Basic language learning in artificial animals
Paper in 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, Computer Science and Engineering (Chalmers), Data Science

Martin Nilsson

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Claes Strannegård

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Advances in Intelligent Systems and Computing

21945357 (ISSN) 2194-5365 (eISSN)

Vol. 848 155-161
9783319993157 (ISBN)

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

Subject Categories

Computer and Information Science





More information

Latest update