Keyan Zahedi

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This work presents a novel learning method in the context of embodied artificial intelligence and self-organization, which has as few assumptions and restrictions as possible about the world and the underlying model. The learning rule is derived from the principle of maximizing the predictive information in the sensorimotor loop. It is evaluated on robot(More)
Using discrete-time dynamics of a two neuron network with recurrent connectivity it is shown that for specific parameter configurations the output signals of neurons can be of almost sinusoidal shape. These networks live near the Sacker-Neimark bifurcation set, and are termed SO(2)-networks, because their weight matrices correspond to rotations in the(More)
The field of embodied intelligence emphasises the importance of the morphology and environment with respect to the behaviour of a cognitive system. The contribution of the morphology to the behaviour, commonly known as morphological computation, is well-recognised in this community. We believe that the field would benefit from a formalisation of this(More)
We consider the causal structure of the sensorimotor loop (SML) and represent the agent’s policies in terms of conditional restricted Boltzmann machines (CRBMs). CRBMs can model non-trivial conditional distributions on high dimensional input-output spaces with relatively few parameters. In addition, their Glauber dynamics can be computed efficiently to(More)
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(More)
Even if the character of robotics is primarily technological, it was always closely connected with biology right from the beginning. However, most of the time this was only a one-way relationship, for biological insights were often used as a pool of approved ideas and methods to find solutions for rudimentary problems in robotics (walking machines in(More)
It is claimed that synaptic plasticity of neural controllers for autonomous robots can enhance the behavioral properties of these systems. Based on homeostatic properties of so called self-regulating neurons, the presented mechanism will vary the synaptic strength during the robot interaction with the environment, due to driving sensor inputs and motor(More)
This article deals with the causal structure of an agent’s sensorimotor loop. Of particular interest are causal effects that can be identified from an agent-centric perspective based on in situ observations. Within this identification, the world model of the agent plays a central role. Furthermore, various kinds of information flows through the sensorimotor(More)