neural-symbolic learning systems
Abductive reasoning in neural-symbolic learning systems
... Permanent repository link: http://openaccess.city.ac.uk/292/ Link to published version: http://dx.doi.org/10.1007/s11245-006-9005-5 Copyright and reuse: City Research Online aims to make[r] ...
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Value-based argumentation frameworks as neural-symbolic learning systems
... the neural network created by the neural argumentation algorithm executes a sound computation of the prevailing arguments in the argumentation ...a learning mechanism. Learning can be used to ...
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Argumentation Neural Networks: Value-based Argumentation Frameworks as Neural-Symbolic Learning Systems
... Permanent repository link: http://openaccess.city.ac.uk/4062/ Link to published version: TR/2004/DOC/01 Copyright and reuse: City Research Online aims to make research outputs of City, U[r] ...
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Proceedings of IJCAI International Workshop on Neural-Symbolic Learning and Reasoning NeSy 2005
... for systems of propositional rules are available (see, for example, [Aiello et ...generate neural ma- chinery for the non-monotonic processing of unless and until operators, in addition to outputting ...
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Towards integrated neural-symbolic systems for human-level AI: Two research programs helping to bridge the gaps
... in neural-symbolic integration, two research programs aiming at overcoming long-standing chal- lenges in the field are suggested to the community: The first program targets a better understanding of ...
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Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
... AI systems, in particular those based on machine learning, aiming at establishing solid foundations for the ...and learning mechanisms, so as to construct a rich semantics of intelligent cognitive ...
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Learning Symbolic Representations of Hybrid Dynamical Systems
... dynamical systems is a fundamental challenge in all branches of science and ...machine learning techniques, such as neural networks and support vector machines, are numerically accurate, they shed ...
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A neural-symbolic system for temporal reasoning with application to model verification and learning
... at learning the internal states nec- essary to take decisions in a temporal domain associated with synchronization of ...computing systems: the dining philosophers problem, originally de- scribed in ...
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Ontology learning as a use-case for neural-symbolic integration
... The construction of ontologies in whatever language, how- ever, appears as a narrow bottleneck to the proliferation of the Semantic Web and other applications of Semantic Tech- nologies. The success of the Semantic Web ...
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Proceedings of ECAI International Workshop on Neural-Symbolic Learning and reasoning NeSy 2006
... expert systems remain still ...for symbolic representation of the knowledge into these systems, which is a feature in which many CI systems fail to ...that symbolic representation can ...
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Neural-Symbolic Monitoring and Adaptation
... (Artificial) Neural Networks [11] are computational models inspired by biological nervous systems and generally presented as systems of interconnected ‘neurons’ which can compute values from ...The ...
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SEMANTIC IMAGE ANALYSIS USING A SYMBOLIC NEURAL ARCHITECTURE
... Intelligent systems based on symbolic knowledge processing and artificial neural networks differ ...of neural networks with the expressiveness of symbolic knowledge ...and ...
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A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning
... of systems that use machine learning to learn the complex rela- tions from observation of experts and trainees during task ...these systems are successful in learning and generalization, they ...
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Neurons and symbols: a manifesto
... precedes learning. Neural-symbolic networks can represent a range of expressive log- ics and implement certain important principles of symbolic ...However, neural-symbolic ...
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Designing a Symbolic Intermediate Representation for Neural Surface Realization
... from neural NLG systems often contain errors such as hallucination, rep- etition or ...a symbolic intermediate representa- tion to be used in multi-stage neural generation with the intention ...
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Inverse Abstraction of Neural Networks Using Symbolic Interpolation
... trained neural network to guide a local search over program- matic policies that are human-readable and more verifiable than the complex neural networks ...forcement learning, our work introduces a ...
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Static Analysis of Symbolic Transition Systems with Goose
... In this chapter we will detail related work on the verification of STSs. In chapter 3.1 we will explain mathematical induction and how it is used by Donaldson et. al. [15] to substitute cycles for a single property in ...
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Computational Intelligence in Manufacturing Handbook Jun Wang pdf
... intelligent systems for designing, planning, monitoring, modeling, and controlling manufacturing sys- tems and ...processes. Neural networks have proved able to contribute to solving many problems in ...
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An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique
... intelligence neural network technique for continual learning tasks [12]; its principle is based on the formation of Sparse Distributed Representations (SDR), the use of the SDR to form invariant ...
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1. Breast cancer diagonsis using artifical neural network
... a neural network model using data published to the Machine Learning Repository by the University of ...A neural network attempts to replicate the brain as a form of artificial intelligence through ...
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