Nowadays, it is widely recognized that rough sets applications have a great importance in several fields, such as granular computing, data mining and approximate reasoning. This is witnessed by the increasing number of papers and conferences about rough sets and by the implementation of real-life applications based on rough sets.
This fact leads to a continuous and useful development of applicative aspects of rough sets methodologies. Even though this attention to application is of great importance, it is not excluded that theoretical aspects concerning with foundations of rough sets, both logical and mathematical, must be taken into account.
To present the state-of-the-art scientific results and to start a fruitful discussion about rough sets from the theoretical standpoint, the first workshop on rough sets theory will be held in Milano, Italy, May 25-27, 2009 following the special sessions on Rough sets foundations organized at JRS07 and RSKT08. Great importance will be dedicated to discussion and included in the workshop there will be a round table about foundational and open problems in rough sets theory.

The topics of main interest (but not limited to) are:

  • Algebraic structures in rough set theory
  • Rough Logics
  • Topological and modal approach to roughness
  • Granular information systems
  • Rough mereology
  • Generalization of rough set theory: near sets and nearness approximation spaces
  • Category theoretic and philosophical aspects of rough set theory
  • Theoretical aspects of rough entropies
  • Complexity of rough set-related optimization/decision problems
  • Relationship with other paradigms (formal concept analysis, fuzzy sets, Atanassov's IFS,...)
  • Rough sets in approximate reasoning (judgment) in distributed environments (e.g., in multiagent systems) including rough sets and interactive computations
  • Rough sets in reasoning about changes
  • Rough sets in approximate reasoning in dialog of systems with human experts
  • Rough sets and ontology approximation
  • Rough sets and natural language understanding

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