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The aim of this chapter is to introduce a very simple computational theory of perceptions that resembles human estimation. I follow here Professor Zadeh's latest developments in soft computing, oriented towards Computational Theory of Perceptions [54], Percisiated Natural Language [56] and towards the Generalized Theory of Uncertainty [57]. I follow the approach of perceptional computation and continue my study of the semantics of fuzzy sets [23] but approach the problem from a different angle. In a previous paper [24], I introduced a pictorial language in connection with fuzzy logic, a language that defines human-like, multi-domain reasoning and that develops further the existing simple pictorial language, Bliss [2]. In graphical form, fuzzy sets increase the expressive power of the new language, which I call the Description Language of Meaning Articulation (DLMA). This paper introduces the computational theory behind the DLMA. The DLMA contains several Bliss sentences, called moves, which are used to show the right way of reasoning. With the Computational Theory of Meaning Articulation (CTMA), I seek to emulate human estimation and exploit traditional arithmetic for actual computation.The paper is divided into three parts. The first part explores the existing approaches and the latest developments in fuzzy arithmetic. The second part introduces the DLMA and CTMA. The third part provides examples of the CTMA. The chapter ends with a conclusion and suggestions for further research.

DOI: 10.1007/978-3-540-93802-6_6

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