In social robotics, robots needs to be able to be understood by humans. Especially in collaborative tasks where they have to share mutual knowledge. For instance, in an educative scenario, learners share their knowledge and they must adapt their behaviour in order to make sure they are understood by others. Learners display behaviours in order to show their understanding and teachers adapt in order to make sure that the learners' knowledge is the required one. This ability requires a model of their own mental states perceived by others: In this paper, we discuss the importance of a cognitive architecture enabling second-order Mutual Modelling for Human-Robot Interaction in educative contexts.
Mutual modelling has also been studied through educational contexts. Roschelle and Teasley @cite_14 suggested that collaborative learning requires a of the task and of the shared information to solve it. The term mutual modelling" was introduced in Computer-Supported Collaborative Learning (CSCL) by Dillenbourg @cite_21 . It focused on knowledge states of agents. Dillenbourg developed in @cite_7 a computational framework to represent mutual modelling situations.
@cite_14 @cite_21 @cite_7
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It has been hypothesized that collaborative learning is related to the cognitive effort made by co-learners to build a shared understanding. The process of constructing this shared understanding requires that each team member builds some kind of representation of the behavior, beliefs, knowledge or intentions of other group members. In two empirical studies, we measured the accuracy of the mutual model, i.e. the difference between what A believes B knows, has done or intends to do and what B actually knows, has done or intends to do. In both studies, we found a significant correlation between the accuracy of A's model of B and the accuracy of B's model of A. This leads us to think that the process of modeling one's partners does not simply reflect individual attitudes or skills but emerges as a property of group interactions. We describe on-going studies that explore these preliminary results. We introduce collaborative learning in which multiple classifier heads of the same network are simultaneously trained on the same training data to improve generalization and robustness to label noise with no extra inference cost. It acquires the strengths from auxiliary training, multi-task learning and knowledge distillation. There are two important mechanisms involved in collaborative learning. First, the consensus of multiple views from different classifier heads on the same example provides supplementary information as well as regularization to each classifier, thereby improving generalization. Second, intermediate-level representation (ILR) sharing with backpropagation rescaling aggregates the gradient flows from all heads, which not only reduces training computational complexity, but also facilitates supervision to the shared layers. The empirical results on CIFAR and ImageNet datasets demonstrate that deep neural networks learned as a group in a collaborative way significantly reduce the generalization error and increase the robustness to label noise. Mutual modelling, the reciprocal ability to establish a mental model of the other, plays a fundamental role in human interactions. This complex cognitive skill is however difficult to fully apprehend as it encompasses multiple neuronal, psychological and social mechanisms that are generally not easily turned into computational models suitable for robots. This article presents several perspectives on mutual modelling from a range of disciplines, and reflects on how these perspectives can be beneficial to the advancement of social cognition in robotics. We gather here both basic tools (concepts, formalisms, models) and exemplary experimental settings and methods that are of relevance to robotics. This contribution is expected to consolidate the corpus of knowledge readily available to human-robot interaction research, and to foster interest for this fundamentally cross-disciplinary field. Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.