est. 1989, revised 1991, 2004
To foster the development and understanding of Artificial Intelligence and its applications worldwide.
To promote interdisciplinary exchanges between Artificial Intelligence and other fields of information processing.
To contribute to the overall aims and objectives and further development of IFIP as the international body for Information Processing.
Artificial Intelligence covers a wide range of techniques, which can be applied to a very wide range of application areas. Its subfields include (but are not restricted to) the following:
Intelligent Planning and Scheduling
Knowledge Discovery and Data Mining
Knowledge Representation and Reasoning
Natural Language Processing
- Knowledge Representation and Reasoning
To study and
develop theory and techniques for knowledge representation and reasoning.
The scope of the Working Group's activities includes (but is not restricted to) the following:
Reasoning about Actions and Change
- Machine Learning and Data Mining
est. 2003, revised 2005
To explore computer methodology and algorithms that improve automatically through experience. Applications range from data mining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests.
Concept Learning and Inductive Learning
Artificial Neural Networks
Computational Learning Theory
Application Case Study
- Intelligent Agents
To study and develop theory and techniques for intelligent agents.
Theory and agent modeling
Agent-based software engineering
Coordinating, cooperation and negotiation
Evolution, adaptation and learning
Agent-based grid computing
WG12.4 - (joint with WG2.12, see TC2)
WG12.5 - Artificial Intelligence Applications
est. 1993, rev. 2003
To explore the use of Artificial Intelligence techniques for applications development.
All areas of application in which Artificial Intelligence techniques can give benefits to users.
Techniques for application development including:
Conceptual frameworks for application specification and design
User interface design
Integration of AI software and systems with conventional databases, programming languages, and operating systems
Related research issues such as knowledge acquisition, learning, validation and implementation techniques.
- Knowledge Management
est. 1993, revised 2003, 2008
To develop advanced methods for organizing, accessing and exploiting multidisciplinary knowledge within organizations and enterprises.
To bring together various areas of KM research and technology to meet this challenge, e.g. knowledge transfer and modeling, optimisation, natural language understanding, speech and image processing and understanding, reasoning methods, learning methods, communication methods, social aspects, complex problem solving, decision support, human-machine interaction, serious games.
To develop technology for intelligent support of Knowledge Cultivators, e.g. intelligent knowledge navigation systems, multi modal interface, automatic translation, competency management, e- and m-activities such as learning, collaborative research and design, business, process control.
To share worldwide experience in the above domains.
Methodology, technologies, processes, and systems for supporting all aspects of knowledge management as communication, collaboration, learning, innovation, decision making, investigation, embedding and archiving.
Knowledge Holonomy – the interplay between individual, organizational, enterprise and society levels. Cross organisational.
Technology trends include:
Intelligent multimodal knowledge acquisition and retrieval
Technology for sustainable development
Convergence of intelligences
Technology for Knowledge Innovation
Human machine interaction and collaboration
Virtual reality and Games for KM
To become a multidisciplinary group that searches for and studies the theoretical foundations, new paradigms, methodologies and technologies needed for the specific support by intelligent computer systems of the knowledge aspects of social processes, community-based elicitation and specification of semantics, and the use of such knowledge e.g. as linked data in applications;
To investigate and promote the applications of such systems in science, industry, and society at large, including opportunities for standardisation;
To meet and communicate regularly, to endorse and create scientific forums of exchange in order to achieve these aims;
To interact productively with selected other working groups and research projects within and outside of IFIP, in particular but not limited to TC2 (Software Theory and Practice), TC5 (Information Technology Applications), TC8 (Information Systems) and other Working Groups of TC12 (Artificial Intelligence).
An initial but not comprehensive list of topics of study includes
theory, formal models, e.g. ontologies, and emerging new paradigms of organized and informal communities, of social and collaborative processes, and of semantics of data and knowledge;
elicitation of ontologies and semantic content creation in general by social processes, expertise sharing and agreement; methodologies for same;
auto-emergence of social semantics; harvesting and mining collective intelligence from community interactions; pragmatic web;
engineering and prototyping of supporting knowledge-based systems for collective intelligence;
collective intelligence in linked data; evolution and quality assurance of such linked data;
the interaction of formal semantics with informal social semantics; social web interoperability issues; modeling of situational awareness; hybrid socio-technical systems;
identity and authentication of entities and services on the (social) semantic web; related issues of trust, privacy and security;
implementation and exploitation of social semantics as web services; self-organizing services tailored to communities; methodologies for adoption of such services;
scalability issues for web-sized collective intelligence;
interoperability of heterogeneous and autonomous knowledge sources from multiple disciplines through their respective communities.
To obtain a deeper understanding of Computational Intelligence and its Applications and help in the development of its theoretical foundations and technological underpinnings.
1) Novel concepts of computational Intelligence
approaches and their adaptation for handling real world applications.
2) Investigation of techniques of modification of computational Intelligence approaches so as to produce more effective computational Intelligence approaches.
3) Enhancement of the computational Intelligence approaches by co-operating with classical or statistical methods.
4) Using computational Intelligence approaches for handling constrained, multi-objective and large scale optimization problems for real world applications.
5) Application of computational Intelligence approaches in real industrial applications.
6) Parallel computational Intelligence approaches for practical applications in real world.
7) Using computational Intelligence approaches for solving dynamic optimization or time-varying problems in real world.
8) The following computational intelligence approaches include, but are not limited to:
- Neural Networks
- Fuzzy Systems
- Evolutionary Computation
- Particle swarm optimization
- Multi-agent systems
- Intelligent control systems
- Support vector machine
- Bayesian networks
- Global and constrained optimization