In this special issue of Minds and Machines ("Situations and Artificial Intelligence") we take a close look at recent situation-theoretic research which has mostly originated within a philosophical framework but promises to have strong connotations for Artificial Intelligence workers. The seven papers which make up this special issue (three of the papers appear in Minds and Machines 9(1)) demonstrate the advantages of the situation-based approach towards problems with a definite AI flavor.

This book describes the mathematical aspects of the semantics of programming languages. The main goals are to provide formal tools to assess the meaning of programming constructs in both a language-independent and a machine-independent way, and to prove properties about programs, such as whether they terminate, or whether their result is a solution of the problem they are supposed to solve. In order to achieve this the authors first present, in an elementary and unified way, the theory of certain topological (...) spaces that have proved of use in the modelling of various families of typed lambda calculi considered as core programming languages and as meta-languages for denotational semantics. This theory is now known as Domain Theory, and was founded as a subject by Scott and Plotkin. One of the main concerns is to establish links between mathematical structures and more syntactic approaches to semantics, often referred to as operational semantics, which is also described. This dual approach has the double advantage of motivating computer scientists to do some mathematics and of interesting mathematicians in unfamiliar application areas from computer science. (shrink)

This is how guidance of behavior to goal grounds and explains cognition and the main forms in which it manages information.

This chapter provides the teleological foundations for our analysis of guidance to goal. Its objective is to ground goal-directedness genetically. The basic suggestion is this. Organisms are small things, with few energy resources and puny physical means, battling a ruthless physical and biological nature. How do they manage to survive and multiply? CLEVERLY, BY ORGANIZING.

In this paper I introduce a formalism for natural language understandingbased on a computational implementation of Discourse RepresentationTheory. The formalism covers a wide variety of semantic phenomena(including scope and lexical ambiguities, anaphora and presupposition),is computationally attractive, and has a genuine inference component. Itcombines a well-established linguistic formalism (DRT) with advancedtechniques to deal with ambiguity (underspecification), and isinnovative in the use of first-order theorem proving techniques.The architecture of the formalism for natural language understandingthat I advocate consists of three levels of processing:underspecification, (...) resolution, andinference. Each of these levels has a distinct function andtherefore employs a different kind of semantic representation. Themappings between these different representations define the interfacesbetween the levels. (shrink)

This paper is a revised and extended version of a keynote contribution to a recent conference on Cognitive Informatics. It offers a brief summary of some of the core concerns of other contributions to the conference, highlighting the range of issues under discussion; and argues that many of the central concepts and preoccupations of cognitive informatics as understood by participants--and others in the general field of computation--rely on ill-founded realist assumptions, and what has been termed the functionalist view of representation. (...) Even if such ideas--albeit in a revised form -- can be defended, there must be a more extensive engagement with the literature and issues outside the confines of the computing and computational orthodoxy. (shrink)

The brain is often taken to be a paradigmatic example of a signaling system with semantic and representational properties, in which neurons are senders and receivers of information carried in action potentials. A closer look at this picture shows that it is not as appealing as it might initially seem in explaining the function of the brain. Working from several sender-receiver models within the teleosemantic framework, I will first argue that two requirements must be met for a system to support (...) genuine semantic information: 1. The receiver must be competent —that is, it must be able to extract rewards from its environment on the basis of the signals that it receives. 2. The receiver must have some flexibility of response relative to the signal received. In the second part of the paper, this initial framework will be applied to neural processes, pointing to the surprising conclusion that signaling at the single-neuron level is only weakly semantic at best. Contrary to received views, neurons will have little or no access to semantic information (though their patterns of activity may carry plenty of quantitative, correlational information) about the world outside the organism. Genuine representation of the world requires an organism - level receiver of semantic information, to which any particular set of neurons makes only a small contribution. (shrink)

Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures—Soar and ACT-R, to name the most prominent—do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes in cognitive (...) architectures. We discuss the degree to which DRS, our earlier proposal for such an internal representation for diagrams, meets these requirements. In DRS, the diagrams are not raw images, but a composition of objects that can be individuated and thus symbolized, while, unlike traditional symbols, the referent of the symbol is an object that retains its perceptual essence, namely, its spatiality. This duality provides a way to resolve what anti-imagists thought was a contradiction in mental imagery: the compositionality of mental images that seemed to be unique to symbol systems, and their support of a perceptual experience of images and some types of perception on them. We briefly review the use of DRS to augment Soar and ACT-R with a diagrammatic representation component. We identify issues for further research. (shrink)

Three different ways to understand the representational content of the feature maps employed in early vision are compared. First is Stephen Kosslyn's claim, entered as part of the debate over mental imagery, that such areas support "depictive" representation, and that visual perception uses them as depictive representations. Reasons are given to doubt this view. Second, an improved version of what I call "feature-placing" is described and advanced. Third, feature-placing is contrasted with the notion that the representational content of those feature (...) maps could be conveyed in a list of sentences about visual objects. Some problems with this last alternative are described. (shrink)

We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.

In 1991, I included a brief discussion of the Baldwin effect in my account of the evolution of human consciousness, thinking I was introducing to non-specialist readers a little-appreciated, but no longer controversial, wrinkle in orthodox neo-Darwinism. I had thought that Hinton and Nowlan (1987) and Maynard Smith (1987) had shown clearly and succinctly how and why it worked, and restored the neglected concept to grace. Here is how I put it then.

Advocates of dynamic systems have suggested that higher mental processes are based on continuous representations. In order to evaluate this claim, we first define the concept of representation, and rigorously distinguish between discrete representations and continuous representations. We also explore two important bases of representational content. Then, we present seven arguments that discrete representations are necessary for any system that must discriminate between two or more states. It follows that higher mental processes require discrete representations. We also argue that discrete (...) representations are more influenced by conceptual role than continuous representations. We end by arguing that the presence of discrete representations in cognitive systems entails that computationalism (i.e., the view that the mind is a computational device) is true, and that cognitive science should embrace representational pluralism. (shrink)

The “surge in use of finite-state methods” ([10]) in computational linguistics has largely, if not completely, left semantics untouched. The present paper is directed towards correcting this situation. Techniques explained in [1] are applied to a fragment of temporal semantics through an approach we call finite-state temporality. This proceeds from the intuition of an event as “a series of snapshots” ([15]; see also [12]), equating snapshots with symbols that collectively form our alphabet. A sequence of snapshots then becomes a string (...) over that alphabet, evoking comic/film strips. Jackendoff has, among others, objected to conceptualizing events in terms of snapshots ([8]). To counter these objections, we step up from events-as-strings to event-typesas-regular languages ([5, 6]), recognizing the need for variable granularity. Beyond the introduction of disjunction implicit in the step from a single string up to a set of strings, we obtain a useful logic from the regular operations and a careful choice of the snapshots (constituting our alphabet). (shrink)

The processing of sequences of (English) sentences is analyzedcompositionally through transitions that merge sentences, rather thandecomposing them. Transitions that are in a precise senseinertial are related to disjunctive and non-deterministic approaches toambiguity. Modal interpretations are investigated, inducing variousequivalences on sequences.

This commentary connects some of Glenberg's ideas to similar ideas from artificial intelligence. Second, it briefly discusses hidden assumptions relating to meaning, representations, and projectable properties. Finally, questions about mechanisms, mental imagery, and conceptualization in animals are posed.

Standard models of cognition are built from abstract, amodal, arbitrary symbols, and the meanings of those symbols are given solely by their interrelations. The target article (Glenberg 1997t) argues that these models must be inadequate because meaning cannot arise from relations among abstract symbols. For cognitive representations to be meaningful they must, at the least, be grounded; but abstract symbols are difficult, if not impossible, to ground. As an alternative, the target article developed a framework in which representations are grounded (...) in perception and action, and hence are embodied. Recent work (Glenberg & Robertson 1999; 2000; Glenberg & Kaschak 2002; Kaschak & Glenberg 2000) extends this framework to language. (shrink)

I examine one of the conceptual cornerstones of the field known as computational neuroscience, especially as articulated in Churchland et al. (1990), an article that is arguably the locus classicus of this term and its meaning. The authors of that article try, but I claim ultimately fail, to mark off the enterprise of computational neuroscience as an interdisciplinary approach to understanding the cognitive, information-processing functions of the brain. The failure is a result of the fact that the authors provide no (...) principled means to distinguish the study of neural systems as genuinely computational/information-processing from the study of any complex causal process. I then argue for two things. First, that in order to appropriately mark off computational neuroscience, one must be able to assign a semantics to the states over which an attempt to provide a computational explanation is made. Second, I show that neither of the two most popular ways of trying to effect such content assignation -- informational semantics and 'biosemantics' -- can make the required distinction, at least not in a way that a computational neuroscientist should be happy about. The moral of the story as I take it is not a negative one to the effect that computational neuroscience is in principle incapable of doing what it wants to do. Rather, it is to point out some work that remains to be done. (shrink)

Darwin differs from Newton and Einstein in that his ideas do not require a complicated or deep mind to understand them, and perhaps did not even require such a mind in order to generate them in the first place. It can be explained to any school-child (as Newtonian mechanics and Einsteinian relativity cannot) that living creatures are just Darwinian survival/reproduction machines. They have whatever structure they have through a combination of chance and its consequences: Chance causes changes in the genetic (...) blueprint from which organisms' bodies are built, and if those changes are more successful in helping their owners survive and reproduce than their predecessors or their rivals, then, by definition, those changes are reproduced, and thereby become more prevalent in succeeding generations: Whatever survives/reproduces better survives/reproduces better. That is the tautological force that shaped us. (shrink)

What is the relation between computation and intennonality? Cognition presup- poses intentionality (or semantics). This much is certain. So, if, according to com- putationalism, cognition is computation, then computation, mo, presupposes..

This paper analyses the impact of representation and search operators on Computational Complexity. A model of computation is introduced based on a directed graph, and representation and search are defined to be the vertices and edges of this graph respectively. Changing either the representation or the search algorithm leads to different possible complexity classes. The final section explores the role of representation in reducing time complexity in Artificial Intelligence.

One of the major divergences between dynamical systems theory and symbolism lies in their views on the role of representation in cognition. From the perspective of development, the cognitive development could be divided into three levels: sensorimotor, imagery representation and linguistic representation. It is claimed that representation is not a sufficient condition though it is necessary for cognition. However, it does not mean that the authors agree with the notion of strong coupling in dynamicism that completely rejects representation.

The paper gives a survey of known results related to computational devices (finite and push–down automata) recognizing monadic generalized quantifiers in finite models. Some of these results are simple reinterpretations of descriptive—feasible correspondence theorems from finite–model theory. Additionally a new result characterizing monadic quantifiers recognized by push down automata is proven.

According to an influential view in contemporary cognitive science, many human cognitive capacities are innate. The primary support for this view comes from ‘poverty of stimulus’ arguments. In general outline, such arguments contrast the meagre informational input to cognitive development with its rich informational output. Consider the ease with which humans acquire languages, become facile at attributing psychological states (‘folk psychology’), gain knowledge of biological kinds (‘folk biology’), or come to understand basic physical processes (‘folk physics’). In all these cases, (...) the evidence available to a growing child is far too thin and noisy for it to be plausible that the underlying principles involved are derived from general learning mechanisms. This only alternative hypothesis seems to be that the child’s grasp of these principles is innate. (Cf. Laurence and Margolis, 2001.). (shrink)

One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car’s successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that (...) the car constitutes a refutation of semantic arguments against the possibility of strong artificial intelligence. (shrink)

Computational semantics is the study of how to represent meaning in a way that computers can use. For the authors of this textbook, this study includes the representation of the meaning of natural language in logic formalisms, the recognition of certain relations that hold within this formalization (such as synonymy, consistency, and implication), and the computational implementation of all this. I think that, while there probably are not many courses devoted to computational semantics, this book could profitably be incorporated into (...) more traditional computational linguistics courses, especially when two courses are offered serially. The material here could be spread out and integrated into parts of a more standard pair of these courses, and it would result in a substantial widening of the knowledge that students come away with from these courses. (shrink)

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. (shrink)

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. (shrink)

What is the computational notion of "implementation"? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation. The online version differs from the published version in being a bit longer and going into a bit more detail.

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)

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)

Fodor advocates a view of cognitive processes as computations defined over the language of thought (or Mentalese). Even among those who endorse Mentalese, considerable controversy surrounds its representational format. What semantically relevant structure should scientific psychology attribute to Mentalese symbols? Researchers commonly emphasize logical structure, akin to that displayed by predicate calculus sentences. To counteract this tendency, I discuss computational models of navigation drawn from probabilistic robotics. These models involve computations defined over cognitive maps, which have geometric rather than logical (...) structure. They thereby demonstrate the possibility of rational cognitive processes in an exclusively non-logical representational medium. Furthermore, they offer much promise for the empirical study of animal navigation. (shrink)

Detractors of Searle’s Chinese Room Argument have arrived at a virtual consensus that the mental properties of the Man performing the computations stipulated by the argument are irrelevant to whether computational cognitive science is true. This paper challenges this virtual consensus to argue for the first of the two main theses of the persons reply, namely, that the mental properties of the Man are what matter. It does this by challenging many of the arguments and conceptions put forth by the (...) systems and logical replies to the Chinese Room, either reducing them to absurdity or showing how they lead, on the contrary, to conclusions the persons reply endorses. The paper integrates thinking about the Chinese Room Argument with additional philosophical considerations, the foundations of the theory of computation, and theoretical and experimental psychology. This integration purports to show how all these dimensions tend to support the proposed thesis of the persons reply. (shrink)

Both formal semantics and cognitive semantics are the source of important insights about language. By developing precise statements of the rules of meaning in fragmentary, abstract languages, formalists have been able to offer perspicuous accounts of how we might come to know such rules and use them to communicate with others. Conversely, by charting the overall landscape of interpretations, cognitivists have documented how closely interpretations draw on the commonsense knowledge that lets us make our way in the world. There is (...) no opposition between these insights. Sooner or later we will have a semantics that responds to both. However, developing such a semantics is profoundly difficult, because there are certain tensions to be overcome in reconciling the two perspectives. For one thing, the overall landscape of meaning does seem to be characterized by a much richer ontology and more dynamic categories than are exhibited by the fragments typically studied in the formal tradition. One sign of strain is the recent tendency to talk of “procedural”, “non-compositional”, or “computational” semantics, as in Hamm, Kamp and van Lambalgen 2006, hereafter HK&vL. We think such locutions can serve as useful reminders to keep semantics fixed on the central question of how language allows us to share information that some have and others need to get. However, there is some danger that formalists will merely by put off by an idea that, taken literally, may not be such a good one. In this short article, we want to explore and defend the traditional realist view attributed by HK&vL to Lewis among others. In fact, this view offers a well-developed, extremely straightforward and robust account of the relation between semantics and cognition. Moreover, while the realist view has ways of accommodating the representationalist insights of DRT (Lewis 1979; Thomason 1990; Stalnaker 1998), it remains unclear how “computational” semantics can account for the key data for the realist view: cases where we judge interlocutors to be ignorant about aspects of meaning in their native language (Kripke 1972; Putnam 1975; Stalnaker 1979; Williamson 1994).. (shrink)

We compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and pushdown automata is psychologically relevant. Our research improves upon hypothesis and explanatory power of recent neuroimaging studies as well as provides evidence for the claim that human linguistic abilities are constrained by computational complexity.

We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality.
In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves upon hypothesis and (...) explanatory power of recent neuroimaging studies as well as provides
evidence. (shrink)

Situation theory has been developed over the last decade and various versions of the theory have been applied to a number of linguistic issues. However, not much work has been done in regard to its computational aspects. In this paper, we review the existing approaches towards `computational situation theory' with considerable emphasis on our own research.

Just as war can be viewed as continuation of diplomacy using other means, computational semantics is continuation of logical analysis of natural language by other means. For a long time, the tool of choice for this used to be Prolog. In our recent textbook we argue (and try to demonstrate by example) that lazy functional programming is a more appropriate tool. In the talk we will lay out a program for computational semantics, by linking computational semantics to the general analysis (...) of procedures for social interaction. The talk will give examples of how Haskell can be used to begin carrying out this program. (shrink)

Almost forty years ago Richard Montague proposed to analyse natural language with the same tools as formal languages. In particular, he gave formal semantic analyses of several interesting fragments of English in terms of typed logic. This led to the development of Montague grammar as a particular style of formal analysis of natural language.

An emerging standard for polymorphically typed, lazy, purely functional programming is Haskell, a language named after Haskell Curry. Haskell is based on (polymorphically typed) lambda calculus, which makes it an excellent tool for computational semantics.

We explicate representational content by addressing how representations that ex- plain intelligent behavior might be acquired through processes of Darwinian evo- lution. We present the results of computer simulations of evolved neural network controllers and discuss the similarity of the simulations to real-world examples of neural network control of animal behavior. We argue that focusing on the simplest cases of evolved intelligent behavior, in both simulated and real organisms, reveals that evolved representations must carry information about the creature’s environ- ments (...) and further can do so only if their neural states are appropriately isomor- phic to environmental states. Further, these informational and isomorphism rela- tions are what are tracked by content attributions in folk-psychological and cognitive scientific explanations of these intelligent behaviors. (shrink)

This paper continues a strain of intellectual complaint against the presumptions of certain kinds of formal semantics (the qualification is important) and their bad effects on those areas of artificial intelligence concerned with machine understanding of human language. After some discussion of the use of the term epistemology in artificial intelligence, the paper takes as a case study the various positions held by McDermott on these issues and concludes, reluctantly, that, although he has reversed himself on the issue, there was (...) no time at which he was right. (shrink)