Abstract
In order to understand language, a hearer must draw inferences to establish referential and causal coherence. Hence our ability to understand language suggests that we are capable of performing a wide range of inferences rapidly and spontaneously. This poses a challenge for cognitive science: How can a system of slow neuron-like elements encode a large body of knowledge and perform inferences with such speed? SHRUTI attempts to answer this question by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, and systematic rule-like knowledge, and yet perform a range of inferences within a few hundred milliseconds. This paper describes a novel representation of types and instances in SHRUTI that supports the encoding of rules and facts involving types and quantifiers, enables SHRUTI to distinguish between hypothesized and asserted entities, and facilitates the dynamic instantiation and unification of entities during inference.
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Shastri, L. (2000). Types and Quantifiers in SHRUTI – A Connectionist Model of Rapid Reasoning and Relational Processing. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_3
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DOI: https://doi.org/10.1007/10719871_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67305-7
Online ISBN: 978-3-540-46417-4
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