Abstract-Automated planning has achieved significant breakthroughs in recent years. Nonetheless, attempts to improve search algorithm efficiency remain the primary focus of most research. However, it is also possible to build on previous searches and learn from previously found solutions. Our approach consists in learning macro-actions and adding them into the planner's domain. A macro-action is an action sequence selected for application at search time and applied as a single indivisible action. Carefully chosen macros can drastically improve the planning performances by reducing the search space depth. However, macros also increase the branching factor. Therefore, the use of macros entails a utility problem: a trade-off has to be addressed between the benefit of adding macros to speed up the goal search and the overhead caused by increasing the branching factor in the search space. In this paper, we propose an online domain and plannerindependent approach to learn 'useful' macros, i.e. macros that address the utility problem. These useful macros are obtained by statistical and heuristic filtering of a domain specific macro library. The library is created from the most frequent action sequences derived from an n-gram analysis on successful plans previously computed by the planner. The relevance of this approach is proven by experiments on International Planning Competition domains.
Inspiré par les pratiques d'établissements prestigieux (notamment au MIT), le projet pédagogique ici présenté vise à fournir un support attractif tel que les robots pour l'enseignement de l'intelligence artificielle au travers de projets interdisciplinaires et à améliorer la réussite des étudiants en Licence et en Master. En effet, en impliquant les étudiants dans des projets leur permettant de découvrir les différentes thématiques enseignées en Master, nous souhaitons intéresser les étudiants de licence et les accompagner dans la construction de leur projet de formation afin qu'il leur corresponde au mieux. L'étude comparative présentée ici a permis d'étudier les effets de l'utilisation de robots en pédagogie de projet sur la motivation et l'investissement des étudiants.
Summarizing texts is not a straightforward task. Before even considering text summarization, one should determine what kind of summary is expected. How much should the information be compressed? Is it relevant to reformulate or should the summary stick to the original phrasing? State-of-the-art on automatic text summarization mostly revolves around news articles. We suggest that considering a wider variety of tasks would lead to an improvement in the field, in terms of generalization and robustness. We explore meeting summarization: generating reports from automatic transcriptions. Our work consists in segmenting and aligning transcriptions with respect to reports, to get a suitable dataset for neural summarization. Using a bootstrapping approach, we provide pre-alignments that are corrected by human annotators, making a validation set against which we evaluate automatic models. This consistently reduces annotators' efforts by providing iteratively better pre-alignment and maximizes the corpus size by using annotations from our automatic alignment models. Evaluation is conducted on public_meetings, a novel corpus of aligned public meetings. We report automatic alignment and summarization performances on this corpus and show that automatic alignment is relevant for data annotation since it leads to large improvement of almost +4 on all ROUGE scores on the summarization task.
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