Humanlike computational learning theory. A computational semiotics perspective

Abstract

Focussing on the development of smart interfaces for smart environments one has to meet the challenge that these interfaces need a kind of intelligence which resembles human intelligence sufficiently. Part of this humanlike intelligence is the ability to learn from an environment those properties which are relevant for human persons and to be able to communicate symbolically. This paper describes a possible synthesis of two main semiotic paradigms and how this synthesis can be applied to the problem of a symbolically communicating humanlike intelligence.