This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in their behaviour over time, for example how to classify companies' share price stability over a period of time or how to classify students' preferences for subjects while they are progressing through school. A specific case the paper considers is the strategy of players in public goods games (as common in economics) across multiple consecutive games. Initial classification starts from expert definitions specifying class allocation for players based on aggregated attributes of the temporal data. Based on these initial classifications, the optimisation process tries to find an improved classifier which produces the best possible compact classes of objects (players) for every time point in the temporal data. The compactness of the classes is measured by a cost function based on internal cluster indices like the Dunn Index, distance measures like Euclidean distance or statistically derived measures like standard deviation. The paper discusses the approach in the context of incorporating changing player strategies in the aforementioned public good games, where common classification approaches so far do not consider such changes in behaviour resulting from learning or in-game experience. By using the proposed process for classifying temporal data and the actual players' contribution during the games, we aim to produce a more refined classification which in turn may inform the interpretation of public goods game data.
Optimising the attribute order in Fuzzy Rough Rule Induction
Department of Mathematics, Computer Science and Statistics, Ghent University（数学、计算机科学与统计学系，根特大学）；Faculty of Economics, University of Catania（经济学院，卡塔尼娅大学）；Institute of Computing Science, Poznań University of Technology（计算科学研究所，波兹南技术大学）；Systems Research Institute, Polish Academy of Sciences（系统研究所，波兰科学院）
Optimising Temporary Accommodation Placement Across London with AI-Powered SaaS in E-Governance Systems
UK Centre for AI in the Public Sector（英国公共部门人工智能中心）；Institute for Connected Communities（连接社区研究所）；University of East London（东伦敦大学）；London Borough of Newham（伦敦新ham区）
Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins
Tsinghua University（清华大学）；Portland State University（波特兰州立大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）
Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction
6 Ohio University, USA.\；4 U.S. Army DEVCOM Army Research Laboratory, USA.\；8 Cardiff University, UK.\；9 University of Southampton, UK.\；10 Imperial College London, UK.\
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
University of Washington（华盛顿大学）；Allen Institute for Brain Science（艾伦脑科学研究所）；McGill University（麦吉尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Montreal Neurological Institute（蒙特利尔神经学研究所）；Université de Montréal（蒙特利尔大学）
TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networks
School of Electrical and Computer Engineering, Purdue University, Indiana, USA（电气与计算机工程学院，普渡大学，印第安纳州，美国）；Microelectronics Department, Delft University of Technology, Delft, NL（微电子系，代尔夫特理工大学，代尔夫特，荷兰）
Improving Task Instructions for Data Annotators: How Clear Rules and Higher Pay Increase Performance in Data Annotation in the AI Economy
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究院）；Humboldt Institute for Internet and Society（洪堡互联网与社会研究院）；Bruegel（布鲁盖尔研究所）；University College London（伦敦大学学院）
Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Computer Science, Yale University（耶鲁大学计算机科学系）；Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳高等研究院）
