Overview

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.

Please see the Aims and Scope to learn about the focal topics in Computational Intelligence.

Aims and Scope

FOCAL TOPICS OF COMPUTATIONAL INTELLIGENCE

Discovery 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.