Simple Language Learning in Artificial General Intelligence

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/256405
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Type: Examensarbete för masterexamen
Master Thesis
Title: Simple Language Learning in Artificial General Intelligence
Authors: Johannesson, Louise
Nilsson, Martin
Abstract: The development of Artificial Intelligence (AI) has made it possible for computers to solve more and more of the tasks that humans can perform. One area of interest within AI research is Artificial General Intelligence (AGI), which aims to develop AI systems capable of solving multiple varying tasks. The Generic Animat research project is one biologically inspired approach to AGI. Our thesis aims to explore Natural Language Processing within the Animat model, by mimicking the way young children start to learn their first language. During the course of the thesis project we have developed two slightly different prototypes (referred to as the “Temporal Animat” and the “Chunking Animat”), both of which can learn to recognise and produce words that they are exposed to in a noise-free environment. Furthermore, functionality for word-to-word association and multimodal association has been implemented, thereby allowing the prototypes to associate between words, based on their occurences in some input text, and between words and other types of sensor input representing non-language concepts in the Animat’s environment. The prototypes were evaluated on small input texts with a limited number of words. The result of these evaluations suggests that both versions of the Animat are capable of achieving high degrees of accuracy when their word-to-word associations are compared to those of vector space models. The evaluation of multimodal associations has been done only for the Temporal Animat. The results indicate that the Temporal Animat makes mostly accurate associations between words and other input, although more tests on larger data sets are needed to determine how well it works on a larger scale. Due to the similarity in implementation between the two prototypes, the Chunking Animat is expected to be equally successful at this task.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/256405
Collection:Examensarbeten för masterexamen // Master Theses



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