Reasoning about Time and Knowledge in Neural Symbolic Learning Systems

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Artur Garcez, Luis Lamb


We show that temporal logic and combinations of temporal logics and modal logics of knowledge can be effectively represented in ar(cid:173) tificial neural networks. We present a Translation Algorithm from temporal rules to neural networks, and show that the networks compute a fixed-point semantics of the rules. We also apply the translation to the muddy children puzzle, which has been used as a testbed for distributed multi-agent systems. We provide a complete solution to the puzzle with the use of simple neural networks, capa(cid:173) ble of reasoning about time and of knowledge acquisition through inductive learning.