What is the computational notion of “implementation”? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation.

A critical study of John Searle's Minds, Brains and Science (Cambridge, MA: Harvard University Press, 1984).

The paper explores the distinction between two doctrines, both of which inform theory construction in much of modern cognitive psychology: the representational theory of mind and the computational theory of mind. According to the former, propositional attitudes are to be construed as relations that organisms bear to mental representations. According to the latter, mental processes have access only to formal (nonsemantic) properties of the mental representations over which they are defined.The following claims are defended: (1) That the traditional dispute between (...) “rational” and “naturalistic” psychology is plausibly viewed as an argument about the status of the computational theory of mind. Rational psychologists accept a formality condition on the specification of mental processes; naturalists do not. (2) That to accept the formality condition is to endorse a version of methodological solipsism. (3) That the acceptance of some such condition is warranted, at least for that part of psychology which concerns itself with theories of the mental causation of behavior. This is because: (4) such theories require nontransparent taxonomies of mental states; and (5) nontransparent taxonomies individuate mental states without reference to their semantic properties. Equivalently, (6) nontransparent taxonomies respect the way that the organism represents the object of its propositional attitudes toitself, and it is this representation which functions in the causation of behavior.The final section of the paper considers the prospect for a naturalistic psychology: one which defines its generalizations over relations between mental representations and their environmental causes, thus seeking to account for the semantic properties of propositional attitudes. Two related arguments are proposed, both leading to the conclusion that no such research strategy is likely to prove fruitful. (shrink)

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. (shrink)

The idea that human thinking and machine computing are "radically the same" provides the central theme for this marvelously lucid and witty book on...

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. (shrink)

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. (shrink)

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. (shrink)

A critique of several recent objections to John Searle's Chinese-Room Argument against the possibility of "strong AI" is presented. The objections are found to miss the point, and a stronger argument against Searle is presented, based on a distinction between "syntactic" and "semantic" understanding.

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...) the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?". (shrink)

Cognitive science normally takes the individual agent as its unit of analysis. In many human endeavors, however, the outcomes of interest are not determined entirely by the information processing properties of individuals. Nor can they be inferred from the properties of the individual agents, alone, no matter how detailed the knowledge of the properties of those individuals may be. In commercial aviation, for example, the successful completion of a flight is produced by a system that typically includes two or more (...) pilots interacting with each other and with a suite of technological devices. This article presents a theoretical framework that takes a distributed, socio‐technical system rather than an individual mind as its primary unit of analysis. This framework is explicitly cognitive in that it is concerned with how information is represented and how representations are transformed and propagated in the performance of tasks. An analysis of a memory task in the cockpit of a commercial airliner shows how the cognitive properties of such distributed systems can differ radically from the cognitive properties of the individuals who inhabit them. (shrink)

This presentation of a system of propositional logic is a foundational paper for systems of illocutionary logic. The language contains the illocutionary force operators '' for assertion and ' ' for supposition. Sentences occurring in proofs of the deductive system must be prefixed with one of these operators, and rules of take account of the forces of the sentences. Two kinds of semantic conditions are investigated; familiar truth conditions and commitment conditions. Accepting a statement A or rejecting A commits a (...) person to accepting some statements (the symbol '' marks this value), to rejecting some statements (), and will leave the person uncommitted with respect to others (). Commitment valuations assign the values to sentences of ; such a valuation is conceived as reflecting the beliefs/knowledge of a particular person. This paper explores the relations between truth conditions and commitment conditions, and between semantic concepts defined in terms of these conditions. (shrink)

The first of its kind to thoroughly cover language technology at all levels and with all modern technologies this book takes an empirical approach to the ...

Computationalism, the notion that cognition is computation, is a working hypothesis of many AI researchers and Cognitive Scientists. Although it has not been proved, neither has it been disproved. In this paper, I give some refutations to some well-known alleged refutations of computationalism. My arguments have two themes: people are more limited than is often recognized in these debates; computer systems are more complicated than is often recognized in these debates. To underline the latter point, I sketch the design and (...) abilities of a possible embodied computer system. (shrink)

The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not ordinarily require (...) the execution of mental algorithms, which appears to be at best no more than one rather special kind of thinking. (shrink)

Hutchins examines a set of phenomena that have fallen between the established disciplines of psychology and anthropology, bringing to light a new set of relationships between culture and cognition.

Natural languages differ from most formal languages in having a partial, rather than a total, semantic interpretation function; e.g., some noun phrases don't refer. The usual semantics for handling such noun phrases (e.g., Russell, Quine) require syntactic reform. The alternative presented here is semantic expansion, viz., enlarging the range of the interpretaion function to make it total. A specific ontology based on Meinong's Theory of Objects, which can serve as domain on interpretation, is suggested, and related to the work of (...) Castaneda, Frege, Katz and Fodor, Parsons, and Scott. (shrink)

Available for the first time in 20 years, here are two important works from the 1920s by the best-known representative of the Vienna Circle.

This essay re-examines Meinong's "Über Gegenstandstheorie" and undertakes a clarification and revision of it that is faithful to Meinong, overcomes the various objections to his theory, and is capable of offering solutions to various problems in philosophy of mind and philosophy of language. I then turn to a discussion of a historically and technically interesting Russell-style paradox (now known as "Clark's Paradox") that arises in the modified theory. I also examine the alternative Meinong-inspired theories of Hector-Neri Castañeda and Terence Parsons.

Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical underdetermination of the theory (...) by the data. Level t1 is clearly too underdetermined, T2 is vulnerable to a counterexample (Searle's Chinese Room Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3 is the appropriate target level for cognitive science. When it is reached, however, there will still remain more unanswerable questions than when Physics reaches its Grand Unified Theory of Everything (GUTE), because of the mind/body problem and the other-minds problem, both of which are inherent in this empirical domain, even though Turing hardly mentions them. (shrink)

Expanding on some of Chomsky’s recently expressed views of meaning in a way that is consistent with his long-held rationalist conception of mind, I show how syntax, broadly conceived, could individuate meanings and provide a science of meanings inside the head. Interpretation becomes a pragmatic matter, although a rationalist account of mind shows how internal meanings guide interpretation and, more generally, language use. In this view of meanings, interpretation, and mind, semantics as usually understood disappears.

We shall identify the subset of objective reality that all people experience during their everyday lives as the common-sense world. The most prominent occupants of this world are medium-sized physical things and people. However, the common-sense world is a rich and exciting domain that is also home to countless other kinds of entities. ;Common sense, then, is the sophisticated, true, and reliable set of fundamental beliefs and abilities shared by all human adults. This knowledge base underlies whatever other knowledge we (...) might obtain, and it allows us to provide expert explanations and predictions about the common-sense world. Like knowledge in general, common-sense knowledge may be either declarative or procedural . Since common sense includes both procedural and declarative knowledge, its justification is both intuitive and pragmatic. Young children acquire common sense through simple experience with the common-sense world, not by undertaking any formal instruction or systematic inquiry. ;Among common sense's most general claims is common-sense realism , a commitment to the objective existence of the common-sense world. The more specific theory of common-sense psychology explains the behavior and mental activity of people in terms of their beliefs and desires. Likewise, naive physics attempts to describe, explain, and predict the behavior of the purely physical parts of the common-sense world. ;Just as there are common-sense theories of psychology and physics, there is also a commonsensical theory of ethics. This important component of common sense is a prerequisite for more sophisticated moral philosophy. It is also a crucial component of common-sense psychology, since we presuppose it during our everyday interactions with others. (shrink)

states are to be understood in terms of their functional relationships to other mental states, not in terms of their material instantiation in any particular kind of hardware. But the argument that material instantiation is irrelevant to functional..

An integrated statement is made concerning the semantic status of nodes in a propositional semantic network, claiming that such nodes represent only intensions. Within the network, the only reference to extensionality is via a mechanism to assert that two intensions have the same extension in same world. This framework is employed in three application problems to illustrate the nature of its solutions.The formalism used here utilizes only assertional information and no structural, or definitional, information. This restriction corresponds to many of (...) the psychologically motivated network models. Some of the psychological implications of network processes called node merging and node splitting are discussed. Additionally, it is pointed out that both our networks and the psychologically based networks are prone to memory confusions about knowing unless augmented by domain‐specific inference processes, or by structural information. (shrink)

The informative, nontechnical chapters written especially for this book describe many of the areas of computer science critical to the design of intelligent ...

Offers a text of mathematical logic on a sophisticated level, presenting the reader with several of the most significant discoveries, including the independence of the continuum hypothesis, the Diophantine nature of enumerable sets and the impossibility of finding an algorithmic solution for certain problems.

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. (shrink)