Abstract
A unifying machine learning algorithm is proposed, in which the same processes, data structures and memory management can be used simultaneously in divergent realms, including conversation, musical composition, and robotics. A central aspect of the project is setting up learning modules that can absorb and use relationships in the input – relationships that are neither analyzed and predicted nor perceived by the programmer. The data, internal objects, and actions available to the program exist as points in a quasi-Cartesian, multi-dimensional knowledge space. The geometry of this space is determined by semantic content. Within a combination of this space and a kind of production system one can take advantage of content-addressable memory to replace all search, one can implement table-driven program control that allows the program to change itself while running and distributed massively-parallel processing can be used.
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Notes
- 1.
See “Conceptual Role Semantics” at https://www.iep.utm.edu/conc-rol/.
- 2.
Search on Wang-goertzel.AGI_Aspects.pdf.
- 3.
Briefly, the work is based on a theory of intelligence, there is an engineering plan to implement the work, and the work has produced some concrete results.
- 4.
Integrated Intelligent Capabilities (AAAI Special Track), A Roadmap to Human-Level Intelligence (IEEE WCCI Panel Session), Building and Evaluating Models of Human-Level Intelligence (CogSci Symposium), and The AGIRI workshop.
- 5.
See “Voronoi diagrams” at mathworld.wolfram.com/VoronoiDiagram.html.
- 6.
See “The curse of dimension” at https://www.encyclopediaofmath.org/index.php/Curse_of_dimension.
- 7.
Eleanor Rosch's work on prototype theory is spread out among a number of papers and book chapters, all of which are listed at https://en.wikipedia.org/wiki/Eleanor_Rosch.
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Lanz, C. (2020). Nomen Meum Earl: Yet Another Route to Intelligent Machine Behavior. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_27
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