COMPUTATIONAL INTELLIGENCE

这本领先的国际期刊促进和刺激人工智能领域的研究。从人工智能的工具和语言到它的哲学含义，计算智能涵盖了广泛的问题，为发表实验和理论研究，以及调查和影响研究提供了一个强有力的论坛。该杂志的设计是为了满足广泛的人工智能工作者在学术和工业研究的需要。计算智能的焦点话题发现科学和知识挖掘。发现科学（又称发现科学）是一种强调对大量实验数据或文本数据进行分析的科学方法，目的是寻找新的模式或相关性，从而形成假设和其他科学方法。感兴趣的工具包括：数据挖掘：在操作或事务数据中查找关联或关系；文本挖掘和信息提取：在自然语言文本中查找概念及其关联或关系；结构化、半结构化和非结构化文本挖掘；文本摘要：提取术语和短语来自总结其内容的大型文本文档集合的ASE；Web挖掘：Web结构、内容和使用挖掘；以及从文本和数据库学习本体。Web智能和语义Web。网络智能涉及到人工智能在下一代网络系统、服务和资源中的应用。这些包括更好的搜索/检索算法、客户端系统（例如更有效的代理）和服务器端系统（例如，在网页和整个网站上展示材料的有效方法，包括自适应网站和个性化界面）。语义网是万维网的一个扩展，在万维网中，Web内容以程序（软件代理）可访问的形式表示，遵循Web作为数据、信息和知识交换的通用媒介的愿景。代理和多代理系统。代理作为一种计算抽象已经取代了软件中的“对象”，并根据代理社会、市场经济、电子商务模型和博弈论等概念，为向交互智能实体的社会转移提供了必要的要素。这种抽象分布在整个科学世界，很大程度上取决于应用。多智能体系统（MAS）是许多自主智能体相互作用的系统。代理人可以是合作的，追求共同目标的，也可以是自私的，追求自己的利益。必须为多代理系统开发体系结构、交互协议和语言。感兴趣的主题包括：面向自主的计算；代理系统方法和语言；基于代理的模拟和建模；基于代理的应用程序；基于代理的协商和自主拍卖；对多代理系统的高级软件工程支持；对代理社会的信任；以及分布式问题解决。基于知识的系统中的机器学习。基于知识的系统旨在在需要时和需要时为决策和信息共享提供专业知识。下一代这样的系统需要利用大领域的特定知识，将机器学习和结构化的背景知识表示（如本体论）以及因果表示和约束推理相结合。信息共享是指为共享和传播信息创造协作的知识环境。学习是基于现实数据的。关键挑战包括将实际问题分解为多个可学习组件、组件之间的交互以及应用适当的学习算法，通常是在缺乏足够数量的标记训练数据的情况下。感兴趣的主题包括将机器学习方法应用于新的实际问题，引入新的算法、可学习组件的系统框架或评估技术。人工智能的关键应用领域。我们的目标是使期刊成为关键应用领域的焦点，在这些领域，人工智能正在产生重大影响，但缺乏连贯的出版场所。其中包括：商业智能，即支持商业决策者的数据挖掘；社交网络挖掘，例如，对社交网络的聚合属性和动态进行建模，对社交网络的顶点和边缘进行分类，识别用户群；关键的基础设施保护，例如入侵/异常检测和响应，以及学习系统管理、日志文件挖掘的知识库；娱乐和游戏开发，即使用人工智能技术构建游戏引擎；软件工程，包括程序理解、软件存储库和逆向工程；商业、金融、商业和经济：学习聚合行为（如股票市场趋势）DS）或为个人和群体人口统计建模（例如，Web挖掘）；以及基于知识和个性化的用户界面，以使交互更清晰、更高效，更好地支持用户的目标，以及高效地呈现复杂信息。请注意，对于直接应用于机器学习或其他人工智能技术的新任务或新领域的提交，将在不进行审查的情况下被拒绝，除非它们在其他方面带来了新颖性，如结果的重要性和分析、某些方法为何比这些领域中的其他方法更有效的解释，或其他相关见解。

This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.FOCAL TOPICS OF COMPUTATIONAL INTELLIGENCEDiscovery science and knowledge mining. Discovery science (also known as discovery-based science) is a scientific methodology which emphasizes analysis of large volumes of experimental data or text data with the goal of finding new patterns or correlations, leading to hypothesis formation and other scientific methodologies. Tools of interest include: Data Mining: looking for associations or relationships in operational or transactional data; Text Mining and Information Extraction: looking for concepts and their associations or relationships in natural language text; Structured, semi-structured and unstructured text mining; Text Summarization: extracting terms and phrases from large text document collections that summarize their content; Web mining: Web structure, content and usage mining; and, Ontology Learning from Text and Data bases.Web intelligence and semantic web. Web intelligence is concerned with the application of AI to the next generation of web systems, services and resources. These include better search/retrieval algorithms, client side systems (e.g. more effective agents) and server side systems (e.g. effective ways to present material on web pages and throughout web sites, including adaptive websites and personalized interfaces).The semantic web is an extension to the World Wide Web, in which web content is expressed in a form that is accessible to programs (software agents), following the vision of the web as universal medium for data, information and knowledge exchange.Agents and multiagent systems. Agents as a computational abstraction have replaced 'objects' in software and have provided the necessary ingredients to move to societies of interacting intelligent entities, based on concepts like agent societies, market economies, e-commerce models and game theory. Such abstractions are dispersed throughout the scientific world, depending largely on applications. Multiagent systems (MAS) are systems in which many autonomous intelligent agents interact with each other. Agents can be either cooperative, pursuing a common goal, or selfish, going after their own interests. Architectures, interaction protocols and languages must be developed for multiagent systems. Topics of interest include: Autonomy-oriented computing; Agent systems methodology and language; Agent-based simulation and modeling; Agent-based applications; Agent-based negotiation and autonomous auction; Advanced Software Engineering supports for Multiagent systems; Trust in Agent Society; and Distributed problem solving.Machine learning in knowledge-based systems. Knowledge-based systems aim to make expertise available for decision making, and information sharing, when and where needed. The next generation of such systems needs to tap into large domain-specific knowledge, which combine machine learning and structured background knowledge representation, such as ontology, and causal representations and constraint reasoning. Information sharing is concerned with creating collaborative knowledge environments for sharing and disseminating information. Learning is based on real-world data. Key challenges involve the decomposition of practical problems into multiple learnable components, the interaction between the components, and the application of suitable learning algorithms, often in the absence of adequate amounts of labeled training data. Topics of interest include the application of machine learning methods to new practical problems introducing novel algorithms, system frameworks of learnable components or evaluation techniques.Key application areas of AI. We aim to make the journal the focus of key application areas, where AI is making a significant impact, but lack a coherent publication venue. These include: Business Intelligence, i.e. data mining to support business decision makers; Social Network mining, e.g. modelling aggregate properties and dynamics of social networks, classifying vertices and edges of social networks, identifying clusters of users; Critical Infrastructure Protection, e.g. intrusion/anomaly detection & response, learning knowledge bases of system administration, log file mining); Entertainment and Game Development, i.e. building game engines using AI techniques; Software Engineering, including program understanding, software repositories and reverse engineering; Business, Finance, Commerce and Economics: learning aggregate behaviours (e.g. stock market trends) or modeling individual and group demographics (e.g. web mining); and Knowledge-based and Personalized User Interfaces, to make interaction clearer to the user and more efficient, with better support for the users' goals, and efficient presentation of complex information.Please note that submissions that are straightforward applications to Machine Learning or other AI techniques to new tasks or new domains will be rejected without review unless they bring novelty in other aspects, such as significance and analysis of the results, explanations of why some methods work better than others in these domains, or other relevant insights.