实时游戏的多目标蒙特卡罗树搜索

Diego Perez Liebana, Sanaz Mostaghim, Spyridon Samothrakis, S. Lucas
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引用次数: 24

摘要

传统上,多目标优化是工程或金融等领域的研究课题,对游戏研究影响不大。然而,为了获得高质量的游戏水平,基于多目标评估的行动决策可能是有益的。本文提出了一种多目标蒙特卡罗树搜索算法,用于实时博弈域的规划和控制,当决策下一步行动的时间预算接近40毫秒时。将该算法与蒙特卡罗树搜索的单目标版本和滚动视界实现的非支配排序进化算法II (NSGA-II)进行了比较。本文采用了深海宝藏(DST)和多目标物理旅行商问题(mo - pstp)两种不同的基准。在每个游戏中使用相同的启发式,分析的重点是算法如何探索搜索空间。结果表明,该算法优于NSGA-II。此外,还表明该算法能够收敛到不同的最优解或最优帕累托前沿(如果在搜索过程中实现)。
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Multiobjective Monte Carlo Tree Search for Real-Time Games
Multiobjective optimization has been traditionally a matter of study in domains like engineering or finance, with little impact on games research. However, action-decision based on multiobjective evaluation may be beneficial in order to obtain a high quality level of play. This paper presents a multiobjective Monte Carlo tree search algorithm for planning and control in real-time game domains, those where the time budget to decide the next move to make is close to 40 ms. A comparison is made between the proposed algorithm, a single-objective version of Monte Carlo tree search and a rolling horizon implementation of nondominated sorting evolutionary algorithm II (NSGA-II). Two different benchmarks are employed, deep sea treasure (DST) and the multiobjective physical traveling salesman problem (MO-PTSP). Using the same heuristics on each game, the analysis is focused on how well the algorithms explore the search space. Results show that the algorithm proposed outperforms NSGA-II. Additionally, it is also shown that the algorithm is able to converge to different optimal solutions or the optimal Pareto front (if achieved during search).
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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