這本領先的國際期刊促進和刺激人工智能領域的研究。從人工智能的工具和語言到它的哲學含義，計算智能涵蓋了廣泛的問題，為發表實驗和理論研究，以及調查和影響研究提供了一個強有力的論壇。該雜志的設計是為了滿足廣泛的人工智能工作者在學術和工業研究的需要。計算智能的焦點話題發現科學和知識挖掘。發現科學（又稱發現科學）是一種強調對大量實驗數據或文本數據進行分析的科學方法，目的是尋找新的模式或相關性，從而形成假設和其他科學方法。感興趣的工具包括：數據挖掘：在操作或事務數據中查找關聯或關系；文本挖掘和信息提?。涸谧匀徽Z言文本中查找概念及其關聯或關系；結構化、半結構化和非結構化文本挖掘；文本摘要：提取術語和短語來自總結其內容的大型文本文檔集合的ASE；Web挖掘：Web結構、內容和使用挖掘；以及從文本和數據庫學習本體。Web智能和語義Web。網絡智能涉及到人工智能在下一代網絡系統、服務和資源中的應用。這些包括更好的搜索/檢索算法、客戶端系統（例如更有效的代理）和服務器端系統（例如，在網頁和整個網站上展示材料的有效方法，包括自適應網站和個性化界面）。語義網是萬維網的一個擴展，在萬維網中，Web內容以程序（軟件代理）可訪問的形式表示，遵循Web作為數據、信息和知識交換的通用媒介的愿景。代理和多代理系統。代理作為一種計算抽象已經取代了軟件中的“對象”，并根據代理社會、市場經濟、電子商務模型和博弈論等概念，為向交互智能實體的社會轉移提供了必要的要素。這種抽象分布在整個科學世界，很大程度上取決于應用。多智能體系統（MAS）是許多自主智能體相互作用的系統。代理人可以是合作的，追求共同目標的，也可以是自私的，追求自己的利益。必須為多代理系統開發體系結構、交互協議和語言。感興趣的主題包括：面向自主的計算；代理系統方法和語言；基于代理的模擬和建模；基于代理的應用程序；基于代理的協商和自主拍賣；對多代理系統的高級軟件工程支持；對代理社會的信任；以及分布式問題解決。基于知識的系統中的機器學習?；谥R的系統旨在在需要時和需要時為決策和信息共享提供專業知識。下一代這樣的系統需要利用大領域的特定知識，將機器學習和結構化的背景知識表示（如本體論）以及因果表示和約束推理相結合。信息共享是指為共享和傳播信息創造協作的知識環境。學習是基于現實數據的。關鍵挑戰包括將實際問題分解為多個可學習組件、組件之間的交互以及應用適當的學習算法，通常是在缺乏足夠數量的標記訓練數據的情況下。感興趣的主題包括將機器學習方法應用于新的實際問題，引入新的算法、可學習組件的系統框架或評估技術。人工智能的關鍵應用領域。我們的目標是使期刊成為關鍵應用領域的焦點，在這些領域，人工智能正在產生重大影響，但缺乏連貫的出版場所。其中包括：商業智能，即支持商業決策者的數據挖掘；社交網絡挖掘，例如，對社交網絡的聚合屬性和動態進行建模，對社交網絡的頂點和邊緣進行分類，識別用戶群；關鍵的基礎設施保護，例如入侵/異常檢測和響應，以及學習系統管理、日志文件挖掘的知識庫；娛樂和游戲開發，即使用人工智能技術構建游戲引擎；軟件工程，包括程序理解、軟件存儲庫和逆向工程；商業、金融、商業和經濟：學習聚合行為（如股票市場趨勢）DS）或為個人和群體人口統計建模（例如，Web挖掘）；以及基于知識和個性化的用戶界面，以使交互更清晰、更高效，更好地支持用戶的目標，以及高效地呈現復雜信息。請注意，對于直接應用于機器學習或其他人工智能技術的新任務或新領域的提交，將在不進行審查的情況下被拒絕，除非它們在其他方面帶來了新穎性，如結果的重要性和分析、某些方法為何比這些領域中的其他方法更有效的解釋，或其他相關見解。

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.