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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 1 (1991), S. 55-73 
    ISSN: 1572-8641
    Keywords: Belief ; syntax ; propositions ; meaning ; information ; tractability ; degrees of confidence ; dispositions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract Within AI and the cognitively related disciplines, there exist a multiplicity of uses of ‘belief’. On the face of it, these differing uses reflect differing views about the nature of an objective phenomenon called ‘belief’. In this paper I distinguish six distinct ways in which ‘belief’ is used in AI. I shall argue that not all these uses reflect a difference of opinion about an objective feature of reality. Rather, in some cases, the differing uses reflect differing concerns with special AI applications. In other cases, however, genuine differences exist about the nature of what we pre-theoretically call belief. To an extent the multiplicity of opinions about, and uses of ‘belief’, echoes the discrepant motivations of AI researchers. The relevance of this discussion for cognitive scientists and philosophers arises from the fact that (a) many regard theoretical research within AI as a branch of cognitive science, and (b) even if theoretical AI is not cognitive science, trends within AI influence theories developed within cognitive science. It should be beneficial, therefore, to unravel the distinct uses and motivations surrounding ‘belief’, in order to discover which usages merely reflect differing pragmatic concerns, and which usages genuinely reflect divergent views about reality.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 1 (1991), S. 279-320 
    ISSN: 1572-8641
    Keywords: Process belief model ; nested propositional attitudes ; senses ; truth ; liar paradox
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract A process-oriented model of belief is presented which permits the representation of nested propositional attitudes within first-order logic. The model (NIM, for ‘nested intensional model’) is axiomatized, sense-based (via intensions), and sanctions inferences involving nested epistemic attitudes, with different agents and different times. Because NIM is grounded upon senses, it provides a framework in which agents may reason about the beliefs of another agent while remaining neutral with respect to the syntactic forms used to express the latter agent's beliefs. Moreover, NIM provides agents with a conceptual map, interrelating the concepts of ‘knowledge’, ‘belief’, ‘truth’, and a number of cognate concepts, such as ‘infers’, ‘retracts’, and ‘questions’. The broad scope of NIM arises in part from the fact that its axioms are represented in a novel extension of first-order logic, ℐ-FOL (presented herein). ℐ-FOL simultaneously permits the representation of truth ascriptions, implicit self-reference, and arbitrarily embedded sentences within a first-order setting. Through the combined use of principles derived from Frege, Montague, and Kripke, together with context-sensitive semantic conventions, ℐ-FOL captures the logic of truth inferences, while avoiding the inconsistencies exhibited by Tarski. Applications of ℐ-FOL and NIM to interagent reasoning are described and the soundness and completeness of ℐ-FOL are established herein.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 7 (1997), S. 571-579 
    ISSN: 1572-8641
    Keywords: systematicity ; connectionism ; cognitive architecture ; explanation ; structure-sensitivity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract In his discussion of results which I (with Michael Hayward) recently reported in this journal, Kenneth Aizawa takes issue with two of our conclusions, which are: (a) that our connectionist model provides a basis for explaining systematicity “within the realm of sentence comprehension, and subject to a limited range of syntax” (b) that the model does not employ structure-sensitive processing, and that this is clearly true in the early stages of the network's training. Ultimately, Aizawa rejects both (a) and (b) for reasons which I think are ill-founded. In what follows, I offer a defense of our position. In particular, I argue (1) that Aizawa adopts a standard of explanation that many accepted scientific explanations could not meet, and (2) that Aizawa misconstrues the relevant meaning of ‘structure-sensitive process’.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 3 (1993), S. 183-200 
    ISSN: 1572-8641
    Keywords: Connectionism ; representation ; explicit rules
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract At present, the prevailing Connectionist methodology forrepresenting rules is toimplicitly embody rules in “neurally-wired” networks. That is, the methodology adopts the stance that rules must either be hard-wired or “trained into” neural structures, rather than represented via explicit symbolic structures. Even recent attempts to implementproduction systems within connectionist networks have assumed that condition-action rules (or rule schema) are to be embodied in thestructure of individual networks. Such networks must be grown or trained over a significant span of time. However, arguments are presented herein that humanssometimes follow rules which arevery rapidly assignedexplicit internal representations, and that humans possessgeneral mechanisms capable of interpreting and following such rules. In particular, arguments are presented that thespeed with which humans are able to follow rules ofnovel structure demonstrates the existence of general-purpose rule following mechanisms. It is further argued that the existence of general-purpose rule following mechanisms strongly indicates that explicit rule following is not anisolated phenomenon, but may well be a common and important aspect of cognition. The relationship of the foregoing conclusions to Smolensky's view of explicit rule following is also explored. The arguments presented here are pragmatic in nature, and are contrasted with thekind of arguments developed by Fodor and Pylyshyn in their recent, influential paper.
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 5 (1995), S. 219-242 
    ISSN: 1572-8641
    Keywords: Explicit ; implicit ; connectionism ; representation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract Much of traditional AI exemplifies the “explicit representation” paradigm, and during the late 1980's a heated debate arose between the classical and connectionist camps as to whether beliefs and rules receive an explicit or implicit representation in human cognition. In a recent paper, Kirsh (1990) questions the coherence of the fundamental distinction underlying this debate. He argues that our basic intuitions concerning ‘explicit’ and ‘implicit’ representations are not only confused but inconsistent. Ultimately, Kirsh proposes a new formulation of the distinction, based upon the criterion ofconstant time processing. The present paper examines Kirsh's claims. It is argued that Kirsh fails to demonstrate that our usage of ‘explicit’ and ‘implicit’ is seriously confused or inconsistent. Furthermore, it is argued that Kirsh's new formulation of the explicit-implicit distinction is excessively stringent, in that it banishes virtually all sentences of natural language from the realm of explicit representation. By contrast, the present paper proposes definitions for ‘explicit’ and ‘implicit’ which preserve most of our strong intuitions concerning straightforward uses of these terms. It is also argued that the distinction delineated here sustains the meaningfulness of the abovementioned debate between classicists and connectionists.
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 7 (1997), S. 1-37 
    ISSN: 1572-8641
    Keywords: Connectionism ; systematicity ; learning ; language ; semantics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract Fodor's and Pylyshyn's stand on systematicity in thought and language has been debated and criticized. Van Gelder and Niklasson, among others, have argued that Fodor and Pylyshyn offer no precise definition of systematicity. However, our concern here is with a learning based formulation of that concept. In particular, Hadley has proposed that a network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning representations to novel sentences (both simple and embedded) which contain words in syntactic positions they did not occupy during training. The experience of researchers indicates that strong systematicity in any form is difficult to achieve in connectionist systems. Herein we describe a network which displays strong semantic systematicity in response to Hebbian, connectionist training. During training, two-thirds of all nouns are presented only in a single syntactic position (either as grammatical subject or object). Yet, during testing, the network correctly interprets thousands of sentences containing those nouns in novel positions. In addition, the network generalizes to novel levels of embedding. Successful training requires a, corpus of about 1000 sentences, and network training is quite rapid. The architecture and learning algorithms are purely connectionist, but ‘classical’ insights are discernible in one respect, viz, that complex semantic representations spatially contain their semantic constituents. However, in other important respects, the architecture is distinctly non-classical.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 9 (1999), S. 197-221 
    ISSN: 1572-8641
    Keywords: cognitive architecture ; connectionism ; skills ; modules
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract In the late 1980s, there were many who heralded the emergence of connectionism as a new paradigm – one which would eventually displace the classically symbolic methods then dominant in AI and Cognitive Science. At present, there remain influential connectionists who continue to defend connectionism as a more realistic paradigm for modeling cognition, at all levels of abstraction, than the classical methods of AI. Not infrequently, one encounters arguments along these lines: given what we know about neurophysiology, it is just not plausible to suppose that our brains are digital computers. Thus, they could not support a classical architecture. I argue here for a middle ground between connectionism and classicism. I assume, for argument's sake, that some form(s) of connectionism can provide reasonably approximate models – at least for lower-level cognitive processes. Given this assumption, I argue on theoretical and empirical grounds that most human mental skills must reside in separate connectionist modules or ‘sub-networks’. Ultimately, it is argued that the basic tenets of connectionism, in conjunction with the fact that humans often employ novel combinations of skill modules in rule following and problem solving, lead to the plausible conclusion that, in certain domains, high level cognition requires some form of classical architecture. During the course of argument, it emerges that only an architecture with classical structure could support the novel patterns of information flow and interaction that would exist among the relevant set of modules. Such a classical architecture might very well reside in the abstract levels of a hybrid system whose lower-level modules are purely connectionist.
    Type of Medium: Electronic Resource
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  • 8
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 3 (1987), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: In Minds, Machines, and Gödel, Lucas offers an argument, based upon Godel's incompleteness theorems, that his mind cannot be modelled by a machine. This argument has generated a variety of alleged refutations, some of which are incompatible with others. It is argued here that the incompatibility of these refutations points to a puzzle or paradox which has not yet been resolved. A solution to this puzzle is presented in which it is argued that the existence of an algorithm, capable of generating a godel sentence for an axiomatic model of that same algorithm, is not incompatible with Godels well-known results. It is further argued that, contrary to received opinion, Gödel's results do not provide grounds for believing that cognitive agents are incapable of proving the consistency of correct formal models of their own cognitive mechanisms. This is shown to be so, even on the assumption that these formal models are known by those agents (on empirical grounds) to be formal models of themselves. Finally, the implications of the above issues for theoretical questions in AI are explored.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 4 (1988), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Logical omniscience may be described (roughly) as the state of affairs in which an agent explicitly believes anything which is logically entailed by that agent's beliefs. It is widely agreed that humans are not logically omniscient, and that an adequate formal model of belief, coupled with a correct semantic theory, would not entail logical omniscience. Recently, two prominent models of belief have emerged which purport both to avoid logical omniscience and to provide an intuitively appealing semantics. The first of these models is due to Levesque (1984b); the second to Fagin and Halpem (1985). It is argued herein that each of these models faces serious difficulties. Detailed criticisms are presented for each model, and a computationally oriented theory of intensions is presented which provides the foundation for a new formal model of belief. This formal model is presented in a decidable subset of first-order logic and is shown to provide a solution to the general problem of logical omniscience. The model provides for the possibility of belief revision and places no a priori restrictions upon an agent's representation language.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Mind & language 12 (1997), S. 0 
    ISSN: 1468-0017
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Linguistics and Literary Studies , Psychology
    Notes: Abstract: In their provocative 1988 paper, Fodor and Pylyshyn issued a formidable challenge to connectionists, i.e. to provide a non-classical explanation of the empirical phenomenon of systematicity in cognitive agents. Since the appearance of F&P's challenge, a number of connectionist systems have emerged which prima facie meet this challenge. However, Fodor and McLaughlin (1990) advance an argument, based upon a general principle of nomological necessity, to show that one of these systems (Smolensky's) could not satisfy the Fodor-Pylyshyn challenge. Yet, if Fodor and McLaughlin's analysis is correct, it is doubtful whether any existing connectionist system would fare better than Smolensky's. In the view of Fodor and McLaughlin, humans and classical architectures display systematicity as a matter of nomological necessity (necessity by virtue of natural law), but connectionist architectures do not. However, I argue that the Fodor-Pylyshyn-McLaughlin appeal to nomological necessity is untenable. There is a sense in which neither classical nor connectionist architectures possess nomological (or‘nomic’) necessity. However, the sense in which classical architectures do possess nomic necessity applies equally well to at least some connectionist architectures. Representational constituents can have causal efficacy within both classical and connectionist architectures.
    Type of Medium: Electronic Resource
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