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Population of simulated agents controlled by dynamical neural networks are trained by artificial evolution to access linguistic instructions and to execute them by indicating, touching or moving specific target objects. During training the agent experiences only a subset of all object/action pairs. During post-evaluation, some of the successful agents proved to be able to access and execute also linguistic...
Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve the ability to adapt is by encoding local learning rules. However, a significant problem with most such approaches is that local learning rules for every connection in the network must be discovered separately. This paper...
Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed and the results show good performance compared to previous results achieved using learning methods. Furthermore,...
This paper presents results from two sets of experiments which investigate how strategies used by embodied dynamical agents in a simple braking task are affected by the perceptual information that the agents receive. Agents are evolved in a simple 2D environment containing one stationary object. The task of the agents is to stop as close as possible to the object without hitting it. The results of...
Self-organizing without a central controller in order to achieve collaboration towards an objective is one the main challenges in the design and operation of multi-robot systems. It is of great interest in the field to explore different approaches in order to achieve this end. Here we consider a distributed open-ended evolutionary approach called Asynchronous Situated Co-evolution (ASiCO) and introduce...
In this study we show how simulated robots evolved to display a navigation skills can spontaneously develop an internal model and rely on it to complete their task when sensory stimulation is temporarily unavailable. The analysis of some of the best evolved agents indicates that their internal model operates by anticipating functional properties of the next sensory state rather than the exact state...
The coupling between an agent’s body and its nervous system ensures that optimal behaviour generation can be undertaken in a specific niche. Depending on this coupling, nervous system or body plan architecture can partake in more or less of the behaviour. We will refer to this as the automatic distribution of computational workload. It is automatic since the coupling is evolved and not pre-specified...
The basal ganglia (BG) are a set of subcortical nuclei involved in action selection processes. We explore here the automatic parameterization of two models of the basal ganglia (the GPR and the CBG) using multi-objective evolutionary algorithms. We define two objective functions characterizing the supposed winner-takes-all functionality of the BG and obtain a set of solutions lying on the Pareto front...
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