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A sample nonogram puzzle. The objective of nonograms is, starting with a blank board (see left figure), to find a pattern of shaded boxes on the board such that the number of consecutively shaded boxes in each row and column appear as specified, in length and order, by the numbers that are printed to the left of each row and above each column (see right figure). For a more detailed explanation see the Domain section. 

A sample nonogram puzzle. The objective of nonograms is, starting with a blank board (see left figure), to find a pattern of shaded boxes on the board such that the number of consecutively shaded boxes in each row and column appear as specified, in length and order, by the numbers that are printed to the left of each row and above each column (see right figure). For a more detailed explanation see the Domain section. 

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Conference Paper
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We present the results of a 100 participant study on the role of a robot's physical presence in a robot tutoring task. Partic-ipants were asked to solve a set of puzzles while being pro-vided occasional gameplay advice by a robot tutor. Each par-ticipant was assigned one of five conditions: (1) no advice, (2) robot providing randomized advice, (3)...

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... A robot being physically located with the participant has many advantages. For example, research has shown that a physically present robot led to greater compliance [2] and learning gains [16] than the same robot displayed in a video. Studies have also shown that corrective feedback provided by co-located robots is more effective in helping people learn a given task correctly than video-displayed robots [28]. ...
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Thesis
Robots can not be programmed for every situation they might encounter justifying the need that they learn how to solve new tasks. Learning and teaching are animportant human social skills. Up to now, social skills of robots can be described as rather basic. Interactive robot learning approaches usually do not consider explicit teaching. Furthermore, usually they exploit actions that either accomplish the task or serve a social purpose. However, human behavior allows for actions that combine task and social aspects in one action. Human-Robot Interaction has not yet sufficiently addressed these combined actions. In this thesis we focus on actions that combine task and social actions. We are interested in how humans use these type of actions in HRI settings, how teaching influences this behavior and how these actions can be used to to augment robot learning. We conducted a user study investigating human teaching behavior towards a robot. Further, we implement the augmentation of robot learning with exploitation of task- and social channels by introducing a framework based on Reinforcement Learning.