this article.

This paper presents this algorithm, gives demonstrations of an implemented system, and describes the results of some preliminary empirical studies, which lend support to the algorithm

The use of logic for knowledge representation and reasoning systems is controversial. There are, indeed, several ways that standard First Order Predicate Logic is inappropriate for modelling natural language understanding and commonsense reasoning. However, a more appropriate logic can be designed. This chapter presents several aspects of such a logic.

this paper, I will discuss two aspects of SNePS propositional semantic networks [5, 8, 12, 17] that distinguish them as formalisms for the representation of knowledge---cables and paths. I will also discuss a kind of inference sanctioned by each one---reduction inference and path-based inference, respectively, and the integration of these two kinds of inference into a kind of "subconscious" reasoning. Informally, a semantic network is a labelled directed acyclic graph in which nodes represent entities and labelled arcs represent binary relations between entities. A propositional semantic network is a semantic network in which every proposition represented in the network is represented by a node, rather than by an arc. We will refer to a node that represents a proposition as a propositional node. Isolated nodes are not allowed in a semantic network, and since a semantic network is a variety of relational graph, it does not make sense to have two arcs with the same label emanate from the same node and terminate at the same node. However, there is no restriction forbidding several arcs with the same label from emanating from the same node if they terminate in different nodes. Informally, we will call a set of such arcs a cable. (We will formalize this below.) A propositional node, therefore, may have a set of cables emanating from it. Each cable represents an argument position of the proposition represented by the propositional node, the label

The propositional content of a reference is the proposition attributing to the referent the properties that correspond to the nouns and modifiers in the reference (for example, the propositional content of `Mary' is that the referent is named `Mary'). During language comprehension, the hearer or reader must determine the set of beliefs with respect to which the propositional content of a reference is to be understood. In the prototypical case, this set consists of the propositions that she believes that the speaker or writer believes that she and the speaker or writer mutually believe. This paper identifies two contexts in which the propositional content of a specific reference is not understood with respect to this set--- subjective and objective sentences in third-person fictional narrative text---and identifies some implications of this for understanding specific references in these contexts. 1 Introduction Specific references are references to particular entities, for example, `a...

William J. end Shapiro, Stuart C. (1984), "Quasi-lndexical Reference in Propositional Semantic Networks, " Proceedings of the loth International Conference on Computational Linguistics ( COLING-84 ; Stanford Univ.) (Morristown, NJ: Assoc. for Computational Linguistics): 65-70. Rapaport, William J. (1986), "Logical Foundations for Belief Representation," Cognitiv e Science 10: 371-422. Reiser, Brian J. (1981), "Character Tracking and the Understanding of Narrative," Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI-81; Van. couver) (Los Altos, CA: Morgen Kanhmmn): 209-211. Roach, Eleanor and Lloyd, B.B. (1978). Cognition and Categorization (Hillsdale, NJ: Lawrence Erlbaum Associ- ates). Shapiro, Smart C. (1979). "The SNePS Sementic Network Processing System," in N.V. Findlet (ed.), Associative Network. v (New York: Academic): 179-203. Shapiro, Stuart C. end Rapaport, William J. (1987). "SNePS Considered as a Fully Intensional Propositional S

Abstract not found

It is sometimes necessary to reason non-monotonically, to withdraw previously held beliefs. Default rules and defeasible reasoning have been frequently used to handle such situations. Some years ago, J. Terry Nutter proposed a form of defeasible reasoning involving additional truthvalues that would avoid non-monotonicity in many situations. We adopt and implement much of Nutter's underlying concept, but use a structure of case frames in our knowledge representation to avoid the need for more than the standard truth values. Our implementation captures most of the advantages of the original proposal and in some cases allows more flexibility.