEE International Conference on Data Mining (ICDM) 2018
链接:https://arxiv.org/abs/1907.09065
【31】 High Dimensional Bayesian Optimization via Supervised Dimension Reduction
标题:基于监督降维的高维贝叶斯优化
作者: Miao Zhang, Steven Su
备注:7 pages, 3 figures, IJCAI 2019 accepted paper
链接:https://arxiv.org/abs/1907.08953
【32】 Techniques for Automated Machine Learning
标题:机器自动学习技术
作者: Yi-Wei Chen, Xia Hu
链接:https://arxiv.org/abs/1907.08908
【33】 Log-linear models independence structure comparison
标题:对数线性模型独立性结构比较
作者: Jan Strappa, Facundo Bromberg
链接:https://arxiv.org/abs/1907.08892
【34】 Potential-Based Advice for Stochastic Policy Learning
标题:基于潜力的随机策略学习建议
作者: Baicen Xiao, Radha Poovendran
备注:Accepted to the IEEE Conference on Decision and Control, 2019
链接:https://arxiv.org/abs/1907.08823
【35】 Linked Crunchbase: A Linked Data API and RDF Data Set About Innovative Companies
标题:链接的Crunchbase:关于创新公司的链接数据API和RDF数据集
作者: Michael Färber
链接:https://arxiv.org/abs/1907.08671
【36】 Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
标题:Snamed2Vec:医疗保健分析临床知识库的随机行走和Poincaré嵌入
作者: Khushbu Agarwal, Robert Rallo
备注:2019 KDD Workshop on Applied Data Science for Healthcare (DSHealth '19). this https URL
链接:https://arxiv.org/abs/1907.08650
【37】 3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks
标题:3DPalsyNet:一个使用全三维卷积神经网络的面瘫分级和运动识别框架
作者: Gary Storey, Chang-Tsun Li
链接:https://arxiv.org/abs/1905.13607
【38】 Social Behavioral Phenotyping of Drosophila with a2D-3D Hybrid CNN Framework
标题:2D-3D混合CNN框架下果蝇的社会行为表型
作者: Ziping Jiang, Richard Jiang
链接:https://arxiv.org/abs/1903.11421
翻译:腾讯翻译君