Interactive Interpretation of Serial Episodes: Experiments in musical analysis

National audienceThe context of this work is the study of sequential data that can be represented with sequences of timestamped events. The aim is to explore these sequences with sequence mining to discover serial episodes which are frequent event subsequences that occur frequently in data (Mannila et al., 1997). The domain of melodic analysis is studied in this work : the aim is to highlight the structure of a musical piece by discovering its main melodic patterns. The episodes produced by the miner are examined by a user generally an expert of the domain who have to identify relevant episodes and interpret them. Meanwhile in the interpretation step, the user has to face to a recurrent overabundance of mining's results which makes difficult the identification of interesting ones. There is a real need to adopt a rigorous approach to methodically manage this step and assist the user's work. For this, we propose a visual and interactive approach to assist the interpretation of serial episodes. An Interactive approach to the interpretation of serial episodes We propose to assist the interpretation task by managing combinatorial redundancy in order to focus on relevant episodes. The assistance combines iteratively ranking and filtering useless episodes to help focusing on relevant ones. It has been exemplified in the Transmute prototype, a web-based application enabling user's interaction with events sequences and serial episodes that are represented graphically on a timeline with customisable icons. The interpretation process consists in the main iterative steps : ranking, selection and filtering. The user can choose measures to rank episodes and then select among them to display their occurrences in the sequence. When a choice is made, a filtering process is triggered to clean up other episodes that can no longer be selected following the previous selections of the user. Finally, the user can interpret the episodes by attaching them annotations and record the model resulting from the interpretation into a knowledge base. The ranking of episodes is performed thanks to several objective interestingness measures which estimate the relative importance and compactness of the episodes in the sequence. The first measure is the event coverage indicator which is the number of distinct events of the occurrences of an episode. The second measure is the spreading indicator which is the number of events of the sequence in the time intervals of the episode occurrences. The noise indicator is the difference between these two previous indicators and corresponds to the number of events of the sequence in the time intervals of the episode occurrences. Temporal measures may also be used when event duration are known. The selection of an episode by the user triggers the filtering process which is based on the event coverage of the selected episode. The remaining episodes are examined and occurrences having at least an event in common with the event coverage are discarded. The support is consequently updated and episodes whose support becomes less than the given frequency threshold are discarded. This results in removing combinatorial redundancy around the chosen episode and leads to a gradual diminution of the remaining episodes, allowing to the user a better focus on other episodes

Social Robots in Collaborative Learning: Consequences of the Design on Students' Perception

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Social Robot as an Awareness Tool to Help Regulate Collaboration

International audienceIn collaborative learning, group awareness is a central issue. Being aware of the group's perceptions allows an adequate regulation of the activity. Our research explores whether a social robot can provide the necessary awareness. This study evaluates the usability and acceptability of a social robot used in such a role. The robot can express emotions and move according to territoriality principles, leading to a novel communication strategy. We designed and evaluated a learning situation where a Cozmo robot is included in a project meeting. As an awareness tool, it moves and expresses specific emotions that represent individual and group feelings to regulate learner communication behaviors

Collaborative Knowledge Acquisition under Control of a Non-Regression Test System

14 pagesInternational audienceThis paper introduces BeGoood, a generic system for man- aging non-regression tests on knowledge-bases. BeGoood is a system al- lowing to define test plans in order to monitor the evolution of knowledge- bases. Any system answering queries by providing results in the form of set of strings can be tested with BeGoood. BeGoood has been devel- oped following a REST architecture and is independent of any applica- tion domain. This paper describes the architecture of the system and gives a use case to illustrate how BeGoood is able to manage a collab- orative knowledge evolution in the framework of a case-based reasoning system

Man-Machine Collaboration to Acquire Cooking Adaptation Knowledge for the TAAABLE Case-Based Reasoning System

International audienceThis paper shows how humans and machines can better collaborate to acquire adaptation knowledge (AK) in the framework of a case-based reasoning (CBR) system whose knowledge is encoded in a semantic wiki. Automatic processes like the CBR reasoning process itself, or speci c tools for acquiring AK are integrated as wiki extensions. These tools and processes are combined on purpose to collect AK. Users are at the center of our approach, as they are in a classical wiki, but they will now bene t from automatic tools for helping them to feed the wiki. In particular, the CBR system, which is currently only a consumer for the knowledge encoded in the semantic wiki, will also be used for producing knowledge for the wiki. A use case in the domain of cooking is given to exemplify the man-machine collaboration

Découverte opportuniste de connaissances d'adaptation

National audienceL'étape d'adaptation est souvent considérée comme le talon d'Achille du raisonnement à partir de cas car elle nécessite des connaissances spécifiques au domaine d'application qui sont difficiles à acquérir. Dans ce papier, deux stratégies sont combinées pour faciliter la tâche d'acquisition de connaissances d'adaptation : les connaissances d'adaptation sont apprises à partir de la base de cas par des techniques d'extraction de connaissances, et l'acquisition de connaissances d'adaptation est déclenchée de manière opportuniste au cours d'une session particulière de résolution de problèmes

Towards an operator for merging taxonomies

International audienceThe merging of knowledge bases is a fundamental part of the collaboration in continuous knowledge construction. This paper introduces an operator for merging similar taxonomies, i.e. taxonomies that share the major part of their contents. Taxonomies have been chosen for the low time and space complexity of the classical inferences defined on them. A limit of this language is that it does not incorporate negations, thus the union of taxonomies is never inconsistent, though it is meaningful to consider that their merging does not coincide with their union. Thus, a way to extend the taxonomies' language is presented to allow the definition of a merging operator. This operator is algorithmically simple for the part of their contents on which the taxonomies agree, confining complexity to the part on which they do not. So it allows a low time and space complexity merging on similar taxonomies

Developmental Learning for Social Robots in Real-World Interactions

International audienceThis paper reports preliminary research work on applying developmental learning to social robotics for making human-robot interactions more instinctive and more natural. Developmental learning is an unsupervised learning strategy relying on the fact that the learning agent is intrinsically motivated, and is able to incrementally build its own representation of the world through its experiences of interaction with it. Our claim is that using developmental learning in social robots could dramatically change the way we envision human-robot interaction, notably by giving the robot an active role in the interaction building process, and even more importantly, in the way it autonomously learns suitable behaviors over time. Developmental learning appears to be an appropriate approach to develop a form of "interactional intelligence" for social robots. In this work, our goal was to set up a common framework for implementing, experimenting and evaluating developmental learning algorithms with various social robots

Acquisition de connaissances du domaine d'un système de RàPC : une approche fondée sur l'analyse interactive des échecs d'adaptation --- le système FrakaS

National audienceUn système de raisonnement à partir de cas (RàPC) s'appuie sur des connaissances du domaine, en plus de la base de cas. L'acquisition de nouvelles connaissances du domaine doit améliorer les résultats d'un tel système. Cet article présente une approche pour une telle acquisition de connaissances qui est fondée sur les échecs du système. Le système de RàPC considéré est supposé produire des solutions qui sont cohérentes avec les connaissances du domaine mais mais ces solutions peuvent être incohérentes avec les connaissances de l'expert et cette incohérence constitue une situation d'échec. Grâce à une analyse interactive de cet échec, des connaissances sont acquises qui contribuent à remplir le fossé existant entre les connaissances du système et celles de l'expert. Un autre type d'échec apparait quand la solution présentée par le système n'est que partielle : certaines informations additionnelles sont requises pour pouvoir exploiter cette solution. Une fois de plus, l'interaction avec l'expert entraîne une acquisition de nouvelles connaissances. Cette approche a été implantée dans un prototype, baptisé FrakaS, et testé sur un exemple dans le domaine d'application de l'aide à la décision thérapeutique en cancérologie du sein