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Open AccessProceedings

Structural Machines as Unconventional Knowledge Processors

Ageno School of Business, Golden Gate University, San Francisco, CA 94105, USA
Department of Mathematics, University of California Los Angeles, Los Angeles, CA 90095, USA
Author to whom correspondence should be addressed.
Conference Morphological, Natural, Analog and Other Unconventional Forms of Computing for Cognition and Intelligence (MORCOM), Berkeley, CA, USA, 2–6 June 2019.
Proceedings 2020, 47(1), 26;
Published: 7 May 2020
(This article belongs to the Proceedings of IS4SI 2019 Summit)
Knowledge systems often have very sophisticated structures depicting cognitive and
structural entities. For instance, representation of knowledge in the form of a text involves the
structure of this text. This structure is represented by a hypertext, which is networks consisting of
linguistic objects, such as words, phrases and sentences, with diverse links connecting them.
Current computational machines and automata such as Turing machines process information in
the form of symbol sequences. Here we discuss based the methods of structural machines
achieving higher flexibility and efficiency of information processing in comparison with regular
models of computation. Being structurally universal abstract automata, structural machines allow
working directly with knowledge structures formed by knowledge objects and connections
between them.
Keywords: cognition; named set; knowledge structure; theory of oracles; structural machine cognition; named set; knowledge structure; theory of oracles; structural machine
MDPI and ACS Style

Mikkilineni, R.; Burgin, M. Structural Machines as Unconventional Knowledge Processors. Proceedings 2020, 47, 26.

AMA Style

Mikkilineni R, Burgin M. Structural Machines as Unconventional Knowledge Processors. Proceedings. 2020; 47(1):26.

Chicago/Turabian Style

Mikkilineni, Rao; Burgin, Mark. 2020. "Structural Machines as Unconventional Knowledge Processors" Proceedings 47, no. 1: 26.

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