Abstract
Linguoplotter is a distributed and chaotic architecture where an entanglement of different processes interact to generate a text describing a raw data input. This paper describes recent additions to the architecture whereby a greater degree of language comprehension is used to improve the coherence of generated text. Some examples of the architecture operating are considered, including where it performs well and generates a good quality text; and instances where it gets trapped in loops that either prevent an output from being generated or cause a lower quality output to be produced before there is a chance to find a better alternative. Finally, ideas from the program Metacat are considered which could allow the program to observe its own processes and become a more human-like intelligence.
Partially supported by the UK EPSRC under grants EP/R513106/1 (Wright), EP/S033564/1 and EP/W001632/1 (Purver); the Slovenian Research Agency via research core funding for the programme Knowledge Technologies (P2-0103) and the projects CANDAS (J6-2581) and SOVRAG (J5-3102).
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Notes
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Using the version at https://github.com/georgeawright/linguoplotter/tree/v2.0.0.
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Wright, G.A., Purver, M. (2023). Self-Comprehension for More Coherent Language Generation. In: Hammer, P., Alirezaie, M., Strannegård, C. (eds) Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science(), vol 13921. Springer, Cham. https://doi.org/10.1007/978-3-031-33469-6_33
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