Читать книгу Introduction to Fuzzy Logic - James K. Peckol - Страница 47

We now move from animals and humans to raise the question that has perplexed for eons. Can a computing machine be designed to think, to reason, to learn, to create, that is, to self‐modify? Can such machines learn and operate like a human being? To be able to design and implement such tools and machines, we must first fully understand their intended applications and what these terms actually mean in such contexts.

Throughout history, as an outgrowth of the work of researchers in both the information processing and the epistemological schools, many different models of human reasoning have been explored, proposed, and tried. In each instance, the hypothetical model tries to capture the dynamic nature, inexactness, or intuitive nature of the underlying process.

Frequently, the heuristic character of human reasoning is quantified numerically. This is seen in Lotfi Zadeh's characterization of notions such as young or old on a mathematical scale or Ted Shortliffe's measures of belief and disbelief. Herb Simon has countered that people do not reason numerically. Perhaps they do not. However, mathematics is a reasonable first‐order approach when attempting to capture the essence of the intuitive inexactness humans so readily accept but which computers have difficulty accommodating. Three or four simple words illustrate the essence of the two philosophies:

 |(yes, no) → (maybe) → (maybe not)| … |(crisp) → (fuzzy)|

Let's begin our study by looking at some of the early works. This work is rooted mainly in the studies, writings, and teachings of early Greek philosophers including Socrates, Plato, Aristotle, Parmenides, and Heraclitus. Philosophy in the early days often included mathematics and related reasoning.