Rule-Based Sequence Learning Extension for Animats
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Typ
Examensarbete för masterexamen
Master Thesis
Master Thesis
Program
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2018
Författare
Pihlgren, Gustav Grund
Lallo, Nicklas
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis introduces a rule-based, sequence learning model. It proposes that parts of this model could be used as a independent extension to other machine learning models, animats specifically. The model uses Q-learning and state space search to generalize which are equivalent. This allows reducing the input state space to train faster and better draw conclusions about the features in the dataset at large. This knowledge can then be used to calculate the best action for the given sequence. The model is implemented in order to evaluate its capabilities. The model is evaluated primarily on the domains of simple arithmetic, Boolean logic, and simple English grammar and then compared to the performance of a Recurrent Neural Network using Long-Short Term Memory-units.
Beskrivning
Ämne/nyckelord
Data- och informationsvetenskap , Computer and Information Science