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9 篇 会议

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Grounding symbols in sensorimotor categories with neural networks

Grounding symbols in sensorimotor categories with neural net...

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iee colloquium on Grounding Representations: Integration of Sensory Information in Natural Language Processing, Artificial Intelligence and neural Networks

作者： S. Harnad Cognitive Sciences Centre Department of Psychology University of Southampton Southampton UK

The symbol grounding problem is: how can you ground the meanings of symbols autonomously, i.e. without the mediation of external interpretation? One solution to the symbol grounding problem is to abandon symbol systems altogether. The alternative is to abandon pure symbol systems for hybrid symbolic/nonsymbolic systems in which the symbol-meaning connection is not interpretation-dependent but autonomous and direct. It is such an approach that I describe in this paper. Suppose that the features in the sensory projection that allow the system to correctly categorise the objects of which they are the projections are found by some sort of learning mechanism, e.g. a neural net that takes sensory projections as input and gives category names as output. The name would be connected to the category of objects it denotes by a physical link including the neural net and the sensory projection of the objects. That would be the grounding, and the composite shape of the arbitrary name plus the net plus the sensory projection would no longer be arbitrary. If the combinations into which the name could enter were governed not only by syntactic rules operating on the arbitrary shape of the name alone, but also by the nonarbitrary shape of its grounding, then we would have a kind of doubly-constrained hybrid system, with heritable grounding. neural nets can do simple categorisation, but it still remains to embed them in autonomous sensorimotor systems that also have compositional capacity, to see how the two sets of constraints interact. One clue about this interaction may come from human categorical perception.

关键词： Cognitive science neural networks Terminology Pattern recognition

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iee colloquium on 'neural nets in human-computer interaction' (Digest No.179)

IEE Colloquium on 'Neural Nets in Human-Computer Interaction...

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iee colloquium on neural nets in human-computer interaction

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Application of neural computing techniques to learning support environments

Application of neural computing techniques to learning suppo...

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iee colloquium on neural nets in human-computer interaction

作者： L.J. Allinson N.M. Allinson Department of Psychology University of York York UK Department of Electronics University of York York UK

The application of neural network methods is demonstrated for the analysis of data obtained in a study of a hypertext system employed in computer assisted learning (CAL). The authors also discuss the future role of ne... 详细信息

关键词： computer aided instruction Hypertext systems neural networks

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neural networks in human-computer interaction: a view of user modelling

Neural networks in human-computer interaction: a view of use...

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iee colloquium on neural nets in human-computer interaction

作者： J. Finlay R. Beale Department of Computer Science University of York York UK

neural networks have proved valuable in solving pattern recognition and classification problems. Primarily they have been successfully applied to the fields of image and speech recognition, and much research is continuing in these areas. However in the field of human-computer interaction, pattern recognition is also a common requirement. The authors identify two related applications of neural techniques to recognizing patterns in user behaviour. The first is dynamic user modelling, which may be used in the development of adaptive interfaces, or intelligent help and tutoring systems, to provide the system with a model of the particular user. The second can be viewed as a form of static user modelling: analysing behavioural traces or protocol analysis.< >

关键词： Pattern recognition human factors Interactive systems User interfaces

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HCI: a cognitive neural net prospectus

HCI: a cognitive neural net prospectus

引用

iee colloquium on neural nets in human-computer interaction

作者： I. Aleksander H.B. Morton C.E. Myers Department of Electrical Engineering Imperial College London London UK Department of Human Sciences Brunei University Uxbridge UK

If neural nets are to have a future, and if the notion that they succeed where conventional artificial intelligence has failed is to be substantiated, they will have to be effective in improving the human computer interface. The key factor is their potential contribution to areas of language and scene understanding which will allow natural language (not necessarily speech) to be used as part of the interface. The authors present a 'gedanken experiment' of the way that a neural system will have to be designed and to operate in a very simple demonstrator task: getting a vision system to respond linguistically to a 'natural language' request based on a visual input.< >

关键词： Cognitive science Machine vision Natural languages neural networks Robots User interfaces

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Gestures and neural networks in human-computer interaction

Gestures and neural networks in human-computer interaction

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iee colloquium on neural nets in human-computer interaction

作者： R. Beale A.D.N. Edwards Dept. of Comput. Sci. York Univ. UK Department of Computer Science University of York York UK

One approach to improving human-computer communication is to copy aspects of human-human communication. Gesture is a very natural and much-used component of human-human communication but it is very imprecise. The authors present some preliminary work investigating the feasibility of using a neural network to recognize gestures from a small set of structured gestures-letters in sign language. Simulations have been carried out which use a neural network trained to recognize sign language letters. These show a very high level of recognition, even when there is considerable noise in the data. Sign language has been used in these experiments because it is a formalized symbolic gestural language but its interpretation by computer may have some significant applications. At the same time there is also a vast number of other applications which would lend themselves to gestural input on a less formal, but still symbolic basis and some of these are discussed.< >

关键词： human factors Interactive systems Learning systems Natural languages neural networks User interfaces

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Data visualisation using neural networks

Data visualisation using neural networks

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iee colloquium on neural nets in human-computer interaction

作者： D. Clayworth Neural Networks Applications Harwell Laboratory AEA Industrial Technology Didcot Oxfordshire UK

The problem of analysing a dataset which has many variables is one common to many disciplines, from sociology to molecular chemistry. Many techniques are employed in an attempt to detect patterns or trends within the datasets. RENDER is a neural net based algorithm for mapping high-dimensional dataset into two dimensions, so that the human eye can be used to detect trends and patterns in the data. It combines the advantages of linear and nonlinear mapping, in that it is relatively inexpensive to compute, it is reversible and does not lose as much information as a linear method. However the mathematics are not as precise as either of the other two methods, which may lead to difficulty with interpreting results. Further developments are in hand, both to improve the algorithm and its mathematical basis, and to develop it as a software tool.< >

关键词： computer graphics neural networks

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Safety critical neural computing: explanation and verification in knowledge augmented neural networks

Safety critical neural computing: explanation and verificati...

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iee colloquium on neural nets in human-computer interaction

作者： J.H. Johnson N.J. Hallam P.D. Picton Faculty of Technology Open University Milton UK

Conventional neural networks cannot contain a priori knowledge and cannot explain their output. A mathematical theory of black box classifiers is developed which covers most of the best known neural architectures. The limitations of the non-model based computational paradigm are discussed; these include inability to predict the behaviour of systems with multiple-valued, discontinuous, catastrophic and chaotic state spaces. Worse, they include the inability to detect the presence of such systems, when they are working: outside their 'range of competence', and with data of quality outside their range of experience. neural networks themselves cannot communicate with human decisionmakers in human terms; often the choice is 'take it or leave it'. Knowledge-based computation does not necessarily have these drawbacks, and can therefore augment the powerful neural computing paradigm where it is weakest. The authors consider three fundamental ways of combining the two computational paradigms and show how the explanation facility of knowledge based systems can be used to induce explanation on the output of neural subsystems. They conclude with an architecture which is generic for safety critical neural computation.< >

关键词： Explanation Knowledge based systems neural networks Safety

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Correcting recognition errors in handwritten, vocalised Pitman's shorthand outlines using a Boltzmann machine

Correcting recognition errors in handwritten, vocalised Pitm...

引用

iee colloquium on neural nets in human-computer interaction

作者： C.G. Leedham Y. Qiao Department of Electronic Systems Engineering University of Essex Colchester UK Dept. of Electron. Syst. Eng. Essex Univ. Colchester UK

Handwritten shorthand is typically 30%-50% faster than the QWERTY keyboard for recording textual information. Therefore, the automatic online recognition and transcription of Pitman's handwritten shorthand is a potentially faster method of entering textual material into a computer system. Pitman's shorthand can be considered as composed of two types of symbol: shortforms and vocalised outlines. The vocalised outlines account for between 50% and 70% of shorthand and are composed of a connected string of simple line segments which represent consonant sounds in the word surrounded by simple dots and dashes to represent the vowel sounds. To achieve automatic recognition of these vocalised outlines it is necessary to segment the consonant line into its basic features, classify each of them, including any surrounding vowel symbols and then determine the implied phonetic structure of the word. The authors consider a method of incorporating knowledge about the structure of the consonant kernel of vocalised shorthand outlines in a neural network to detect and correct errors in the initial classification of the basic pattern features and therefore improve the overall recognition performance. The initial results are encouraging.< >

关键词： Character recognition Pattern recognition neural networks Text processing

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