Both everyday experience and scientific studies indicate that emotional intelligence in general, and empathy in particular, improve the effectiveness of human teamwork. Research in affective computing confirms their significance in systems where humans and artificial agents interact. This paper explores the notion of empathy between artificial agents that has so far received little attention, and argues that it could have significant impact on the design of robust and resilient agent teams. Combining the formal framework of Emotional BDI agents with the principles underlying the leading model of empathy in psychology and neuroscience, we define a hierarchy of affective and behavioral responses, integrated into an algorithm that formalizes the interactions between the subject and object of empathy in the domain of artificial practical reasoning agents.
Deep learning is well known as a method to extract hierarchical representations of data. This method has been widely implemented in many fields , including image classification, speech recognition, natural language processing, etc. Over the past decade, deep learning has made a great progress in solving face recognition problems due to its effectiveness. In this thesis a novel deep learning multilayer hierarchy based methodology, named Local Binary Pattern Network (LBPNet), is proposed. Unlike the shallow LBP method, LBPNet performs multi-scale analysis and gains high-level representations from low-level overlapped features in a systematic manner. The LBPNet deep learning network is generated by retaining the topology of Convolutional Neural Network (CNN) and replacing its trainable kernel with the offthe-shelf computer vision descriptor, the LBP descriptor. This enables LBPNet to achieve a high recognition accuracy without requiring costly model learning approach on massive data. LBPNet progressively extracts features from input images from test and training data through multiple processing layers, pairwisely measures the similarity of extracted features in regional level , and then performs the classification based on the aggregated similarity values. Through extensive numerical experiments using the popular benchmarks (i.e., FERET, LFW and YTF) , LBPNet has shown the promising results. Its results outperform (on FERET) or are comparable (on LFW and FERET) to other methods in the same categories, which are single descriptor based unsupervised learning methods 7 Conclusions and Future Work 67 7.1 Conclusions 7.2 Future Work .
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