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IFIP Working Group 1.02
Descriptional Complexity

Charter of the IFIP Working Group 1.02

Aims

Descriptional complexity has historically been a multidisciplinary area of study, with contributions from automata theory, computational complexity, cryptography, information theory, probability, statistics, pattern recognition, machine learning, computational learning theory, computer vision, neural networks, formal languages and other fields. The aims of the working group are therefore:

1. To promote research in all aspects of descriptional complexity through conferences, publications, and more informal means of scientific interaction such as electronic news groups;
2. To promote interaction and the exchange of information across traditional discipline boundaries;
3. To provide a point of contact for all researchers in all disciplines interested in descriptional complexity and its applications.

Scope

The scope of the working group encompasses all aspects of descriptional complexity, both theory and application. These aspects include but are not limited to:

1. Algorithmic and other descriptional theories of randomness;
2. The use of descriptional randomness and associated descriptional complexity measures in computational complexity, cryptography, information theory, probability, and statistics;
3. The minimum description-length principle, stochastic complexity, algorithmic probability, and other descriptional complexity measures related to inductive inference and prediction;
4. The use of such descriptional complexity measures in statistical inference, pattern recognition, machine learning, computational learning theory, computer vision, and neural networks;
5. Generalized descriptional complexity measures and their properties, including resource-bounded complexity, structural complexity, hierarchical complexity, and the complexity of sets, languages, grammars, automata, etc.;
6. Program complexity and reliability of software.