Connectionist modal logic: Representing modalities in neural networks

A S D Garcez, L C Lamb, D M Gabbay

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


Modal logics are amongst the most successful applied logical systems. Neural networks were proved to be effective learning systems. In this paper, we propose to combine the strengths of modal logics and neural networks by introducing Connectionist Modal Logics (CML). CML belongs to the domain of neural-symbolic integration, which concerns the application of problem-specific symbolic knowledge within the neurocomputing paradigm. In CML, one may represent, reason or learn modal logics using a neural network. This is achieved by a Modalities Algorithm that translates modal logic programs into neural network ensembles. We show that the translation is sound, i.e. the network ensemble computes a fixed-point meaning of the original modal program, acting as a distributed computational model for modal logic. We also show that the fixed-point computation terminates whenever the modal program is well-behaved. Finally, we validate CML as a computational model for integrated knowledge representation and learning by applying it to a well-known testbed for distributed knowledge representation. This paves the way for a range of applications on integrated knowledge representation and learning, from practical reasoning to evolving multi-agent systems. (c) 2006 Elsevier B.V. All rights reserved
Original languageEnglish
Pages (from-to)34 - 53
Number of pages20
JournalTheoretical Computer Science
Issue number1-2
Publication statusPublished - 22 Feb 2007


Dive into the research topics of 'Connectionist modal logic: Representing modalities in neural networks'. Together they form a unique fingerprint.

Cite this