The semantic component of the speech understanding system being developed jointly by SRI and SDC rules out phrase combinations that are not meaningful and produces semantic interpretations for combination that are.
Commonsense reasoning is "nonmonotonic" in the sense that we often draw, on the basis of partial information, conclusions that we later retract when we are given more complete information.
Native speakers of English show definite and consistent preferences for certain readings of syntactically ambiguous sentences. A user of a natural-language-processing system would naturally expect it to reflect the same preferences.
Current shape-from-shading and shape-from-texture methods are applicable only to smooth surfaces, while real surfaces are often rough and crumpled. To extend such methods to real surfaces we must have a model that also applies to rough surfaces.
We review previous efforts to recover surface shape from image irradiance in order to assess what can and cannot be accomplished. We consider the informational requirements and restrictions of these approaches
Simpson's paradox exemplifies a class of problems that can arise when the logic used to reason about the semantics of propositional sentences does not adequately capture certain dependencies between sentences of interest.
A method of using photographic film and pin-hole optical wiring is proposed here that seems particularly suited for simulating an electronic data processing machine having many elements operating in parallel.
