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Computation with Information Described in Natural Language — the Concept of Generalized-constraint-based Computation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zadeh, Lotfi A. |
| Copyright Year | 2006 |
| 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? Computation with information described in natural language, or NLcomputation 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, NLcomputation 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. Our approach to NL-computation centers on what is referred to as generalizedconstraint-based computation, or GC-computation for short. A generalized constraint is expressed as X isr R, where X is the constrained variable, R is a constraining relation and r is an indexical variable which defines the way in which R constrains X. The principal constraints are possibilistic, veristic, probabilistic, usuality, random set, fuzzy graph and group. Generalized constraints may be combined, qualified, propagated, and counter propagated, generating what is called the Generalized Constraint Language, GCL. The key underlying idea is that information conveyed by a proposition may be represented as a generalized constraint, that is, as an element of GCL. |
| Starting Page | 3 |
| Ending Page | 4 |
| Page Count | 2 |
| File Format | PDF HTM / HTML |
| DOI | 10.1142/9789812774118_0001 |
| Alternate Webpage(s) | http://www.scs-europe.net/services/ecms2006/ecms2006%20pdf/1-zadeh-inv.pdf |
| Alternate Webpage(s) | https://www.computer.org/csdl/proceedings/cimca/2005/2504/02/01631435.pdf |
| Alternate Webpage(s) | https://doi.org/10.1142/9789812774118_0001 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
