TC 12 - Artificial Intelligence - Aims and
Scopes
est. 1989,
revised 1991, 2004
AIMS
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.
SCOPE
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:
Automated Reasoning
Belief Revision
Case-Based Reasoning
Computer Vision
Constraint Satisfaction
Data Mining
Evolutionary Algorithms
Intelligent Agents
Intelligent Planning and Scheduling
Intelligent Robotics
Knowledge Acquisition
Knowledge Discovery and Data Mining
Knowledge Engineering
Knowledge-Based Systems
Knowledge Management
Knowledge Representation and Reasoning
Machine Learning
Machine Translation
Model-based Reasoning
Natural Language Processing
Neural Nets
Pattern Recognition
Qualitative Reasoning
Search
Semantic Web
Temporal Reasoning
WG12.1
- Knowledge Representation and Reasoning
est. 2004
AIM
To study and
develop theory and techniques for knowledge representation and reasoning.
SCOPE
The scope of the Working Group's activities includes (but is not restricted
to) the following:
Abductive Reasoning
Inductive Reasoning
Non-monotonic Reasoning
Reasoning about Actions and Change
Spatial Reasoning
Temporal Reasoning
Automated Reasoning
Computational Logic
Logic Programming
Situation Calculus
Production Systems
Semantic Networks
Frames
Object-orientated Representation
Bayesian Networks
WG12.2
- Machine Learning and Data Mining
est. 2003, revised 2005
AIM
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.
SCOPE
Concept Learning and Inductive Learning
Association Rules
Case-based Learning
Artificial Neural Networks
Bayesian Learning
Uncertainty Learning
Reinforcement Learning
Evolutionary Learning
Perceptual Learning
Computational Learning Theory
Population-based Learning
Data Mining
Application Case Study
WG12.3
- Intelligent Agents
est. 2003
AIM
To study and
develop theory and techniques for intelligent agents.
SCOPE
Theory and agent modeling
Agent architectures
Agent-based software engineering
Coordinating, cooperation and negotiation
Evolution, adaptation and learning
Multiple agents
Mobile agents
Agent-based grid computing
Agent-based applications
WG12.4 -
(joint with WG2.12, see TC2)
WG12.5 - Artificial Intelligence Applications
est. 1993, rev.
2003
AIM
To explore the
use of Artificial Intelligence techniques for applications development.
SCOPE
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.
WG12.6
- Knowledge Management
est.
1993, revised 2003, 2008
AIMS
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.
SCOPE
Methodology,
technologies, processes, and systems for supporting all aspects of knowledge
management as communication, collaboration, learning, innovation, decision
making, investigation, embedding and archiving.
Knowledge
thinking.
Knowledge
Holonomy – the interplay between individual, organizational, enterprise
and society levels. Cross organisational.
Technology
trends include:
Intelligent multimodal knowledge acquisition and retrieval
Knowledge discovery
Technology for sustainable development
Convergence of intelligences
Technology for Knowledge Innovation
Human machine interaction and collaboration
Virtual reality and Games for KM
WG12.7
– Social Networking Semantics and Collective Intelligence
est. 2010
AIMS
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).
SCOPE
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.
WG12.9
– Computational Intelligence
est. 2011
AIMS
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