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Embodied imitation and behavioural adaptation in robot collectives

Başlık çevirisi mevcut değil.

  1. Tez No: 400209
  2. Yazar: MEHMET DİNÇER ERBAŞ
  3. Danışmanlar: PROF. ALAN FT WİNFİELD, PROF. LARRY BULL
  4. Tez Türü: Doktora
  5. Konular: Çevre Mühendisliği, Environmental Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2012
  8. Dil: İngilizce
  9. Üniversite: University of the West of England
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 141

Özet

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Özet (Çeviri)

Social learning, which enables the individuals to learn from others in a community, is an importantmechanism for social animals. One of the most important types of social learning is imitationas it allows certain skills and behaviours to be transferred between individuals without languageor other complex symbolic communication. This thesis investigates embodied imitation andbehavioural adaptation in collective robotics. Firstly, by using robots to model social learning byimitation in a collective, we examine the adaptation of behaviours and their structure. Secondly,the link between imitation and individual learning is investigated and a method that uses imitationas an enhancing factor to increase the learning speed of artificial agents is developed and tested.In this thesis, real robots are used to model imitation and a robot to robot movement imitationalgorithm is implemented. The algorithm is embodied in the sense that robots use their on-boardsensors to perceive the movement of other robots. Following this, a quality of imitation functionis designed to quantitatively assess the fidelity of imitation. It is shown that although we havea homogeneous group of robots, variations that are caused by embodiment arise during imitationprocess. In experiments not previously undertaken in a group of real robots, it is shown thatthese variations allow certain behaviours, that appear to be more robust to uncertainties in robot?ssensors, to emerge during multiple cycles of imitation. Therefore, as the robots share a similarperceptual context, the structure of the imitated behaviours adapts to the uncertainties in robot?ssensors so that these emergent behaviours can be copied with high fidelity. This process is proposedas a possible abstract yet embodied model of cultural evolution that explains the emergenceand propagation of shared behaviours by imitation in a group of social agents.This thesis also presents an algorithm that combines individual learning and imitation. Thealgorithm uses imitation as an enhancing factor that increases the learning speed of agents thatutilise a well known reinforcement learning method. The algorithm is novel as it attempts touse imitation of purely observed behaviours to enhance learning. As in nature, imitation providesagents with model behaviours that can lead their individual learning process. Finding in what stateor context these model behaviours are useful is determined by the learning process of the imitatingagent. The algorithm is validated both in a simulation, and on real robots that use embodiedimitation and learning speed is shown to increase in the robot collective.

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