logica de lo preconciente aportes a la primera topica - Asociación ...

Pablo D. Slemenson“Lógica de lo preconciente”What is remarkable is that humans are capable of performing a wide variety of taskswithout any measurements and any computations. A familiar example is the task ofparking a car. For a human it is an easy task so long as the final position of the car isnot specified precisely. In performing this and similar tasks, humans employ theirability to exploit the tolerance for imprecision to achieve tractability, robustness, andlow solution cost. What is important to recognize is that this essential ability is closelylinked to the modality of granulation and, more particularly, to information granulation.In a very broad sense, granulation involves partitioning of whole into parts. In morespecific terms, granulation involves partitioning a physical or mental object into acollection of granules, with a granule being a clump of objects (points) drawn togetherby indistinguishability, similarity, proximity, or functionality. Granulation may bephysical or mental; dense or sparse; and crisp or fuzzy, depending on whether theboundaries of granules are or are not sharply defined. Modes of informationgranulation (IG) in which granules are crisp play important roles in a wide variety ofmethods, approaches, and techniques. Among them are: interval analysis, quantization,chunking, rough set theory, diakoptics, divide and conquer, Dempster-Shafer theory,machine learning from examples, qualitative process theory, decision trees, semanticnetworks, analog-to-digital conversion, constraint programming, image segmentation,cluster analysis, and many others. Important though it is, crisp IG has a major blindspot. More specifically, it fails to reflect the fact that in much--perhaps most--of humanreasoning and concept formation the granules are fuzzy rather than crisp. For example,the fuzzy granules of a human head are the nose, ears, forehead, hair, cheeks, etc. Eachof the fuzzy granules is associated with a set of fuzzy attributes, e.g., in the case of hair,the fuzzy attributes are color, length, texture, etc. In turn, each of the fuzzy attributes isassociated with a set of fuzzy values. For example, in the case of the fuzzy attributeLength (hair), the fuzzy values are long, short, not very long, etc. The fuzziness ofgranules, their attributes, and their values is characteristic of the ways in which humanconcepts are formed, organized, and manipulated. In effect, fuzzy informationgranulation (fuzzy IG) may be viewed as a human way of employing data compressionfor reasoning and, more particularly, making rational decisions in an environment ofimprecision, uncertainty, and partial truth. In fuzzy logic, the machinery of fuzzyinformation granulation--based on the concepts of a linguistic variable, fuzzy if-thenrule, and fuzzy graph--has long played a key role in most of its applications. However,what is emerging now is a much more general theory of information granulation thatApendice 315