中文简介

本杂志旨在支持各种研究领域交叉施肥产生的新的计算和认知范式的发展。这些领域包括但不限于编程（逻辑、约束、功能、面向对象）、分布式/并行计算、基于知识的系统、面向代理的系统以及人类具体知识的认知方面。它还鼓励有关所有类型的学习、知识发现、进化机制、人类认知和学习的理论和/或实践论文，以及能够引导我们构建更复杂和智能系统的关键技术的新兴系统。编委会希望新一代的计算技术能够通过确保出版过程的顺利进行，在具有广泛兴趣的活跃研究人员中起到催化剂的作用。新一代计算涉及的领域包括：学习：学习的基础和模型，计算学习理论，语法推理，归纳逻辑编程，统计学习方法，贝叶斯网络，强化学习数据挖掘：频繁模式挖掘、流数据挖掘、图形和网络挖掘、关系数据挖掘、文本和Web挖掘、数据挖掘的统计方法、数据挖掘的机器学习方法、数据挖掘的可视化方法。认知计算：建模人类知识、建模人类问题解决与学习、语义计算、建模与分析决策、认知架构、人工通用智能、人级人工智能。编程和语义：计算的基础和模型，计算逻辑，编程系统，声明式编程，并发和并行，量子计算。生物和纳米系统的控制理论：分子系统的形式模型，基于令牌系统的计算，自然界中信号的非布尔表示，基于自然界中发现的机制的细胞自动机。生物/纳米/分子计算与工程：分子机器人与人工细胞、DNA纳米工程、分子计算/编程、自组织系统。技能科学与哲学：生活中的技能与知识、交流与社会技能、身技能与知识的学习、“感性”与价值创造、体育科学、身体运动的测量与分析、身体系统论、技能科学的认知方法、本体感觉的主观言语化、共同进化身体和语言的n，符号接地，符号生成计算社会科学：社会媒体、网络服务、网络挖掘、社会研究、语义网络、众包、社会系统、社会模拟、虚拟实验室

投稿网址：http://www.ohmsha.co.jp/ngc/instruction.htm

英文简介

The journal is specially intended to support the development of new computational and cognitive paradigms stemming from the cross-fertilization of various research fields. These fields include, but are not limited to, programming (logic, constraint, functional, object-oriented), distributed/parallel computing, knowledge-based systems, agent-oriented systems, and cognitive aspects of human embodied knowledge. It also encourages theoretical and/or practical papers concerning all types of learning, knowledge discovery, evolutionary mechanisms, human cognition and learning, and emergent systems that can lead to key technologies enabling us to build more complex and intelligent systems. The editorial board hopes that New Generation Computing will work as a catalyst among active researchers with broad interests by ensuring a smooth publication process.Areas covered in New Generation Computing include:Learning: Foundations and Models of Learning, Computational Learning Theory, Grammatical Inference, Inductive Logic Programming, Statistical Learning Methods, Bayesian Networks, Reinforcement LearningData Mining: Frequent pattern mining, Stream Data Mining, Graph and Network mining, Relational Data Mining, Text and Web Mining, Statistical methods for Data Mining, Machine learning methods for Data Mining, Visualization methods for Data MiningCognitive Computing: Modeling Human Knowledge, Modeling Human Problem Solving and Learning, Semantic Computing, Modeling and Analyzing Decision Making, Cognitive Architecture, Artificial General Intelligence, Human Level AI.Programming and Semantics: Foundations and Models of Computation, Computational Logic, Programming Systems, Declarative Programming, Concurrency and Parallelism, Quantum Computing.Control Theory of Bio- and Nano-systems: Formal Models of Molecular Systems, Computation by Token-based Systems, Non-Boolean Representations of Signals in Nature, Cellular Automata Based on Mechanisms Found in Nature.Bio/Nano/Molecular Computing and Engineering: Molecular Robotics & Artificial Cells, DNA Nanoengineering, Molecular Computing/Programming, Self-organizing Systems.Skill Science and Philosophy: Skills and Knowledge in Life, Communication and Social Skills, Learning of Embodied Skills and Knowledge, “Kansei" and Value Creation, Sports Science, Measurement and Analysis of Body Movements, Systems Theory of Body, Cognitive Approach of Skill Science, Subjective Verbalization of Proprioceptive Sense, Co-evolution of Body and Language, Symbol Grounding, Symbol GenerationComputational Social Science: Social Media, Web Services, Web Mining, Social Studies, Semantic Web, Crowdsourcing, Social Systems, Social Simulation, Virtual Lab