Rational imitation for robots: the cost difference model

© 2017, © The Author(s) 2017. Infants imitate behaviour flexibly. Depending on the circumstances, they copy both actions and their effects or only reproduce the demonstrator’s intended goals. In view of this selective imitation, infants have been called rational imitators. The ability to selectively and adaptively imitate behaviour would be a beneficial capacity for robots. Indeed, selecting what to imitate is an outstanding unsolved problem in the field of robotic imitation. In this paper, we first present a formalized model of rational imitation suited for robotic applications. Next, we test and demonstrate it using two humanoid robots

Dominant Glint Based Prey Localization in Horseshoe Bats: A Possible Strategy for Noise Rejection

Rhinolophidae or Horseshoe bats emit long and narrowband calls. Fluttering insect prey generates echoes in which amplitude and frequency shifts are present, i.e. glints. These glints are reliable cues about the presence of prey and also encode certain properties of the prey. In this paper, we propose that these glints, i.e. the dominant glints, are also reliable signals upon which to base prey localization. In contrast to the spectral cues used by many other bats, the localization cues in Rhinolophidae are most likely provided by self-induced amplitude modulations generated by pinnae movement. Amplitude variations in the echo not introduced by the moving pinnae can be considered as noise interfering with the localization process. The amplitude of the dominant glints is very stable. Therefore, these parts of the echoes contain very little noise. However, using only the dominant glints potentially comes at a cost. Depending on the flutter rate of the insect, a limited number of dominant glints will be present in each echo giving the bat a limited number of sample points on which to base localization. We evaluate the feasibility of a strategy under which Rhinolophidae use only dominant glints. We use a computational model of the echolocation task faced by Rhinolophidae. Our model includes the spatial filtering of the echoes by the morphology of the sonar apparatus of Rhinolophus rouxii as well as the amplitude modulations introduced by pinnae movements. Using this model, we evaluate whether the dominant glints provide Rhinolophidae with enough information to perform localization. Our simulations show that Rhinolophidae can use dominant glints in the echoes as carriers for self-induced amplitude modulations serving as localization cues. In particular, it is shown that the reduction in noise achieved by using only the dominant glints outweighs the information loss that occurs by sampling the echo

Review of European guidelines on palliative sedation: a foundation for the updating of the European Association for Palliative Care framework

in 2009, the European Association for Palliative Care (EAPC) developed a framework on palliative sedation, acknowledging this practice as an important and ethically acceptable intervention of last resort for terminally ill patients experiencing refractory symptoms. Before and after that, other guidelines on palliative sedation have been developed in Europe with variations in terminology and concepts. As part of the Palliative Sedation project (Horizon 2020 Funding No. 825700), a revision of the EAPC framework is planned. The aim of this article is to analyze the most frequently used palliative sedation guidelines as reported by experts from eight European countries to inform the discussion of the new framework. The three most reported documents per country were identified through an online survey among 124 clinical experts in December 2019. Those meeting guideline criteria were selected. Their content was assessed against the EAPC framework on palliative sedation. The quality of their methodology was evaluated with the Appraisal Guideline Research and Evaluation (AGREE) II instrument. Nine guidelines were included. All recognize palliative sedation as a last-resort treatment for refractory symptoms, but the criterion of refractoriness remains a matter of debate. Most guidelines recognize psychological or existential distress as (part of) an indication and some make specific recommendations for such cases. All agree that the assessment should be multiprofessional, but they diverge on the expertise required by the attending physician/team. Regarding decisions on hydration and nutrition, it is proposed that these should be independent of those for palliative sedation, but there is no clear consensus on the decision-making process. Several weaknesses were highlighted, particularly in areas of rigor of development and applicability. The identified points of debate and methodological weaknesses should be considered in any update or revision of the guidelines analyzed to improve the quality of their content and the applicability of their recommendations

Autonomous parsing of behavior in a multi-agent setting

Imitation learning is a promising route to instruct robotic multi-agent systems. However, imitating agents should be able to decide autonomously what behavior, observed in others, is interesting to copy. Here we investigate whether a simple recurrent network (Elman net) can be used to extract meaningful chunks from a continuous sequence of observed actions. Results suggest that, even in spite of the high level of task specific noise, Elman nets can be used for isolating re-occurring action patterns in robots. Limitations and future directions are discussed

From spreading of behavior to dyadic interaction - a robot learns what to imitate

Imitation learning is a promising route to instruct robotic multi-agent systems and in human-robot interaction. However, imitating agents should be able to decide autonomously what behavior, observed in others, is interesting to copy. Here we investigate whether a simple recurrent network (Elman Net) can be used to extract meaningful chunks from a continuous sequence of observed actions. Results suggest that, even in spite of the high level of task specific noise, Elman nets can be used for isolating re-occurring action patterns in robots. We also show how we use these results for recognizing and imitating emotional behaviors in human-robot interaction scenario. Limitations and future directions are also discussed

Simulated trust : towards robust social learning

Social learning is a potentially powerful learning mechanism to use in artificial multi-agent systems. However, findings about how animals use social learning show that it is also possibly detrimental. By using social learning agents act based on second-hand information that might not be trustworthy. This can lead to the spread of maladaptive behavior throughout populations. Animals employ a number of strategies to selectively use social learning only when appropriate. This suggests that artificial agents could learn more successfully if they are able to strike the appropriate balance between social and individual learning. In this paper, we propose a simple mechanism that regulates the extent to which agents rely on social learning. Our agents can vary the amount of trust they have in others. The trust is not determined by the performance of others but depends exclusively on the agents’ own rating of the demonstrations. The effectiveness of this mechanism is examined through a series of simulations. We first show that there are various circumstances under which the performance of multi-agents systems is indeed seriously hampered when agents rely on indiscriminate social learning. We then investigate how agents that incorporate the proposed trust mechanism fare under the same circumstances. Our simulations indicate that the mechanism is quite effective in regulating the extent to which agents rely on social learning. It causes considerable improvements in the learning rate, and can, under some circumstances, even improve the eventual performance of the agents. Finally, some possible extensions of the proposed mechanism are being discussed

Simulated trust : towards robust social learning

Social learning is a potentially powerful learning mechanism to use in artificial multi-agent systems. However, findings about how animals use social learning show that it is also possibly detrimental. By using social learning agents act based on second-hand information that might not be trustworthy. This can lead to the spread of maladaptive behavior throughout populations. Animals employ a number of strategies to selectively use social learning only when appropriate. This suggests that artificial agents could learn more successfully if they are able to strike the appropriate balance between social and individual learning. In this paper, we propose a simple mechanism that regulates the extent to which agents rely on social learning. Our agents can vary the amount of trust they have in others. The trust is not determined by the performance of others but depends exclusively on the agents’ own rating of the demonstrations. The effectiveness of this mechanism is examined through a series of simulations. We first show that there are various circumstances under which the performance of multi-agents systems is indeed seriously hampered when agents rely on indiscriminate social learning. We then investigate how agents that incorporate the proposed trust mechanism fare under the same circumstances. Our simulations indicate that the mechanism is quite effective in regulating the extent to which agents rely on social learning. It causes considerable improvements in the learning rate, and can, under some circumstances, even improve the eventual performance of the agents. Finally, some possible extensions of the proposed mechanism are being discussed

Autonomous parsing of behavior in a multi-agent setting

Imitation learning is a promising route to instruct robotic multi-agent systems. However, imitating agents should be able to decide autonomously what behavior, observed in others, is interesting to copy. Here we investigate whether a simple recurrent network (Elman Net) can be used to extract meaningful chunks from a continuous sequence of observed actions. Results suggest that, even in spite of the high level of task specific noise, Elman nets can be used for isolating re-occurring action patterns in robots. Limitations and future directions are discussed