Aims and Scope
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
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.