Prolog and natural-language analysis
Prolog and natural-language analysis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
Tagging with Small Training Corpora
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Connectionist model generation: A first-order approach
Neurocomputing
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Neuro-symbolic integration merges background knowledge and neural networks to provide a more effective learning system. It uses the Core Method as a means to encode rules. However, this method has several drawbacks in dealing with rules that have temporal extent. First, it demands some interface with the world which buffers the input patterns so they can be represented all at once. This imposes a rigid limit on the duration of patterns and further suggests that all input vectors be the same length. These are troublesome in domains where one would like comparable representations for patterns that are of variable length (e.g. language). Second, it does not allow dynamic insertion of rules conveniently. Finally and also most seriously, it cannot encode rules having preconditions satisfied at non-deterministic time points - an important class of rules. This paper presents novel methods for encoding such rules, thereby improves and extends the power of the state-of-the-art neuro-symbolic integration.