# Abstract

What is computation with information described in natural language? Here are simple examples. I am planning to drive from Berkeley to Santa Barbara, with stopover for lunch in Monterey. It is about 10 am. It will probably take me about two hours to get to Monterey and about an hour to have lunch. From Monterey, it will probably take me about five hours to get to Santa Barbara. What is the probability that I will arrive in Santa Barbara before about six pm? Another simple example: A box contains about twenty balls of various sizes. Most are large. What is the number of small balls? What is the probability that a ball drawn at random is neither small nor large? Another example: A function, f, from reals to reals is described as: If X is small then Y is small; if X is medium then Y is large; if X is large then Y is small. What is the maximum of f? Another example: Usually the temperature is not very low, and usually the temperature is not very high. What is the average temperature? Another example: Usually most United Airlines flights from San Francisco leave on time. What is the probability that my flight will be delayed?

Computation with information described in natural language, or NL-computation for short, is a problem of intrinsic importance because much of human knowledge is described in natural language. It is safe to predict that as we move further into the age of machine intelligence and mechanized decision-making, NL-computation will grow in visibility and importance.

Computation with information described in natural language cannot be dealt with through the use of machinery of natural language processing. The problem is semantic imprecision of natural languages. More specifically, a natural language is basically a system for describing perceptions. Perceptions are intrinsically imprecise, reflecting the bounded ability of sensory organs, and ultimately the brain, to resolve detail and store information. Semantic imprecision of natural languages is a concomitant of imprecision of perceptions.

The generalized-constraint-based computational approach to NL-computation opens the door to a wide-ranging enlargement of the role of natural languages in scientific theories. Particularly important application areas are decision-making with information described in natural language, economics, risk assessment, qualitative systems analysis, search, question-answering and theories of evidence.

# Biography

Prof. Lofti Zadeh is the acknowledged father of fuzzy logic. He has been on the faculty of Electrical Engineering departments at Columbia University and at the University of California, Berkeley. He is now a Professor Emeritus and Director of the U.C. Berkeley's Initiative on Soft Computing.

He has won numerous awards including a Honorary Doctorate from Paul-Sabatier in 1986, Japan's Honda Award in 1989, the IEEE Education Medal in 1973, the IEEE Centennial Medal in 1984, and the IEEE Richard W. Hamming Medal in 1992.