77 research outputs found

    Implementation of dynamical systems with plastic self-organising velocity fields

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    To describe learning, as an alternative to a neural network recently dynamical systems were introduced whose vector fields were plastic and self-organising. Such a system automatically modifies its velocity vector field in response to the external stimuli. In the simplest case under certain conditions its vector field develops into a gradient of a multi-dimensional probability density distribution of the stimuli. We illustrate with examples how such a system carries out categorisation, pattern recognition, memorisation and forgetting without any supervision. [Continues.

    On the application of neural networks to symbol systems.

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    While for many years two alternative approaches to building intelligent systems, symbolic AI and neural networks, have each demonstrated specific advantages and also revealed specific weaknesses, in recent years a number of researchers have sought methods of combining the two into a unified methodology which embodies the benefits of each while attenuating the disadvantages. This work sets out to identify the key ideas from each discipline and combine them into an architecture which would be practically scalable for very large network applications. The architecture is based on a relational database structure and forms the environment for an investigation into the necessary properties of a symbol encoding which will permit the singlepresentation learning of patterns and associations, the development of categories and features leading to robust generalisation and the seamless integration of a range of memory persistencies from short to long term. It is argued that if, as proposed by many proponents of symbolic AI, the symbol encoding must be causally related to its syntactic meaning, then it must also be mutable as the network learns and grows, adapting to the growing complexity of the relationships in which it is instantiated. Furthermore, it is argued that in order to create an efficient and coherent memory structure, the symbolic encoding itself must have an underlying structure which is not accessible symbolically; this structure would provide the framework permitting structurally sensitive processes to act upon symbols without explicit reference to their content. Such a structure must dictate how new symbols are created during normal operation. The network implementation proposed is based on K-from-N codes, which are shown to possess a number of desirable qualities and are well matched to the requirements of the symbol encoding. Several networks are developed and analysed to exploit these codes, based around a recurrent version of the non-holographic associati ve memory of Willshaw, et al. The simplest network is shown to have properties similar to those of a Hopfield network, but the storage capacity is shown to be greater, though at a cost of lower signal to noise ratio. Subsequent network additions break each K-from-N pattern into L subsets, each using D-from-N coding, creating cyclic patterns of period L. This step increases the capacity still further but at a cost of lower signal to noise ratio. The use of the network in associating pairs of input patterns with any given output pattern, an architectural requirement, is verified. The use of complex synaptic junctions is investigated as a means to increase storage capacity, to address the stability-plasticity dilemma and to implement the hierarchical aspects of the symbol encoding defined in the architecture. A wide range of options is developed which allow a number of key global parameters to be traded-off. One scheme is analysed and simulated. A final section examines some of the elements that need to be added to our current understanding of neural network-based reasoning systems to make general purpose intelligent systems possible. It is argued that the sections of this work represent pieces of the whole in this regard and that their integration will provide a sound basis for making such systems a reality

    Towards Lifelong Reasoning with Sparse and Compressive Memory Systems

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    Humans have a remarkable ability to remember information over long time horizons. When reading a book, we build up a compressed representation of the past narrative, such as the characters and events that have built up the story so far. We can do this even if they are separated by thousands of words from the current text, or long stretches of time between readings. During our life, we build up and retain memories that tell us where we live, what we have experienced, and who we are. Adding memory to artificial neural networks has been transformative in machine learning, allowing models to extract structure from temporal data, and more accurately model the future. However the capacity for long-range reasoning in current memory-augmented neural networks is considerably limited, in comparison to humans, despite the access to powerful modern computers. This thesis explores two prominent approaches towards scaling artificial memories to lifelong capacity: sparse access and compressive memory structures. With sparse access, the inspection, retrieval, and updating of only a very small subset of pertinent memory is considered. It is found that sparse memory access is beneficial for learning, allowing for improved data-efficiency and improved generalisation. From a computational perspective - sparsity allows scaling to memories with millions of entities on a simple CPU-based machine. It is shown that memory systems that compress the past to a smaller set of representations reduce redundancy and can speed up the learning of rare classes and improve upon classical data-structures in database systems. Compressive memory architectures are also devised for sequence prediction tasks and are observed to significantly increase the state-of-the-art in modelling natural language

    Internet-based computer-aided learning for artificial neural networks

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    This thesis presents research performed to mvestigate the potential offered by the Internet for the implementation of an Engineering Computer-Aided Learning (CAL) environment. The research comprises two categories, a detailed literature survey of CAL and its application through the medium of the Internet environment. As a direct result of the literature survey, the scope of CAL can be considered to comprise the use of text, graphics, animations and sound. It is through the use of the CAL media that the true power of computer-aided education can be realized. The research performed focuses on student motivation, with emphasis placed on the educational environment. The Internet as a CAL environment was chosen for evaluation in this thesis due to its ability to convey information in a variety of contexts to any student with Internet access Courseware was developed for the M Eng (Masters in Engineering) program, specifically in the field of Artificial Neural Networks (ANNs) ANNs lend themselves to CAL due to their mathematical, graphical and exploratory nature. By considering the courseware developed the Internet is evaluated as an effective CAL medium through feedback from students taking the MEng course and from other interested parties. The thesis then concludes with suggestions for further development of CAL courseware on the Internet

    [Re|Dis]Connection:Interactive Storytelling Art

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    First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

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    Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered
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