Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Knowledge-based artificial neural networks
Artificial Intelligence
Robust reasoning: integrating rule-based and similarity-based reasoning
Artificial Intelligence
Computational capabilities of recurrent NARX neural networks
Computational capabilities of recurrent NARX neural networks
A neuroidal architecture for cognitive computation
Journal of the ACM (JACM)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Connectionist inference models
Neural Networks
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
Three problems in computer science
Journal of the ACM (JACM)
Handbook of Temporal Reasoning in Artificial Intelligence (Foundations of Artificial Intelligence (Elsevier))
A connectionist computational model for epistemic and temporal reasoning
Neural Computation
The Harmonic Mind: From Neural Computation to Optimality-Theoretic GrammarVolume I: Cognitive Architecture (Bradford Books)
The semantics of intention maintenance for rational agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Integrating model verification and self-adaptation
Proceedings of the IEEE/ACM international conference on Automated software engineering
Representing, learning and extracting temporal knowledge from neural networks: a case study
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Learning to adapt requirements specifications of evolving systems (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
A neural-symbolic cognitive agent for online learning and reasoning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Learning revised models for planning in adaptive systems
Proceedings of the 2013 International Conference on Software Engineering
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The importance of the efforts towards integrating the symbolic and connectionist paradigms of artificial intelligence has been widely recognised. Integration may lead to more effective and richer cognitive computational models, and to a better understanding of the processes of artificial intelligence across the field. This paper presents a new model for the representation, computation, and learning of temporal logic in connectionist systems. The model allows for the encoding of past and future temporal logic operators in neural networks, through a neural-symbolic translation algorithms introduced in the paper. The networks are relatively simple and can be used for reasoning about time and for learning by examples with the use of standard neural learning algorithms. We validate the model in a well-known application dealing WIth temporal synchronisation in distributed knowledge systems. This opens several interesting research paths in cognitive modelling, with potential applications in agent technology, learning and reasoning.