Varol Akman

The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing's ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the 'other minds' problem, and similar topics in philosophy of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic. (This paper was reprinted in: The Turing Test: The Elusive Standard of Artificial Intelligence, J. H. Moor, ed., Kluwer Academic, pp. 23-78, 2003.)

Papers in this special issue were written upon invitation. They were then subjected to the usual refereeing process of the Journal of Pragmatics. While we have attempted to cover almost all important areas in which context is employed as a conceptual apparatus, our coverage is clearly limited in scope. Accordingly, instead of a general updated overview of the use of context in every conceivable specific field (let's say the state-of-the-art of interdisciplinary research on context: a colossal/impossible enterprise!), we will offer the readers of this special issue an introduction to the problems involved in the study of context by way of eight papers encompassing eight different areas. These are (in alphabetical order): artificial intelligence, bilingualism, child development, cognitive science, conversation analysis, neuroscience, philosophy of language, and pragmatics. Our goal is to propose a multi-faceted view of context, and to stimulate further investigations of this fertile topic, which permeates our lives.

In traditional linguistic accounts of context, one thinks of the immediate features of a speech situation, that is, a situation in which an expression is uttered. Thus, features such as time, location, speaker, hearer and preceding discourse are all parts of context. But context is a wider and more transcendental notion than what these accounts imply. For one thing, context is a relational concept relating social actions and their surroundings, relating social actions, relating individual actors and their surroundings, and relating the set of individual actors and their social actions to their surroundings.

Sir John Lyons’s Linguistic Semantics: An Introduction (Cambridge, UK: Cambridge University Press, 1995) is a tolerable addition to the list of half a dozen or so impressive titles he has produced on linguistic subjects over the years. This book was initially planned to be a second edition of his Language, Meaning and Context (Lyons, 1981). However, in the end it turned out to be a successor and replacement. For it is, in the author’s words, a very different book compared to the 1981 volume: it is much longer, treats topics missing in the earlier volume, and is written in a different style.

"Language has never been accessible to me in the way that it was for Sachs. I'm shut off from my own thoughts, trapped in a no-man's-land between feeling and articulation, and no matter how hard I try to express myself, I can rarely come up with more than a confused stammer. Sachs never had any of these difficulties. Words and things matched up for him, whereas for me they are constantly breaking apart, flying off in a hundred different directions. I spend most of my time picking up the pieces and gluing them back together, but Sachs never had to stumble around like that, hunting through garbage dumps and trash bins, wondering if he hadn't fit the wrong pieces next to each other. His uncertainties were of a different order, but no matter how hard life became for him in other ways, words were never his problem." -Paul Auster, Leviathan

Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta (Larkin 1983). We give several examples showing the power of physical representations.

The work described in this report is motivated by the desire to test the expressive possibilities of action language C+. The Causal Calculator (CCalc) is a system that answers queries about action domains described in a fragment of that language. The Zoo World and the Traffic World have been proposed by Erik Sandewall in his Logic Modelling Workshop—an environment for communicating axiomatizations of action domains of nontrivial size. The Zoo World consists of several cages and the exterior, gates between them, and animals of several species, including humans. Actions in this domain include moving within and between cages, opening and closing gates, and mounting and riding animals. The Traffic World includes vehicles moving continuously between road crossings subject to a number of restrictions, such as speed limits and keeping a fixed safety distance away from other vehicles on the road. We show how to represent the two domains in the input language of CCalc, and how to use CCalc to test these representations.

P.F. Strawson proposed in the early seventies a threefold distinction regarding how context bears on the meaning of "what is said" when a sentence is uttered. The proposal was somewhat tentative and, being aware of this aspect, Strawson himself raised various questions to make it more adequate. In this paper, we review Strawson's scheme, note his concerns, and add some of our own. We also defend its essence and recommend it as an insightful entry point re the interplay of intended meaning and context.

The success of set theory as a foundation for mathematics inspires its use in artificial intelligence, particularly in commonsense reasoning. In this survey, we briefly review classical set theory from an AI perspective, and then consider alternative set theories. Desirable properties of a possible commonsense set theory are investigated, treating different aspects like cumulative hierarchy, self-reference, cardinality, etc. Assorted examples from the ground-breaking research on the subject are also given.

Strawson proposed in the early seventies an attractive threefold distinction regarding how context bears on the meaning of 'what is said' when a sentence is uttered. The proposed scheme is somewhat crude and, being aware of this aspect, Strawson himself raised various points to make it more adequate. In this paper, we review the scheme of Strawson, note his concerns, and add some of our own. However, our main point is to defend the essence of Strawson's approach and to recommend it as a starting point for research into intended meaning and context.

In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples.

Deflationism, one of the influential philosophical doctrines of truth, holds that there is no property of truth, and that overt uses of the predicate "true" are redundant. However, the hypothetical examples used by theorists to exemplify deflationism are isolated sentences, offering little to examine what the predicate adds to meaning within context. We oppose the theory not on philosophical but on empirical grounds. We collect 7,610 occurrences of "it is true that" from 10 influential periodicals published in the United States. We classify and annotate these with respect to the positions of coordinating and subordinating conjunctions that they contain. This way we investigate the contextual relationships between the proposition following "it is true that" with its surroundings. Overall, 34 different syntactical patterns are encountered. In some occurrences of "true", the predicate acts in the same manner as a performative verb does. These occurrences, having been observed in linguistically reliable media, constitute pragmatic counter-examples to deflationism.

The notion of context arises in assorted areas of artificial intelligence (AI), including knowledge representation, natural language processing, intelligent information retrieval, etc. Although the term ‘context’ is frequently employed in descriptions, explanations, and analyses of computer programs in these areas, its meaning is frequently left to the reader’s understanding. My aim in this paper is to offer a swift review of context in AI. I will first identify the role of context in various fields of AI. I will then consider the enterprise of formalising context. The focus of my discussion will be McCarthy's (1993) proposal – a landmark study that inspired almost every logical theory of context since then – and its close variants, studied by his students. (The author is grateful to Prof. Carlo Penco for his invaluable assistance with this work.)

This paper argues that in addition to the familiar approach using formal contexts, there is now a need in artificial intelligence to study contexts as social constructs. As a successful example of the latter approach, I draw attention to 'interpretation' (in the sense of literary theory), viz. the reconstruction of the intended meaning of a literary text that takes into account the context in which the author assumed the reader would place the text. An important contribution here comes from Wendell Harris, enumerating the seven crucial dimensions of context: knowledge of reality, knowledge of language, and the authorial, generic, collective, specific, and textual dimensions. Finally, two recent approaches to interpretation, due to Jon Barwise and Jerry Hobbs, are analyzed as useful attempts which also come to grips with the notion of context. It must be noted that there has been a considerable body of contributions connecting linguistic structure with social context. For example, anthropological linguistics, from Bronislaw Malinowski onwards, has underlined the cultural context of discourse as essential to meaning. This viewpoint became prominent with the emergence of the ethnography of speaking in anthropology. Thus, conversation analysis represents a consistent formal effort to contribute to an analysis of the nature of context. While this paper emphasizes and reviews the literary theory approach, it makes various contacts with works of the latter kind (e.g., the landmark contributions of Erving Goffman, John Gumperz, William Hanks, John Heritage, Dell Hymes, et al.) in order to deliver a more balanced and complete study of the dimensions of context.