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Media type:
E-Article
Title:
A Connectionist Computational Model for Epistemic and Temporal Reasoning
Contributor:
Garcez, Artur S. d'Avila;
Lamb, Luís C.
Published:
MIT Press - Journals, 2006
Published in:
Neural Computation, 18 (2006) 7, Seite 1711-1738
Language:
English
DOI:
10.1162/neco.2006.18.7.1711
ISSN:
1530-888X;
0899-7667
Origination:
Footnote:
Description:
The importance of the efforts to bridge the gap between the connectionist and symbolic paradigms of artificial intelligence has been widely recognized. The merging of theory (background knowledge) and data learning (learning from examples) into neural-symbolic systems has indicated that such a learning system is more effective than purely symbolic or purely connectionist systems. Until recently, however, neural-symbolic systems were not able to fully represent, reason, and learn expressive languages other than classical propositional and fragments of first-order logic. In this article, we show that nonclassical logics, in particular propositional temporal logic and combinations of temporal and epistemic (modal) reasoning, can be effectively computed by artificial neural networks. We present the language of a connectionist temporal logic of knowledge (CTLK). We then present a temporal algorithm that translates CTLK theories into ensembles of neural networks and prove that the translation is correct. Finally, we apply CTLK to the muddy children puzzle, which has been widely used as a testbed for distributed knowledge representation. We provide a complete solution to the puzzle with the use of simple neural networks, capable of reasoning about knowledge evolution in time and of knowledge acquisition through learning.