這本領(lǐng)先的國際期刊促進和刺激人工智能領(lǐng)域的研究。從人工智能的工具和語言到它的哲學(xué)含義，計算智能涵蓋了廣泛的問題，為發(fā)表實驗和理論研究，以及調(diào)查和影響研究提供了一個強有力的論壇。該雜志的設(shè)計是為了滿足廣泛的人工智能工作者在學(xué)術(shù)和工業(yè)研究的需要。計算智能的焦點話題發(fā)現(xiàn)科學(xué)和知識挖掘。發(fā)現(xiàn)科學(xué)（又稱發(fā)現(xiàn)科學(xué)）是一種強調(diào)對大量實驗數(shù)據(jù)或文本數(shù)據(jù)進行分析的科學(xué)方法，目的是尋找新的模式或相關(guān)性，從而形成假設(shè)和其他科學(xué)方法。感興趣的工具包括：數(shù)據(jù)挖掘：在操作或事務(wù)數(shù)據(jù)中查找關(guān)聯(lián)或關(guān)系；文本挖掘和信息提取：在自然語言文本中查找概念及其關(guān)聯(lián)或關(guān)系；結(jié)構(gòu)化、半結(jié)構(gòu)化和非結(jié)構(gòu)化文本挖掘；文本摘要：提取術(shù)語和短語來自總結(jié)其內(nèi)容的大型文本文檔集合的ASE；Web挖掘：Web結(jié)構(gòu)、內(nèi)容和使用挖掘；以及從文本和數(shù)據(jù)庫學(xué)習(xí)本體。Web智能和語義Web。網(wǎng)絡(luò)智能涉及到人工智能在下一代網(wǎng)絡(luò)系統(tǒng)、服務(wù)和資源中的應(yīng)用。這些包括更好的搜索/檢索算法、客戶端系統(tǒng)（例如更有效的代理）和服務(wù)器端系統(tǒng)（例如，在網(wǎng)頁和整個網(wǎng)站上展示材料的有效方法，包括自適應(yīng)網(wǎng)站和個性化界面）。語義網(wǎng)是萬維網(wǎng)的一個擴展，在萬維網(wǎng)中，Web內(nèi)容以程序（軟件代理）可訪問的形式表示，遵循Web作為數(shù)據(jù)、信息和知識交換的通用媒介的愿景。代理和多代理系統(tǒng)。代理作為一種計算抽象已經(jīng)取代了軟件中的“對象”，并根據(jù)代理社會、市場經(jīng)濟、電子商務(wù)模型和博弈論等概念，為向交互智能實體的社會轉(zhuǎn)移提供了必要的要素。這種抽象分布在整個科學(xué)世界，很大程度上取決于應(yīng)用。多智能體系統(tǒng)（MAS）是許多自主智能體相互作用的系統(tǒng)。代理人可以是合作的，追求共同目標(biāo)的，也可以是自私的，追求自己的利益。必須為多代理系統(tǒng)開發(fā)體系結(jié)構(gòu)、交互協(xié)議和語言。感興趣的主題包括：面向自主的計算；代理系統(tǒng)方法和語言；基于代理的模擬和建模；基于代理的應(yīng)用程序；基于代理的協(xié)商和自主拍賣；對多代理系統(tǒng)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(tǒng)中的機器學(xué)習(xí)。基于知識的系統(tǒng)旨在在需要時和需要時為決策和信息共享提供專業(yè)知識。下一代這樣的系統(tǒng)需要利用大領(lǐng)域的特定知識，將機器學(xué)習(xí)和結(jié)構(gòu)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(jié)合。信息共享是指為共享和傳播信息創(chuàng)造協(xié)作的知識環(huán)境。學(xué)習(xí)是基于現(xiàn)實數(shù)據(jù)的。關(guān)鍵挑戰(zhàn)包括將實際問題分解為多個可學(xué)習(xí)組件、組件之間的交互以及應(yīng)用適當(dāng)?shù)膶W(xué)習(xí)算法，通常是在缺乏足夠數(shù)量的標(biāo)記訓(xùn)練數(shù)據(jù)的情況下。感興趣的主題包括將機器學(xué)習(xí)方法應(yīng)用于新的實際問題，引入新的算法、可學(xué)習(xí)組件的系統(tǒng)框架或評估技術(shù)。人工智能的關(guān)鍵應(yīng)用領(lǐng)域。我們的目標(biāo)是使期刊成為關(guān)鍵應(yīng)用領(lǐng)域的焦點，在這些領(lǐng)域，人工智能正在產(chǎn)生重大影響，但缺乏連貫的出版場所。其中包括：商業(yè)智能，即支持商業(yè)決策者的數(shù)據(jù)挖掘；社交網(wǎng)絡(luò)挖掘，例如，對社交網(wǎng)絡(luò)的聚合屬性和動態(tài)進行建模，對社交網(wǎng)絡(luò)的頂點和邊緣進行分類，識別用戶群；關(guān)鍵的基礎(chǔ)設(shè)施保護，例如入侵/異常檢測和響應(yīng)，以及學(xué)習(xí)系統(tǒng)管理、日志文件挖掘的知識庫；娛樂和游戲開發(fā)，即使用人工智能技術(shù)構(gòu)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(yè)、金融、商業(yè)和經(jīng)濟：學(xué)習(xí)聚合行為（如股票市場趨勢）DS）或為個人和群體人口統(tǒng)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(biāo)，以及高效地呈現(xiàn)復(fù)雜信息。請注意，對于直接應(yīng)用于機器學(xué)習(xí)或其他人工智能技術(shù)的新任務(wù)或新領(lǐng)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(jié)果的重要性和分析、某些方法為何比這些領(lǐng)域中的其他方法更有效的解釋，或其他相關(guān)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.