William J. Rapaport

The SNePS Family

Abstract SNePS, the Semantic Network Processing System, is an intensional propositional semantic network that has been designed to be the mind of a computational cognitive agent. In this article, the main features of SNePS are sketched, its antecedents are discussed, and some example current uses are described.

Logical foundations for belief representation

This essay presents a phi l osophi cal ond computotionol theory of the represento-tion of de re, de dlcto, nested, and quasi-indexical belief reports expressed i n natural language. The propositional Semantic Network Processing System (SNePS) is used for representing ond reasoning about these reports. In particular, quasi-indicators (indexical expressions occurring i n intentional contexts and representing uses of indicators by another speaker) pose problems far natural-language representation and reasoning systems, because-unl i ke pure indicators-they cannot be repl aced by careferential NPs without changing the meaning of the embedding sentence. Therefore, the referent of the quasi-indicator must be represented i n such a way that no invalid careferential claims are entailed. The importance of quasi-indicators is discussed, and it is shown that all four of the above categories of belief reports can be handl ed by a single representational technique using belief spaces containing intensional entities. Inference rules ond belief-revision techniques for the system ore also examined. This essay presents a computational analysis of a referential mechanism-quasi-indexicality-first examined in philosophy some 20 years ago, but not hitherto employed in artificial intelligence (AI) studies of belief systems. In turn, a philosophical claim about the relations of de re, de ditto, and de se beliefs is made as a by-product of the computational analysis. I thus hope to illustrate the importance of philosophy for research in AI and the correla-tive importance of a knowledge of AI for philosophical research, in the spirit of Dennetts (1978) recommendations:

Syntactic Semantics: Foundations of Computational Natural-Language Understanding

In this essay, I consider how it is possible to understand natural language and whether a computer could do so. Briefly, my argument will be that although a certain kind of semantic interpretation is needed for understanding natural language, it is a kind that only involves syntactic symbol manipulation of precisely the sort of which computers are capable, so that it is possible in principle for computers to understand natural language. Along the way, I shall discuss recent arguments by John R. Searle and by Fred Dretske to the effect that computers can not understand natural language, and I shall present a prototype natural-language-understanding system to illustrate some of my claims.1

What Did You Mean by That? Misunderstanding, Negotiation, and Syntactic Semantics

Syntactic semantics is a holistic, conceptual-role-semantic theory of how computers can think. But Fodor and Lepore have mounted a sustained attack on holistic semantic theories. However, their major problem with holism (that, if holism is true, then no two people can understand each other) can be fixed by means of negotiating meanings. Syntactic semantics and Fodor and Lepore’s objections to holism are outlined; the nature of communication, miscommunication, and negotiation is discussed; Bruner’s ideas about the negotiation of meaning are explored; and some observations on a problem for knowledge representation in AI raised by Winston are presented.

Searle's Experiments with Thought

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How minds can be computational systems

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test-passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably be persons.

Quasi‐Indexicals and Knowledge Reports

We present a computational analysis of de re, de ditto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Castaiteda, namely, that the simple rule ‘(A knows that P) implies P’ apparently does not hold If P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P, including those containing quasi-indexicals. In so doing, we explore the difference between reasoning in a public communication language and in a knowledge-representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (e.g., written narrative) in order to fully capture certain features of their semantics.

How to Pass a Turing Test

I advocate a theory of “syntactic semantics” as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, considered as the study of relations between symbols and meanings, can be turned into syntax – a study of relations among symbols (including meanings) – and hence syntax (i.e., symbol manipulation) can suffice for the semantical enterprise (contra Searle). (2) Semantics, considered as the process of understanding one domain (by modeling it) in terms of another, can be viewed recursively: The base case of semantic understanding –understanding a domain in terms of itself – is “syntactic understanding.” (3) An internal (or “narrow”), first-person point of view makes an external (or “wide”), third-person point of view otiose for purposes of understanding cognition.

Implementation is semantic interpretation: further thoughts

This essay explores the implications of the thesis that implementation is semantic interpretation. Implementation is (at least) a ternary relation: I is an implementation of an ‘Abstraction’ A in some medium M. Examples are presented from the arts, from language, from computer science and from cognitive science, where both brains and computers can be understood as implementing a ‘mind Abstraction’. Implementations have side effects due to the implementing medium; these can account for several puzzles surrounding qualia. Finally, an argument for benign panpsychism is developed.

How Helen Keller used syntactic semantics to escape from a Chinese Room

A computer can come to understand natural language the same way Helen Keller did: by using “syntactic semantics”—a theory of how syntax can suffice for semantics, i.e., how semantics for natural language can be provided by means of computational symbol manipulation. This essay considers real-life approximations of Chinese Rooms, focusing on Helen Keller’s experiences growing up deaf and blind, locked in a sort of Chinese Room yet learning how to communicate with the outside world. Using the SNePS computational knowledge-representation system, the essay analyzes Keller’s belief that learning that “everything has a name” was the key to her success, enabling her to “partition” her mental concepts into mental representations of: words, objects, and the naming relations between them. It next looks at Herbert Terrace’s theory of naming, which is akin to Keller’s, and which only humans are supposed to be capable of. The essay suggests that computers at least, and perhaps non-human primates, are also capable of this kind of naming.