William J. Rapaport

For a formal language, one usually only considers semantic interpretations which are complete: for each singular referring expression in the language, there corresponds an element of the universe of discourse. However, natural languages only have a partial interpretation function when given such a set- or model-theoretic semantics whose universe of discourse (or "model") is taken to be the real, physical world. To put semantics on a par with syntax for parity of treatment of natural and formal languages, two alternatives suggest themselves. (1) The syntax of a natural language can be changed so that the interpretation function becomes total. What I urge, on the other hand, is (2) that we change the semantics of natural language, as outlined earlier, by enlarging the range of the interpretation function to make it total. I suggest making the world fit our language by adding Meinongian objects to it.

_A unique resource exploring the nature of computers and computing, and their relationships to the world._ _Philosophy of Computer Science_ is a university-level textbook designed to guide readers through an array of topics at the intersection of philosophy and computer science. Accessible to students from either discipline, or complete beginners to both, the text brings readers up to speed on a conversation about these issues, so that they can read the literature for themselves, form their own reasoned opinions, and become part of the conversation by contributing their own views. Written by a highly qualified author in the field, the book looks at some of the central questions in the philosophy of computer science, including: What is philosophy? (for readers who might be unfamiliar with it) What is computer science and its relationship to science and to engineering? What are computers, computing, algorithms, and programs?(Includes a line-by-line reading of portions of Turing’s classic 1936 paper that introduced Turing Machines, as well as discussion of the Church-Turing Computability Thesis and hypercomputation challenges to it) How do computers and computation relate to the physical world? What is artificial intelligence, and should we build AIs? Should we trust decisions made by computers? A companion website contains annotated suggestions for further reading and an instructor’s manual. _Philosophy of Computer Science_ is a must-have for philosophy students, computer scientists, and general readers who want to think philosophically about computer science.

Deliberate contextual vocabulary acquisition (CVA) is a reader’s ability to figure out a meaning for an unknown word from its “context.” without external sources of help. 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 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.

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.

I survey a common theme that pervades the philosophy of computer science (and philosophy more generally): the relation of computing to the world. Are algorithms merely certain procedures entirely characterizable in an “indigenous,” “internal,’ “intrinsic,” “local,” “narrow,” “syntactic” (more generally: “intra-system”), purely-Turing-machine language? Or must algorithms interact with the real world, having a purpose that is expressible only in a language with an “external,” “extrinsic,” “global,” “wide,” “inherited” (more generally: “extra” or “inter-“system) semantics?

In their Why Machines Will Never Rule the World, Landgrebe and Smith (2023) argue that it is impossible for artificial general intelligence (AGI) to succeed, on the grounds that it is impossible to perfectly model or emulate the “complex” “human neurocognitive system”. However, they do not show that it is logically impossible; they only show that it is practically impossible using current mathematical techniques. Nor do they prove that there could not be any other kinds of theories than those in current use. Even if perfect theories were impossible or unlikely, perfection may not be needed and may even be unhelpful.

Narrative passages told from a character's perspective convey the character's thoughts and perceptions. We present a discourse process that recognizes characters' thoughts and perceptions in third-person narrative. An effect of perspective on reference In narrative is addressed: references in passages told from the perspective of a character reflect the character's beliefs. An algorithm that uses the results of our discourse process to understand references with respect to an appropriate set of beliefs is presented.

This essay continues my investigation of `syntactic semantics': the theory that, pace Searle's Chinese-Room Argument, syntax does suffice for semantics (in particular, for the semantics needed for a computational cognitive theory of natural-language understanding). Here, I argue that syntactic semantics (which is internal and first-person) is what has been called a conceptual-role semantics: The meaning of any expression is the role that it plays in the complete system of expressions. Such a `narrow', conceptual-role semantics is the appropriate sort of semantics to account (from an `internal', or first-person perspective) for how a cognitive agent understands language. Some have argued for the primacy of external, or `wide', semantics, while others have argued for a two-factor analysis. But, although two factors can be specified–-one internal and first-person, the other only specifiable in an external, third-person way–-only the internal, first-person one is needed for understanding how someone understands. A truth-conditional semantics can still be provided, but only from a third-person perspective

This project continues our interdisciplinary research into computational and cognitive aspects of narrative comprehension. Our ultimate goal is the development of a computational theory of how humans understand narrative texts. The theory will be informed by joint research from the viewpoints of linguistics, cognitive psychology, the study of language acquisition, literary theory, geography, philosophy, and artificial intelligence. The linguists, literary theorists, and geographers in our group are developing theories of narrative language and spatial understanding that are being tested by the cognitive psychologists and language researchers in our group, and a computational model of a reader of narrative text is being developed by the AI researchers, based in part on these theories and results and in part on research on knowledge representation and reasoning. This proposal describes the knowledge-representation and natural-language-processing issues involved in the computational implementation of the theory; discusses a contrast between communicative and narrative uses of language and of the relation of the narrative text to the story world it describes; investigates linguistic, literary, and hermeneutic dimensions of our research; presents a computational investigation of subjective sentences and reference in narrative; studies children’s acquisition of the ability to take third-person perspective in their own storytelling; describes the psychological validation of various linguistic devices; and examines how readers develop an understanding of the geographical space of a story. This report is a longer version of a project description submitted to NSF. This document, produced in May 2007, is a L ATEX version of Technical Report 89-07 (Buffalo: SUNY Buffalo Department of Computer Science, August 1989), with slightly..

John Searle once said: "The Chinese room shows what we knew all along: syntax by itself is not sufficient for semantics. (Does anyone actually deny this point, I mean straight out? Is anyone actually willing to say, straight out, that they think that syntax, in the sense of formal symbols, is really the same as semantic content, in the sense of meanings, thought contents, understanding, etc.?)." I say: "Yes". Stuart C. Shapiro has said: "Does that make any sense? Yes: Everything makes sense. The question is: What sense does it make?" This essay explores what sense it makes to say that syntax by itself is sufficient for semantics.

For centuries, philosophers studying the great mysteries of human subjectivity have focused on the mind/body problem and the difference between human beings and animals. Now a new ontological question takes center stage: to what extent can a manufactured object (a computer) exhibit qualities of mind? There have been passionate exchanges between those who believe that a "manufactured mind" is possible and those who believe that mind cannot exist except as a living, socially situated, embodied person. As with earlier arguments, this one shows no sign of being resolved. But the fight over computationalism (the belief that all mental processes can be generated by computer programs) has immediate, "hard" consequences for technological research and development, social- and cognitive-science methodology, and for our everyday experience of the world and ourselves. This special issue consists of papers presented at a conference of the same title held at the Center for Cognitive Science at the University at Buffalo, 22-23 May 1990. The authors come from a variety of disciplinary backgrounds (Computer Science, Linguistics, Philosophy, Psychology, Sociology, etc.), and bring a wide variety of perspectives to the topic.

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary.

This paper describes the SNePS knowledge-representation and reasoning system. SNePS is an intensional, propositional, semantic-network processing system used for research in AI. We look at how predication is represented in such a system when it is used for cognitive modeling and natural-language understanding and generation. In particular, we discuss issues in the representation of fictional entities and the representation of propositions from fiction, using SNePS. We briefly survey four philosophical ontological theories of fiction and sketch an epistemological theory of fiction using a story operator and rules for allowing propositions to migrate into and out of story spaces

Contextual vocabulary acquisition (CVA) is the deliberate acquisition of a meaning for a word in a text by reasoning from context, where “context” includes: (1) the reader’s “internalization” of the surrounding text, i.e., the reader’s “mental model” of the word’s “textual context” (hereafter, “co-text” [3]) integrated with (2) the reader’s prior knowledge (PK), but it excludes (3) external sources such as dictionaries or people. CVA is what you do when you come across an unfamiliar word in your reading, realize that you don’t know what it means, decide that you need to know what it means in order to understand the passage, but there is no one around to ask, and it is not in the dictionary (or you are too lazy to look it up). In such a case, you can try to figure out its meaning “from context”, i.e., from clues in the co-text together with your prior knowledge. Our computational theory of CVA—implemented in a the SNePS knowledge representation and reasoning system [28]—begins with a stored knowledge base containing SNePS representations of relevant PK, inputs SNePS representations of a passage containing an unfamiliar word, and draws inferences from these two (integrated) information sources. When asked to define the word, definition algorithms deductively search the resulting network for information of the sort that might be found in a dictionary definition, outputting a definition frame whose slots are the kinds of features that a definition might contain (e.g., class membership, properties, actions, spatio-temporal information, etc.) and whose slot-fillers contain information gleaned from the network [6–8,20,23,24]. We are investigating ways to make our system more robust, to embed it in a naturallanguage-processing system, and to incorporate morphological information. Our research group, including reading educators, is also applying our methods to the develop-

Cognitive agents, whether human or computer, that engage in natural-language discourse and that have beliefs about the beliefs of other cognitive agents must be able to represent objects the way they believe them to be and the way they believe others believe them to be. They must be able to represent other cognitive agents both as objects of beliefs and as agents of beliefs. They must be able to represent their own beliefs, and they must be able to represent beliefs as objects of beliefs. These requirements raise questions about the number of tokens of the belief representation language needed to represent believers and propositions in their normal roles and in their roles as objects of beliefs. In this paper, we explicate the relations among nodes, mental tokens, concepts, actual objects, concepts in the belief spaces of an agent and the agent's model of other agents, concepts of other cognitive agents, and propositions. We extend, deepen, and clarify our theory of intensional knowledge representation for natural-language processing, as presented in previous papers and in light of objections raised by others. The essential claim is that tokens in a knowledge-representation system represent only intensions and not extensions. We are pursuing this investigation by building CASSIE, a computer model of a cognitive agent and, to the extent she works, a cognitive agent herself. CASSIE's mind is implemented in the SNePS knowledge-representation and reasoning system.