Many people have noted the similarities between case theories in linguistics and frame representations in artificial intelligence. In particular, the cases of a verb seem to correspond to the slots of a frame. This has led many people, including Fillmore [1977] and Winston [1977] to assert that cases and slots are one and the same. This hypothesis has not attracted much attention, probably because the notion of “slot” in frame representations is so underconstrained that the theory would seem to be (...) content free. In this note, we wish to show that this is not true. For one thing, the case‐slot identity theory can explain the difficulty linguists have had in deciding how many cases there are. Furthermore, its adoption will place certain interesting, and plausible constraints on the frame and case theories compatible with it. (shrink)

This paper presents a “frame” representation for common sense knowledge and uses it to formalize our knowledge of “mundane” painting (walls; not portraits). These frames. while designed to aid a computer program to understand stories about the painting process, should be of use to programs which attempt to actually carry out the activity. The paper stresses a “deep” understanding of the activity so that the representation indicates not only what steps to carry out, but also how to do them, and (...) why they should be done. To accomplish this, while at the same time preserving modularity and nonredundancy, a system of interframe pointers is introduced (the COMES‐FROM and LEADS‐TO pointers) which explain how or why something is done in tens of knowledge given in other frames. The paper proceeds by steadily deepening an initial English‐like description of the activity, and a context free grammar for the representation is included. (shrink)

L'informatique se définissant comme le traitement rationnel de l'information par machine automatique et l'intelligence se caractérisant par une même capacité de traitement rationnel, il était inévitable que l'on songe à associer l'intelligence au traitement automatique de l'information. C'est ce qu'a fait John McCarthy en forgeant le terme d'intelligence artificielle. Par «intelligence artificielle» on peut vouloir exprimer l'ambition de1. Recréer, transformer ou développer l'intelligence artificiellement2. Simuler l'intelligence en la reconstituant dans des modéles imitant certains aspects de notre intelligence dite naturelle.

We describe a knowledge representation and inference formalism, based on an intensional propositional semantic network, in which variables are structures terms consisting of quantifier, type, and other information. This has three important consequences for natural language processing. First, this leads to an extended, more natural formalism whose use and representations are consistent with the use of variables in natural language in two ways: the structure of representations mirrors the structure of the language and allows re-use phenomena such as pronouns and (...) ellipsis. Second, the formalism allows the specification of description subsumption as a partial ordering on related concepts (variable nodes in a semantic network) that relates more general concepts to more specific instances of that concept, as is done in language. Finally, this structured variable representation simplifies the resolution of some representational difficulties with certain classes of natural language sentences, namely, donkey sentences and sentences involving branching quantifiers. The implementation of this formalism is called ANALOG (A NAtural LOGIC) and its utility for natural language processing tasks is illustrated. (shrink)

Although the connectionist approach has lead to elegant solutions to a number of problems in cognitive science and artificial intelligence, its suitability for dealing with problems in knowledge representation and inference has often been questioned. This paper partly answers this criticism by demonstrating that effective solutions to certain problems in knowledge representation and limited inference can be found by adopting a connectionist approach. The paper presents a connectionist realization of semantic networks, that is, it describes how knowledge about concepts, their (...) properties, and the hierarchical relationship between them may be encoded as an interpreter‐free massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast—in time proportional to the depth of the conceptual hierarchy. The connectionist realization is based on an evidential formulation that leads to principled solutions to the problems of exceptions and conflicting multiple inheritance situations during inheritance, and the best‐match or partial‐match computation during recognition. The paper also identifies constraints that must be satisfied by the conceptual structure in order to arrive at an efficient parallel realization. (shrink)