Basic language learning in artificial animals
Paper i proceeding, 2019

We explore a general architecture for artificial animals, or animats, that develops over time. The architecture combines reinforcement
learning, dynamic concept formation, and homeostatic decision-making aimed at need satisfaction. We show that this
architecture, which contains no ad hoc features for language processing, is capable of basic language learning of three kinds: (i)
learning to reproduce phonemes that are perceived in the environment via motor babbling; (ii) learning to reproduce sequences of
phonemes corresponding to spoken words perceived in the environment; and (iii) learning to ground the semantics of spoken words
in sensory experience by associating spoken words (e.g. the word “cold”) to sensory experience (e.g. the activity of a sensor for
cold temperature) and vice versa.


grounded semantics

generic animat

sequence learning

language learning

poverty of the stimulus


Louise Johannesson

Chalmers, Data- och informationsteknik, Data Science

Martin Nilsson

Chalmers, Data- och informationsteknik, Data Science

Claes Strannegård

Chalmers, Data- och informationsteknik, Data Science

Advances in Intelligent Systems and Computing

21945357 (ISSN) 2194-5365 (eISSN)

9783319993157 (ISBN)

Biologically Inspired Cognitive Architectures (BICA-18)
Prag, Czech Republic,


Data- och informationsvetenskap





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