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Dimensions of Neuralsymbolic Integration  A Structured Survey
 We Will Show Them: Essays in Honour of Dov Gabbay
, 2005
"... Introduction Research on integrated neuralsymbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the ..."
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Cited by 21 (6 self)
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Introduction Research on integrated neuralsymbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. Already in the pioneering days of computational models of neural cognition, the question was raised how symbolic knowledge can be represented and dealt with within neural networks. The landmark paper [McCulloch and Pitts, 1943] provides fundamental insights how propositional logic can be processed using simple artificial neural networks. Within the following decades, however, the topic did not receive much attention as research in arti
Logic Programs, Iterated Function Systems, and Recurrent Radial Basis Function Networks
 Journal of Applied Logic
, 2004
"... Graphs of the singlestep operator for firstorder logic programs  displayed in the real plane  exhibit selfsimilar structures known from topological dynamics, i.e. they appear to be fractals, or more precisely, attractors of iterated function systems. We show that this observation can be ..."
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Cited by 15 (11 self)
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Graphs of the singlestep operator for firstorder logic programs  displayed in the real plane  exhibit selfsimilar structures known from topological dynamics, i.e. they appear to be fractals, or more precisely, attractors of iterated function systems. We show that this observation can be made mathematically precise. In particular, we give conditions which ensure that those graphs coincide with attractors of suitably chosen iterated function systems, and conditions which allow the approximation of such graphs by iterated function systems or by fractal interpolation. Since iterated function systems can easily be encoded using recurrent radial basis function networks, we eventually obtain connectionist systems which approximate logic programs in the presence of function symbols.
The integration of connectionism and firstorder knowledge representation and reasoning as a challenge for artificial intelligence
 In Proceedings of the Third International Conference on Information
, 2006
"... Intelligent systems based on firstorder logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning ..."
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Cited by 11 (7 self)
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Intelligent systems based on firstorder logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current stateoftheart research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neuralsymbolic systems. 1
Using Temporal Binding for Hierarchical Recruitment of Conjunctive Concepts over Delayed Lines
, 2003
"... The temporal correlation hypothesis proposes using distributed synchrony for the binding of different stimulus features. However, synchronized spikes must travel over cortical circuits that have varyinglength pathways, leading to mismatched arrival times. This raises the question of how initial sti ..."
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Cited by 4 (2 self)
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The temporal correlation hypothesis proposes using distributed synchrony for the binding of different stimulus features. However, synchronized spikes must travel over cortical circuits that have varyinglength pathways, leading to mismatched arrival times. This raises the question of how initial stimulusdependent synchrony might be preserved at a destination binding site. Earlier, we proposed constraints on tolerance and segregation parameters for a phasecoding approach, within cortical circuits, to address this question [22]. The purpose of the present paper is twofold. First, we conduct simulation experiments to test the proposed constraints. Second, we explore the practicality of temporal binding to drive a process of longterm memory formation based on a recruitment learning method [15].
Learning Object Recognition in a NeuroBotic System
 In
, 2004
"... Object localisation and identification is a crucial problem for advanced mobile service robots. We developed an object recognition system that localises and identifies objects using a colourbased visual attention control algorithm and a hierarchical neural network for object classification utilisin ..."
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Cited by 3 (2 self)
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Object localisation and identification is a crucial problem for advanced mobile service robots. We developed an object recognition system that localises and identifies objects using a colourbased visual attention control algorithm and a hierarchical neural network for object classification utilising hierarchical class grouping. The approach is evaluated in a test scenario where a robot is situated in front of a table. The robot has to identify and manipulate objects lying on this table. We evaluated the total object recognition performance and compared the effectiveness of different feature sets. The approach showed very encouraging results and meets realtime constraints. 1
A (Somewhat) New Solution to the Binding Problem
, 2006
"... To perform automatic, unconscious inference, the human brain must solve the ”binding problem ” by correctly grouping properties with objects. We propose a connectionist, localist model that uses short signatures, rather than temporal synchrony or other means, to do this binding. The proposed system ..."
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Cited by 3 (1 self)
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To perform automatic, unconscious inference, the human brain must solve the ”binding problem ” by correctly grouping properties with objects. We propose a connectionist, localist model that uses short signatures, rather than temporal synchrony or other means, to do this binding. The proposed system models our ability to both perform unification and handle multiple instantiations of logical terms, among other things. To verify its feasibility, we simulate our model with a computer program modeling simple, neuronlike computations. 1
Ontology Learning as a UseCase for NeuralSymbolic Integration
 In Proceedings of the IJCAI05 Workshop on NeuralSymbolic Learning and Reasoning, NeSy’05
, 2005
"... We argue that the field of neuralsymbolic integration is in need of identifying application scenarios for guiding further research. We furthermore argue that ontology learning  as occuring in the context of semantic technologies  provides such an application scenario with potential for s ..."
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Cited by 2 (2 self)
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We argue that the field of neuralsymbolic integration is in need of identifying application scenarios for guiding further research. We furthermore argue that ontology learning  as occuring in the context of semantic technologies  provides such an application scenario with potential for success and high impact on neuralsymbolic integration.
AppART: A ART hybrid stable learning neural network for universal function approximation
, 2001
"... This work describes AppART, an ARTbased low parameterized neural model that incrementally approximates continuousvalued multidimensional functions from noisy data using biologically plausible processes. AppART performs a higherorder NadarayaWatson regression and can be interpreted as a fuzzy sys ..."
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Cited by 1 (0 self)
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This work describes AppART, an ARTbased low parameterized neural model that incrementally approximates continuousvalued multidimensional functions from noisy data using biologically plausible processes. AppART performs a higherorder NadarayaWatson regression and can be interpreted as a fuzzy system. Some benchmark problems are solved in order to study AppART from an application point of view and to compare its results with the ones obtained from other models.
A Connectionist Model for Constructive Modal Reasoning
"... We present a new connectionist model for constructive, intuitionistic modal reasoning. We use ensembles of neural networks to represent intuitionistic modal theories, and show that for each intuitionistic modal program there exists a corresponding neural network ensemble that computes the program. T ..."
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Cited by 1 (0 self)
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We present a new connectionist model for constructive, intuitionistic modal reasoning. We use ensembles of neural networks to represent intuitionistic modal theories, and show that for each intuitionistic modal program there exists a corresponding neural network ensemble that computes the program. This provides a massively parallel model for intuitionistic modal reasoning, and sets the scene for integrated reasoning, knowledge representation, and learning of intuitionistic theories in neural networks, since the networks in the ensemble can be trained by examples using standard neural learning algorithms. 1
AppART: A hybrid neural network based on Adaptive Resonance Theory for universal function approximation
"... AppART is an Adaptive Resonance Theory low parameterized neural model that incrementally approximates continuousvalued multidimensional functions from noisy data using biologically plausible processes. AppART performs a higherorder NadarayaWatson regression and can be interpreted as a fuzzy logic ..."
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AppART is an Adaptive Resonance Theory low parameterized neural model that incrementally approximates continuousvalued multidimensional functions from noisy data using biologically plausible processes. AppART performs a higherorder NadarayaWatson regression and can be interpreted as a fuzzy logic Standard Additive Model. In this work we describe AppART dynamics and training. We also discuss the approach it makes to hybrid neural systems and deal with its theoretical foundations as a function approximation method. Three benchmark problems are solved in order to study AppART from an application point of view and to compare its results with the ones obtained from other models. Finally two modi cations to the AppART formulation aimed at improving AppART eciency are proposed and tested.