1. A Computational Semiotics Approach for Soft Computing Ricardo R. Gudwin Fernando A.C. Gomide DCA-FEEC-UNICAMP

2. Introduction Computational Intelligence and Soft Computing –model intelligent behavior using ideas from biology and the definition and use of uncertainty –fuzzy systems –neural networks –evolutive systems Hybrid Models –neuro-fuzzy –neuro-genetic –fuzzy-genetic

3. Introduction Computational Semiotics –Emulation of the process of Semiosis in a computer system –Mathematically define concepts from semiotics in order to be used in a computer system –Object (agent)-oriented structure –Meta-theoretical tool designed to formalize intelligent systems –Unify the representations used to formalize the different behaviors found within soft computing

4. Fundamental Transformations Argumentative knowledge –arguments –knowledge of transforming knowledge Three main arguments –knowledge extraction (deduction) –knowledge generation (induction) –knowledge selection (abduction) Selection and Internal Functions in an active object Building blocks for intelligent systems (soft computing)

5. Conclusions Computational Semiotics –aiming at an unified formal model for soft computing –extending soft computing through hybrid systems –focus on the knowledge process embedded in each soft computing technique (fuzzy, neural, genetic) Use of deductive, inductive and abductive arguments to build intelligent behavior Formal model easily converted into a computational algorithm General enough to accommodate specific details of each soft computing technique Do not compete with the current developments for each technique