Abstract. Monte-Carlo Tree Search (MCTS) is a popular technique for playing multi-player games. In this paper, we propose a new method to bias the playout policy of MCTS. The idea is to prune the decisions which seem "bad" (according to the previous iterations of the algorithm) before computing each playout. Thus, the method evaluates the estimated "good" moves more precisely. We have tested our improvement for the game of Havannah and compared it to several classic improvements. Our method outperforms the classic version of MCTS (with the RAVE improvement) and the different playout policies of MCTS that we have experimented.

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Ever-increasing quantities of personal data are generated by individuals, knowingly or unconsciouly, actively or passively (e.g., bank transactions, geolocations, posts on web forums, physiological measures captured by wearable sensors). Most of the time, this wealth of information is stored, managed, and valorized in isolated systems owned by private companies or organizations. Personal information management systems (PIMS) propose a groundbreaking counterpoint to this trend. They essentially aim at providing to any interested individual the technical means to recollect , manage, integrate, and valorize his/her own data through a dedicated system that he/she owns and controls. In this vision paper, we consider personal preferences as first-class citizens data structures. We define and motivate the threefold preference elicitation problem in PIMS-elicitation from local personal data, elicitation from group preferences, and elicitation from user interactions. We also identify hard and diverse challenges to tackle (e.g., small data, context acquisition, small-scale recommendation, low computing resources, data privacy) and propose promising research directions. Overall, we hope that this paper uncovers an exciting and fruitful research track.

Specialized worker profiles of crowdsourcing platforms may contain a large amount of identifying and possibly sensitive personal information (e.g., personal preferences, skills, available slots, available devices) raising strong privacy concerns. This led to the design of privacypreserving crowdsourcing platforms, that aim at enabling efficient crowdsourcing processes while providing strong privacy guarantees even when the platform is not fully trusted. In this paper, we propose two contributions. First, we propose the PKD algorithm with the goal of supporting a large variety of aggregate usages of worker profiles within a privacypreserving crowdsourcing platform. The PKD algorithm combines together homomorphic encryption and differential privacy for computing (perturbed) partitions of the multi-dimensional space of skills of the actual population of workers and a (perturbed) COUNT of workers per partition. Second, we propose to benefit from recent progresses in Private Information Retrieval techniques in order to design a solution to task assignment that is both private and affordable. We perform an in-depth study of the problem of using PIR techniques for proposing tasks to workers, show that it is NP-Hard, and come up with the PKD PIR Packing heuristic that groups tasks together according to the partitioning output by the PKD algorithm. In a nutshell, we design the PKD algorithm and the PKD PIR Packing heuristic, we prove formally their security against honest-butcurious workers and/or platform, we analyze their complexities, and we demonstrate their quality and affordability in real-life scenarios through an extensive experimental evaluation performed over both synthetic and realistic datasets.