Formula One (F1) drivers are amongst the most highly skilled drivers in the world, but not every F1 driver is destined to be a F1 World Champion. Discovering new talent or refreshing strategies are long-term investmen...
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ISBN:
(纸本)9783319121574;9783319121567
Formula One (F1) drivers are amongst the most highly skilled drivers in the world, but not every F1 driver is destined to be a F1 World Champion. Discovering new talent or refreshing strategies are long-term investments for all competitive F1 teams. The F1 world and teams invest vast amounts in developing high-fidelity simulators;however, driving games have seldom been associated with uncovering certain natural abilities. Beyond nature and nurture to attain success at the top level, certain motor-cognitive aspects are paramount for proficiency. One method of potentially finding talent is studying the behavioral and cognitive patterns associated with learning. Here, an F1 simulation game was used to demonstrate how learning had taken place. The indicative change of interest is from cognitive to motor via more skilled autonomous driving style -a skill synonymous with expert driving and ultimately winning races. Our data show clear patterns of how this skill develops.
The way for performing multiple sequence alignment is based on the criterion of the maximum scored information content computed from a weight matrix, but it is possible to have two or more alignments to have the same ...
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ISBN:
(纸本)9783642219306
The way for performing multiple sequence alignment is based on the criterion of the maximum scored information content computed from a weight matrix, but it is possible to have two or more alignments to have the same highest score leading to ambiguities in selecting the best alignment. This paper addresses this issue by introducing the concept of joint weight matrix to eliminate the randomness in selecting the best alignment of multiple sequences. Alignments with equal scores are iteratively re-scored with joint weight matrix of increasing level (nucleotide pairs, triplets and so on) until one single best alignment is eventually found. This method can be easily implemented to algorithms using weight matrix for scoring such as those based on the widely used Gibbs sampling method.
This paper presented a novel approach to solving the problem of robot path planning A Learning Classifier System is an accuracy based machine learning system that combines covering operator and genetic algorithm The c...
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ISBN:
(纸本)9783642149214
This paper presented a novel approach to solving the problem of robot path planning A Learning Classifier System is an accuracy based machine learning system that combines covering operator and genetic algorithm The covering operator is responsible for adjusting precision and large search space according to some reward obtained from the environment The genetic algorithm acts as an innovation discovery component which is responsible for discovering new better path planning rules The advantages of this approach are its accuracy-based representation that can easily reduce learning space improve online learning ability robustness due to the use of genetic algorithm
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