1 / 5

A Computational Semiotics Approach for Soft Computing

A Computational Semiotics Approach for Soft Computing. Ricardo R. Gudwin Fernando A.C. Gomide DCA-FEEC-UNICAMP. Introduction. Computational Intelligence and Soft Computing model intelligent behavior using ideas from biology and the definition and use of uncertainty fuzzy systems

dthurston
Download Presentation

A Computational Semiotics Approach for Soft Computing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related