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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:
 To promote research in all aspects of descriptional complexity
through conferences, publications, and more informal means of
scientific interaction such as electronic news groups;
 To promote interaction and the exchange of information across
traditional discipline boundaries;
 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:
 Algorithmic and other descriptional theories of randomness;
 The use of descriptional randomness and associated
descriptional complexity measures in computational
complexity, cryptography, information theory, probability,
and statistics;
 The minimum descriptionlength principle, stochastic
complexity, algorithmic probability, and other descriptional
complexity measures related to inductive inference and
prediction;
 The use of such descriptional complexity measures in
statistical inference, pattern recognition, machine learning,
computational learning theory, computer vision, and neural
networks;
 Generalized descriptional complexity measures and their
properties, including resourcebounded complexity, structural
complexity, hierarchical complexity, and the complexity of
sets, languages, grammars, automata, etc.;
 Program complexity and reliability of software.
