Artificial Life and Intelligent Agents: First International by Christopher J. Headleand, William J. Teahan, Llyr Ap Cenydd

By Christopher J. Headleand, William J. Teahan, Llyr Ap Cenydd

This booklet constitutes the refereed court cases of the 1st foreign Symposium on synthetic existence and clever brokers, ALIA 2014, held in Bangor, united kingdom, in November 2014. the ten revised complete papers have been rigorously reviewed and chosen from 20 submissions. The papers are equipped in topical sections on studying and evolution; human interplay; robot simulation.

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Extra info for Artificial Life and Intelligent Agents: First International Symposium, ALIA 2014, Bangor, UK, November 5-6, 2014. Revised Selected Papers

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The feature vector is normalized to sum to 1, to give a probability distribution of Uniform Local Binary Patterns, with 1 representing maximum distribution of patterns and 0 for no pattern; which forms the input vector of the neural network along with the efferent copies of the movement and categorization output units Experiment Three. We performed a third experiment in-order to do a comparative analysis of the results with the results from our proposed method in Experiment Two. In this experiment, we adopted the gray-scale averaging method in [1] for the processing of the periphery region of the images taken from the Humanoid robot camera.

In the rest of the scenes it displayed periods of both stable and oscillatory activations of the colour output nodes. 2 Testing Round 2 Four individuals, two each from the two best six-scene and twelve-scene runs, were then chosen to be subject to a further round of testing. The aim of this round was to investigate the robustness and generality of their road-following strategies by observing their behaviour in environments they had not encountered during the evolutionary phase. The twelve scenes were recreated with textures having average contrast of 90 and deviations from mean of around 40 (on a scale of 0–255).

Simulated Road Following Using Neuroevolution 27 The results of the twelve-scene experiments (Fig. 4) were not as uniform, with solutions showing greater variability in their colour perception strategies, depending on the seed and colour distribution set they were evolved in. The majority of solutions (like S5 and S6) only evolved the ability to dynamically vary two of their three colour outputs and simply did not use the third. This meant that two out of the six mono-colour scenes (basic and reversed) could not be solved.

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Artificial Life and Intelligent Agents: First International by Christopher J. Headleand, William J. Teahan, Llyr Ap Cenydd
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