First published 20 July 2015
Quantifying Morphological Computation based on an Information Decomposition of the Sensorimotor Loop
Keyan Ghazi-Zahedi and Johannes Rauh
The question of how an agent is affected by its embodiment has attracted growing attention in recent years. A new field of artificial intelligence has emerged, which is based on the idea that intelligence cannot be understood without taking the embodiment into account. The contribution of an agent’s embodiment to its behaviour is also known as morphological computation. In this work, we propose a quantification of morphological computation, which is based on an information decomposition of the sensorimotor loop into shared, unique and synergistic information. Using a simple model of the sensorimotor loop, we show that the unique information of the body with respect to the environment is a good measure for morphological computation.