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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.

We describe a knowledge representation and inference formalism, based on an intensional propositional semantic network, in which variables are structured 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...

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

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We present a formal description of a logical language that is based on a propositional semantic network. Variables in this language are not atomic and have potentially complex structure. We start from the individual components of a semantic network system, atomic nodes and relations that connect nodes, and provide a complete specification for the structure of nodes and a subsumption procedure between nodes. We differ from other work in subsumption in that the representation language is uniform and based on an extended first-order predicate logic. The language is particularly suitable for addressing some problems associated with natural language processing, namely the representation of complex natural language descriptions and inference associated with description subsumption. 1 Introduction We present a formal description of a propositional semantic-network-based knowledge representation system. Variables in this representation are not atomic and have potentially complex structure. We...

We present a formal description of a propositional semantic-network-based knowledge representation system. Variables in this representation are not atomic and have potentially complex structure. We start from the individual components of a semantic network system, atomics nodes and relations that connect nodes, and provide a complete specification for the structure of nodes and a subsumption procedure between nodes. We differ from other work in subsumption in that the representation language is uniform and based on a first-order predicate logic. There is no distinction between taxonomic and axiomatic knowledge. The utility of the subsumption procedure for processing natural language is illustrated. 1 Introduction Subsumption is a partial ordering on related concepts (say, nodes in a semantic network) that relates more general concepts to more specific instances of that concept. The manner in which "more general" is determined characterizes the type of subsumption (Woods characterizes ...

General knowledge is widely applicable, but relatively slow to apply to any particular situation. Specific knowledge can be used rapidly where it applies, but is only narrowly applicable. We present an automatic scheme to migrate general knowledge to specific knowledge during reasoning. This scheme relies on a nested rule representation which retains the rule builder's intentions about which of the possible specializations of the rule will be most useful. If both general and specific knowledge is available and applicable, a system maybeslowed down by trying to use the general knowledge as well as, or instead of, the specific knowledge. However, if general knowledge is purged from the system after migration, the system will lose the flexibility of being able to handle different situations. To retain the flexibility without paying the price in speed, a shadowing scheme is presented that prevents general knowledge from being used when specific knowledge migrated from it is available and applicable. The combination of knowledge migration and knowledge shadowing allows a deductive reasoning system to learn from and exploit previous experience. Experience is represented by the instance relationship between the general knowledge and the specific knowledge migrated from it. We also

SNePS, the Semantic Network Processing System, has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a "cognitive agent"). This paper expands on this motivation, discusses some of the system features that derived from this motivation, and presents four case studies of interactions with SNePS demonstrating some of these features. The features demonstrated in the case studies are: nonstandard connectives; the use of recursive rules; the Unique Variable Binding Rule, that says that two variables in a rule cannot be instantiated to the same term; and discussing sentences and propositions in natural language. 1 System Description SNePS, the Semantic Network Processing System [9, 15, 17], has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a "cognitive agent"). It has always been the intention that a SNePSbased "knowledge base" would ultimately be built, not by a programmer or k...

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 paper presents several aspects of such a logic. Keywords: Knowledge representation; reasoning; natural language; commonsense reasoning; logic. 1 Introduction My colleagues, students, and I have been engaged in a long-term project to build a natural language using intelligent agent. While our approach to natural language understanding (NLU) and commonsense reasoning (CSR) has been logic-based, we have thought that the logics developed for metamathematics are not, necessarily, the best ones for our purpose. Instead, we have designed new logics, better suited for NLU and CSR. The current version of these logics constitutes the formal language and inference mechanism of the knowledge repr...