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.
This paper focuses on the processes involved in collaboration using a microanalysis of one dyad’s work with a computer-based environment (the Envisioning Machine). The interaction between participants is analysed with respect to a ‘Joint Problem Space’, which comprises an emergent, socially-negotiated set of knowledge elements, such as goals, problem state descriptions and problem solving actions. Our analysis shows how this shared conceptual space is constructed through the external mediational framework of shared language, situation and activity. This approach has particular implications for understanding how the benefits of collaboration are realised and serves to clarify the possible roles of the computers in supporting collaborative learning. This book arises from a series of workshops on collaborative learning, that gathered together 20 scholars from the disciplines of psychology, education and computer science. The series was part of a research program entitled 'Learning in Humans and Machines' (LHM), launched by Peter Reimann and Hans Spada, and funded by the European Science Foundation. This program aimed to develop a multidisciplinary dialogue on learning, involving mainly scholars from cognitive psychology, educational science, and artificial intelligence (including machine learning). During the preparation of the program, Agnes Blaye, Claire O'Malley, Michael Baker and I developed a theme on collaborative learning. When the program officially began, 12 members were selected to work on this theme and formed the so-called 'task force 5'. I became the coordinator of the group. This group organised two workshops, in Sitges (Spain, 1994) and Aix-en-Provence (France, 1995). In 1996, the group was enriched with new members to reach its final size. Around 20 members met in the subsequent workshops, at Samoens (France, 1996), Houthalen (Belgium, 1996) and Mannheim (Germany, 1997). Several individuals joined the group for some time but have not written a chapter. I would nevertheless like to acknowledge their contributions to our activities: George Bilchev, Stevan Harnad, Calle Jansson and Claire O'Malley. 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.