A connectionist inductive learning system for modal logic programming

Neural-Symbolic integration has become a very active research area in the last decade. In this paper, we present a new massively parallel model for modal logic. We do so by extending the language of Modal Prolog to allow modal operators in the head of the clauses. We then use an ensemble of C-IL/sup...

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Bibliographic Details
Main Authors: d'Avila Garcez, A.S., Lamb, L.C., Gabbay, D.M.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Computer networks
Computer science
Concurrent computing
Educational institutions
Learning systems
Logic programming
Multiagent systems
Neural networks
Online Access:Request full text
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Summary:Neural-Symbolic integration has become a very active research area in the last decade. In this paper, we present a new massively parallel model for modal logic. We do so by extending the language of Modal Prolog to allow modal operators in the head of the clauses. We then use an ensemble of C-IL/sup 2/p neural networks to encode the extended modal theory (and its relations), and show that the ensemble computes a fixpoint semantics of the extended theory. An immediate result of our approach is the ability to perform learning from examples efficiently using each network of the ensemble. Therefore, one can adapt the extended C-IL/sup 2/P system by training possible world representations.
DOI:10.1109/ICONIP.2002.1199022