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Ever since its introduction, Monte Carlo Tree Search (MCTS) has shown very good performances on a number of games, most of which are turn-based zero-sum games. More recently, researchers have also started to expand the application of MCTS to other types of games. This paper proposes a new framework of applying MCTS to the game of simulated car racing. We choose to build the search tree in a discretized...
In this paper, we propose an efficient approach to identify the opinion leader from group discussion. This approach is able to recognize the opinion leader without analyzing semantic and syntactic features, which may cost a lot more computing effort. We firstly propose algorithms to evaluate the degree of participation and the emotion expression from the speaking of each member during group discussion...
In this work, we address the problem of transfer learning for sequential recommendation model. Most of the state-of-the-art recommendation systems consider user preference and give customized results to different users. However, for those users without enough data, personalized recommendation systems cannot infer their preferences well or rank items precisely. Recently, transfer learning techniques...
This paper improves upon Job-Level (JL) computing, a general distributed computing approach. In JL computing, a client maintains the overall search tree and parcels the overall search into coarse-grained jobs, which are then each calculated by pre-existing game-playing programs. In order to support large-scale problems such as solving 7×7 killall-Go, or building opening books for 9×9 Go or Connect6,...
Inferring activities on smartphones is a challenging task. Prior works have elaborated on using sensory data from built-in hardware sensors in smartphones or taking advantage of location information to understand human activities. In this paper, we explore two types of data on smartphones to conduct activity inference: 1) Spatial-Temporal: reflecting daily routines from the combination of spatial...
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