Ashutosh Natraj, Stephen Cameron, Chris Melhuish, Mark's Towards Autonomous Robotic Systems: 14th Annual Conference, PDF

Human Computer Interaction

By Ashutosh Natraj, Stephen Cameron, Chris Melhuish, Mark Witkowski

ISBN-10: 3662436442

ISBN-13: 9783662436448

ISBN-10: 3662436450

ISBN-13: 9783662436455

This ebook constitutes the refereed court cases of the 14th convention on Advances in independent Robotics, TAROS 2013, held in Oxford, united kingdom, in August 2013. The 36 revised complete papers awarded including 25 prolonged abstracts have been rigorously reviewed and chosen from 89 submissions. The papers hide quite a few issues corresponding to synthetic intelligence, bio-inspired and aerial robotics, computing device imaginative and prescient, keep watch over, humanoid and robot arm, swarm robotics, verification and ethics.

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Additional info for Towards Autonomous Robotic Systems: 14th Annual Conference, TAROS 2013, Oxford, UK, August 28--30, 2013, Revised Selected Papers

Sample text

Similarly, for the QS-learning and HAQS algorithms the spreading function σ(v, u, s, a) was defined based on [5]. 7 decays according to the rule defined in Eq. 5a. 1 ∗ visitsn (v, u)]d lim τn = 0 n→∞ (5a) (5b) where τn is the value of τ at the n-th iteration and visitsn (s, a) is the number of times the learning agent has visited state v and executed action u at the n-th iteration as well. This way, the rate at which τ decays as the number of iterations n increases is faster than that of α, guaranteeing the convergence requirements, noticing Eq.

Q-learning and HAQL was automated using a third-party commercial software. The results of the trials of the Q-learning and HAQL algorithms are shown in Fig. 2, whilst Fig. 3 shows the learning curves of QS-learning and HAQS and Fig. 4 presents the results of Q(λ)learning and HAQ(λ). The graphs consist of mean and standard deviation (of the 5 trials) of the cumulative goal difference over 500 games. By analysing Figs. 2 and 3, it is possible to notice the similarity between the Q-learning and QSlearning, as well as the HAQL and Fig.

Distributed multirobot exploration maintaining a mobile network. In: 2nd International IEEE Conference on ‘Intelligent Systems’. Proceedings (IEEE Cat. 04EX791), pp. 113–118. IEEE (2004) 11. : Balancing the information gain against the movement cost for multi-robot frontier exploration. In: European Robotics Symposium (2008) 12. : Beyond frontier exploration. In: Proceedings of the 11th Robocup International Symposium (2007) 13. : Frontier-based exploration using multiple robots.

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Towards Autonomous Robotic Systems: 14th Annual Conference, TAROS 2013, Oxford, UK, August 28--30, 2013, Revised Selected Papers by Ashutosh Natraj, Stephen Cameron, Chris Melhuish, Mark Witkowski


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