Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic (computational) model of the mind. Nonsymbolic modeling turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem. 1.

The ‘grounding problem ’ poses the question of how the function and internal mechanisms of a machine, natural or artificial, can be intrinsic to the machine itself, i.e. independent of an external designer or observer. Searle’s and Harnad’s analyses of the grounding problem are briefly reviewed as well as different approaches to solving it, based on the cognitivist and the enactive paradigms in cognitive science. It is argued that, although the two categories of grounding approaches differ in their nature and the problems they have to face, both, so far, fail to provide fully grounded systems for similar reasons: Only isolated parts of systems are grounded, whereas other, essential, parts are left ungrounded. Hence, it is further argued that grounding should instead be understood and approached as radical bottom-up development of complete robotic agents in interaction with their environment.

This work analyzes the central role of interpretation in non-routine design. Based on this analysis, a theory of computer support for interpretation in cooperative design is constructed. The theory is grounded in studies of design and interpretation. It is illustrated by mechanisms provided by a software substrate for computer-based design environments, applied to a sample task of lunar habitat design. Computer support of

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs ” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous Chinese Room argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital

The significance of any system of explicit representation depends not only on the immediate properties of its representational structures, but also on two aspects of the attendant circumstances: implicit relations among, and processes defined over, those individual representations, and larger circumstances in the world in which the whole representational system is embedded. This relativity of representation to circumstance facilitates local inference, and enables representation to connect with action, but it also limits expressive power, blocks generalisation, and inhibits communication. Thus there seems to be an inherent tension between the effectiveness of located action and the detachment of general-purpose reasoning. It is argued that various mechanisms of causally-connected self-reference enable a system to transcend the apparent tension, and partially escape the confines of circumstantial relativity. As well as examining self-reference in general, the paper shows how a variety of particular self-referential mechanisms-- autonymy, introspection, and reflection- provide the means to overcome specific kinds of implicit relativity. These mechanisms are based on distinct notions of self: self as unity, self as complex system, self as independent agent. Their power derives from their ability to render explicit what would otherwise be implicit, and implicit what would otherwise be explicit, all the while maintaining causal connection between the two. Without this causal connection, a system would either be inexorably parochial, or else remain entirely disconnected from its subject matter. When appropriately connected, however, a self-referential system can move plastically back and forth between local effectiveness and detached generality.

Introduction: Two dogmas of reificatory semantics What is meaning, what is it for a sign to be meaningful, how can meaning best be analyzed, and in what sense is linguistic meaning proper or unique to language? Cognitive linguistics offers answers to these questions that challenge two traditional dogmas of linguistic theory, philosophy of language and cognitive science. However, although they have notionally abandoned both these dogmas, many cognitive linguists retain an ambiguous loyalty to some of their underlying presuppositions. I hope to convince them of the necessity to review their deep theoretical commitments, in order to rebut, once and for all, the charge that cognitive semantics entails a Subjectivist theory of meaning. The two dogmas are: (1) the Dogma of the Autonomy of linguistic meaning; and (2) the Dogma of the Compositionality of linguistic meaning. Both these dogmas are variants of a more general, fatal misconception of the nature of linguistic meaning, namely that

We embodied networks of cultured biological neurons in simulation and in robotics. This is a new research paradigm to study learning, memory, and information processing in real time: the Neurally-Controlled Animat. Neural activity was subject to detailed electrical and optical observation using multi-electrode arrays and microscopy in order to access the neural correlates of animat behavior. Neurobiology has given inspiration to AI since the advent of the perceptron and consequent artificial neural networks, developed using local properties of individual neurons. We wish to continue this trend by studying the network processing of ensembles of living neurons that lead to higher-level cognition and intelligent behavior.

In recent years, AI research has started to take seriously the role of context in developing models. This trend is heralded by a departure from logic-based views and their over-emphasis on the formal aspects of thought processes. The departure, however, has not been complete--- some fundamental assumptions of formal logic are still maintained in the new approaches. Similarly, "relevance" remains a major challenge for AI research. This paper outlines an alternative proposal that takes context and relevance as intertwined aspects of thought and intelligence. We argue for a pragmatic approach, which shows better promise than formal logic in dealing with such issues.

Abstract. Fodor and Pylyshyn’s critique of connectionism has posed a challenge to connectionists: Adequately explain such nomological regularities as systematicity and productivity without postulating a “language of thought ” (LOT). Some connectionists like Smolensky took the challenge very seriously, and attempted to meet it by developing models that were supposed to be non-classical. At the core of these attempts lies the claim that connectionist models can provide a representational system with a combinatorial syntax and processes sensitive to syntactic structure. They are not implementation models because, it is claimed, the way they obtain syntax and structure sensitivity is not “concatenative, ” hence “radically different ” from the way classicists handle them. In this paper, I offer an analysis of what it is to physically satisfy/realize a formal system. In this context, I examine the minimal truth-conditions of LOT Hypothesis. From my analysis it will follow that concatenative realization of formal systems is irrelevant to LOTH since the very notion of LOT is indifferent to such an implementation level issue as concatenation. I will conclude that to the extent to which they can explain the law-like cognitive regularities, a certain class of connectionist models proposed as radical alternatives to the classical LOT paradigm will in fact turn out to be LOT models, even though new and potentially very exciting ones.

The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational states are individuated within computability theory and computer science. A secondary purpose is to show that existing arguments for the semantic view are defective.