Title :
Learning and Representing Temporal Knowledge in Recurrent Networks
Author :
Borges, Rafael V. ; Garcez, Artur D´Avila ; Lamb, Luis C.
Author_Institution :
City Univ. London, London, UK
Abstract :
The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system´s desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing and learning temporal knowledge in recurrent networks. The model works in an integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models given a set of desirable system properties, and effective learning from examples, which in turn can lead to temporal knowledge extraction from the corresponding trained networks. The model is sound from a theoretical standpoint, but it has also been tested on a case study in the area of model verification and adaptation. The results contained in this paper indicate that model verification and learning can be integrated within the neural computation paradigm, contributing to the development of predictive temporal knowledge-based systems and offering interpretable results that allow system researchers and engineers to improve their models and specifications. The model has been implemented and is available as part of a neural-symbolic computational toolkit.
Keywords :
knowledge acquisition; knowledge representation; learning (artificial intelligence); recurrent neural nets; artificial intelligence; computer science; knowledge acquisition; knowledge reasoning; learning; model verification; neural computation model; neural computation paradigm; neural-symbolic computational toolkit; predictive temporal knowledge-based system; recurrent network; temporal knowledge extraction; temporal knowledge representation; Adaptation models; Computational modeling; Knowledge engineering; Knowledge representation; Learning systems; Predictive models; Recurrent neural networks; Integrating domain knowledge into nonlinear models; knowledge extraction; model verification; neural-symbolic computation; recurrent neural networks; temporal knowledge learning; temporal logic reasoning; Algorithms; Artificial Intelligence; Computer Simulation; Nonlinear Dynamics;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2011.2170180