William J. Rapaport

H´ector-Neri Casta˜neda-Calder´on (December 13, 1924–September 7, 1991) was born in San Vicente Zacapa, Guatemala. He attended the Normal School for Boys in Guatemala City, later called the Military Normal School for Boys, from which he was expelled for refusing to fight a bully; the dramatic story, worthy of being filmed, is told in the “De Re” section of his autobiography, “Self-Profile” (1986). He then attended a normal school in Costa Rica, followed by studies in philosophy at the University of San Carlos, Guatemala. He won a scholarship to the University of Minnesota, where he received his B.A. (1950), M.A. (1952), and Ph.D. (1954), all in philosophy. His dissertation, “The Logical Structure of Moral Reasoning”, was written under the direction of Wilfrid Sellars. He returned to teach in Guatemala, and then received a scholarship to study at Oxford University (1955–1956), after which he took a sabbatical-replacement position in philosophy at Duke University (1956). His first full-time academic appointment was at Wayne State University (1957– 1969), where he founded the philosophy journal Noˆus (1967, a counter-offer made to him by Wayne State to encourage him to stay there rather than to take the chairmanship of philosophy at the University of Pennsylvania). In 1969, he moved (along with several of his Wayne colleagues) to Indiana University, where he eventually became the Mahlon Powell Professor of Philosophy and, later, its first Dean of Latino Affairs (1978–1981). He remained at Indiana until his death. He was also a visiting professor of philosophy at the University of Texas at Austin (1962–1963) and a fellow at the Center for Advanced Study in the Behavioral Sciences (1981–1982). He received grants and fellowships from the Guggenheim Foundation (1967–1968), the T. Andrew Mellon Foundation, the National Endowment for the Humanities, and the National Science Foundation. He was elected President of the American Philosophical Association Central Division (1979– 1980), named to the American Academy of Arts and Sciences (1990), and received the Presidential Medal of Honor from the Government of Guatemala (1991). Casta˜neda’s philosophical interests spanned virtually the entire spectrum of philosophy, and his theories form a highly interconnected whole..

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

In the Fall of 1983, I offered a junior/senior-level course in Philosophy of Artificial Intelligence, in the Department of Philosophy at SUNY Fredonia, after returning there from a year’s leave to study and do research in computer science and artificial intelligence (AI) at SUNY Buffalo. Of the 30 students enrolled, most were computerscience majors, about a third had no computer background, and only a handful had studied any philosophy. (I might note that enrollments have subsequently increased in the Philosophy Department’s AI-related courses, such as logic, philosophy of mind, and epistemology, and that several computer science students have added philosophy as a second major.) This article describes that course, provides material for use in such a course, and offers a bibliography of relevant articles in the AI, cognitive science, and philosophical literature.

This essay considers what it means to understand natural language and whether a computer running an artificial-intelligence program designed to understand natural language does in fact do so. It is argued that a certain kind of semantics is needed to understand natural language, that this kind of semantics is mere symbol manipulation (i.e., syntax), and that, hence, it is available to AI systems. Recent arguments by Searle and Dretske to the effect that computers cannot understand natural language are discussed, and a prototype natural-language-understanding system is presented as an illustration.

It is a matter of fact—and has been so for a considerable amount of time—that philosophy is taught at the pre—college level. However, to teach philosophy at that (or at any) level is one thing; to teach it well is quite another. Fortunately, it can be taught well, as a host of successful experiences and programs have shown. But in what ways can it be taught? Are there differences in the ways in which it can or should be taught at the pre-college level from the ways in which it is taught in college? Are there differences in the ways in which it can or should be taught at the elementary-school level from ways in which it can or should be taught at the secondary-school level? There are other questions, of a similar nature, that the beginning college-level teacher of philosophy might ask: “I have never taught Introduction to Philosophy before; how should I go about it?” And there is a further question: Should it be taught at all? This question can, of course, be raised at any educational level, but it is especially acute at the elementary level.

This essay presents a philosophical and computational theory of the representation of de re, de dicto, nested, and quasi-indexical belief reports expressed in natural language. The propositional Semantic Network Processing System (SNePS) is used for representing and reasoning about these reports. In particular, quasi-indicators (indexical expressions occurring in intentional contexts and representing uses of indicators by another speaker) pose problems for natural-language representation and reasoning systems, because--unlike pure indicators--they cannot be replaced by coreferential NPs without changing the meaning of the embedding sentence. Therefore, the referent of the quasi-indicator must be represented in such a way that no invalid coreferential 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 handled by a single representational technique using belief spaces containing intensional entities. Inference rules and belief-revision techniques for the system are also examined.

SNePS, the Semantic Network Processing System 45, 54], 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 SNePS-based \knowledge base" would ultimatelybe built, not by a programmeror knowledge engineer entering representations of knowledge in some formallanguage or data entry system, but by a human informing it using a natural language (NL) (generally supposed to be English), or by the system reading books or articles that had been prepared for human readers. Because of this motivation, the criteria for the development of SNePS have included: it should be able to represent anything and everything expressible in NL; it should be able to represent generic, as well as speci c information; it should be able to use the generic and the speci c information to reason and infer information implied by what it has been told; it cannot count on any particular order among the pieces of information it is given; it must continue to act reasonably even if the information it is given includes circular de nitions, recursive rules, and inconsistent information.

This essay presents and defends a triage theory of grading: An item to be graded should get full credit if and only if it is clearly or substantially correct, minimal credit if and only if it is clearly or substantially incorrect, and partial credit if and only if it is neither of the above; no other (intermediate) grades should be given. Details on how to implement this are provided, and further issues in the philosophy of grading (reasons for and against grading, grading on a curve, and the subjectivity of grading) are discussed

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

“Contextual” vocabulary acquisition is the active, deliberate acquisition of a meaning for a word in a text by reasoning from textual clues and prior knowledge, including language knowledge and hypotheses developed from prior encounters with the word, but without external sources of help such as dictionaries or people. But what is “context”? Is it just the surrounding text? Does it include the reader’s background knowledge? I argue that the appropriate context for contextual vocabulary acquisition is the reader’s “internalization” of the text “integrated” into the reader’s “prior” knowledge via belief revision.

Terence Parsons's informal theory of intentional objects, their properties, and modes of predication does not adequately reflect ordinary ways of speaking and thinking. Meinongian theories recognizing two modes of predication are defended against Parsons's theory of two kinds of properties. Against Parsons's theory of fictional objects, I argue that no existing entities appear in works of fiction. A formal version of Parsons's theory is presented, and a curious consequence about modes of predication is indicated.

Hauser argues that his pocket calculator (Cal) has certain arithmetical abilities: it seems Cal calculates. That calculating is thinking seems equally untendentious. Yet these two claims together provide premises for a seemingly valid syllogism whose conclusion - Cal thinks - most would deny. He considers several ways to avoid this conclusion, and finds them mostly wanting. Either we ourselves can't be said to think or calculate if our calculation-like performances are judged by the standards proposed to rule out Cal; or the standards- e.g., autonomy and self-consciousness- make it impossible to verify whether anything or anyone (save oneself) meets them. While appeals to the intentionality of thought or the unity of minds provide more credible lines of resistance, available accounts of intentionality and mental unity are insufficiently clear and warranted to provide very substantial arguments against Cal's title to be called a thinking thing. Indeed, considerations favoring granting that title are more formidable than is generally appreciated. Rapaport's comments suggest that, on a strong view of thinking, mere calculating is not thinking (and pocket calculators don't think), but on a weak, but unexciting, sense of thinking, pocket calculators do think. He closes with some observations on the implications of this conclusion.

Philosophy has been characterized (e.g., by Benson Mates) as a field whose problems are unsolvable. This has often been taken to mean that there can be no progress in philosophy as there is in mathematics or science. The nature of problems and solutions is considered, and it is argued that solutions are always parts of theories, hence that acceptance of a solution requires commitment to a theory (as suggested by William Perry's scheme of cognitive development). Progress can be had in philosophy in the same way as in mathematics and science by knowing what commitments are needed for solutions. Similar views of Rescher and Castañeda are discussed.

Deliberate contextual vocabulary acquisition (CVA) is a reader’s ability to figure out a (not the) meaning for an unknown word from its “context”, without external sources of help such as dictionaries or people. The appropriate context for such CVA is the “belief-revised integration” of the reader’s prior knowledge with the reader’s “internalization” of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new classroom curriculum designed to help students use CVA to improve their reading comprehension.

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.

Everyone has a different "learning style". (A good introduction to the topic of learning styles is Claxton & Murrell 1987. For more on different learning styles, see Keirsey Temperament and Character Web Site, William Perry's Scheme of Intellectual and Ethical Development, Holland 1966, Kolb 1984, Sternberg 1999. For an interesting discussion of some limitations of learning styles from the perspective of teaching styles, see Glenn 2009/2010.) For some online tools targeted at different learning styles, see "100 Helpful Web Tools for Every Kind of Learner".

We discuss a research project that develops and applies algorithms for computational contextual vocabulary acquisition (CVA): learning the meaning of unknown words from context. We try to unify a disparate literature on the topic of CVA from psychology, first- and secondlanguage acquisition, and reading science, in order to help develop these algorithms: We use the knowledge gained from the computational CVA system to build an educational curriculum for enhancing students’ abilities to use CVA strategies in their reading of science texts at the middle-school and college undergraduate levels. The knowledge gained from case studies of students using our CVA techniques feeds back into further development of our computational theory. Keywords: artificial intelligence, knowledge representation, reading, reasoning, science education, vocabulary acquisition.