作者: incubus 時(shí)間: 2025-3-21 21:05 作者: 玉米 時(shí)間: 2025-3-22 03:09 作者: 確保 時(shí)間: 2025-3-22 08:26 作者: ERUPT 時(shí)間: 2025-3-22 11:28
Sequential Halving for Partially Observable Gameseering, and Phantom Go. Additionally, H-MCTS is studied, which uses Sequential Halving at the root of the search tree, and UCB elsewhere. Experimental results reveal that H-MCTS performs the best in Go Fish, whereas its performance is on par in Lost Cities and Phantom Domineering. Sequential Halving作者: harangue 時(shí)間: 2025-3-22 15:29
An Experimental Investigation on the Pancake Problem to the genome rearrangement problem also called sorting by reversals (SBR). The pancake problem is a NP-hard problem. Until now, the best theoretical R-approximation was 2 with an algorithm, which gives a 1.22 experimental R-approximation on stacks with a size inferior to 70. In the current work, w作者: harangue 時(shí)間: 2025-3-22 18:44
485 – A New Upper Bound for Morpion Solitaired of 485 moves. This is achieved in the following way: we encode Morpion 5T rules as a linear program and solve 126912 instances of this program on special octagonal boards. In order to show correctness of this method we analyze rules of the game and use a concept of a potential of a given position.作者: 障礙物 時(shí)間: 2025-3-22 23:43
The Surakarta Bot Revealedtion five times, are revealed. The paper describes its .-based variable-depth search mechanism. Search enhancements such as multi-cut forward pruning and Realization Probability Search are shown to improve the agent considerably. Additionally, features of the static evaluation function are presented作者: LIMN 時(shí)間: 2025-3-23 05:12 作者: 很是迷惑 時(shí)間: 2025-3-23 08:56 作者: 形容詞 時(shí)間: 2025-3-23 10:40
The , System: Learning Board Game Rules with Piece-Move Interactionssystems, is a time-consuming and error-prone activity. In order to counter these difficulties, efforts have been made in various communities to learn the models from input data. One learning approach is to learn models from example transition sequences. Learning state transition systems from example作者: 可行 時(shí)間: 2025-3-23 14:27
Creating Action Heuristics for General Game Playing Agentsrm well in the absence of domain knowledge. Several approaches have been proposed to add heuristics to MCTS in order to guide the simulations. In GGP those approaches typically learn heuristics at runtime from the results of the simulations. Because of peculiarities of GGP, it is preferable that the作者: 品嘗你的人 時(shí)間: 2025-3-23 18:18 作者: implore 時(shí)間: 2025-3-23 23:56
485 – A New Upper Bound for Morpion Solitaire By solving continuous-valued relaxations of linear programs on these boards, we obtain an upper bound of 586 moves. Further analysis of original, not relaxed, mixed-integer programs leads to an improvement of this bound to 485 moves. However, this is achieved at a significantly higher computational cost.作者: 詢(xún)問(wèn) 時(shí)間: 2025-3-24 06:21
On the Cross-Domain Reusability of Neural Modules for General Video Game Playingement learning domains. This approach is more general than previous approaches to transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. We analyze the method’s performance and applicability in high-dimensional Atari 2600 general video game playing.作者: Carminative 時(shí)間: 2025-3-24 06:37 作者: Delectable 時(shí)間: 2025-3-24 11:28
Conference proceedings 2016e-Playing Agents, GIGA 2015, held in conjunction with the 24th International Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, in July 2015..The 12 revised full papers presented were carefully reviewed and selected from 27 submissions. The papers address all aspects of arti作者: 小卷發(fā) 時(shí)間: 2025-3-24 17:09
Michael H. Bross,David C. Campbelllion possible positions and is stored using 500?GB of disk space. In this paper we report results from a preliminary study on how to best use the data to improve the play of a Chinese Checkers program.作者: 事情 時(shí)間: 2025-3-24 20:35
Te Puna - A New Zealand Mission Stationh in an offline setting and online while playing the game against a rule-based baseline. Experimental results show that agents trained from data from average human players can outperform rule-based trading behavior, and that the Random Forest model achieves the best results.作者: CALL 時(shí)間: 2025-3-24 23:25
Challenges and Progress on Using Large Lossy Endgame Databases in Chinese Checkerslion possible positions and is stored using 500?GB of disk space. In this paper we report results from a preliminary study on how to best use the data to improve the play of a Chinese Checkers program.作者: 無(wú)關(guān)緊要 時(shí)間: 2025-3-25 04:19
Learning to Trade in Strategic Board Gamesh in an offline setting and online while playing the game against a rule-based baseline. Experimental results show that agents trained from data from average human players can outperform rule-based trading behavior, and that the Random Forest model achieves the best results.作者: browbeat 時(shí)間: 2025-3-25 09:22
1865-0929 rth Workshop on General Intelligence in Game-Playing Agents, GIGA 2015, held in conjunction with the 24th International Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, in July 2015..The 12 revised full papers presented were carefully reviewed and selected from 27 submissi作者: ventilate 時(shí)間: 2025-3-25 12:47 作者: bacteria 時(shí)間: 2025-3-25 18:44
Te Puna - A New Zealand Mission Stationand Realization Probability Search are shown to improve the agent considerably. Additionally, features of the static evaluation function are presented. Experimental results indicate that features, which reward distribution of the pieces and penalize pieces that clutter together, give a genuine improvement in the playing strength.作者: Expressly 時(shí)間: 2025-3-25 21:56 作者: 大方不好 時(shí)間: 2025-3-26 02:18 作者: 不規(guī)則 時(shí)間: 2025-3-26 06:35
The Surakarta Bot Revealedand Realization Probability Search are shown to improve the agent considerably. Additionally, features of the static evaluation function are presented. Experimental results indicate that features, which reward distribution of the pieces and penalize pieces that clutter together, give a genuine improvement in the playing strength.作者: Interferons 時(shí)間: 2025-3-26 08:46
Space-Consistent Game Equivalence Detection in General Game Playinge equivalence formally and concentrates on a specific scale, space-consistent game equivalence (SCGE). To detect SCGE, an approach is proposed mainly reducing the complex problem to some well-studied problems. An evaluation of the approach is performed at the end.作者: Charlatan 時(shí)間: 2025-3-26 14:30 作者: 呼吸 時(shí)間: 2025-3-26 17:08
Care of the Patient Who Misuses Drugs,of polynomial-time domain-dependent simulations, MCS is a polynomial-time algorithm as well. We observed that MCS at level 3 gives a 1.04 experimental R-approximation, which is a breakthrough. At level 1, MCS solves stacks of size 512 with an experimental R-approximation value of 1.20.作者: 怕失去錢(qián) 時(shí)間: 2025-3-26 23:12 作者: Acupressure 時(shí)間: 2025-3-27 03:00
Gerald K. Goodenough M.D., M.S.P.H. By solving continuous-valued relaxations of linear programs on these boards, we obtain an upper bound of 586 moves. Further analysis of original, not relaxed, mixed-integer programs leads to an improvement of this bound to 485 moves. However, this is achieved at a significantly higher computational cost.作者: heirloom 時(shí)間: 2025-3-27 06:24 作者: NOMAD 時(shí)間: 2025-3-27 12:01 作者: 滲透 時(shí)間: 2025-3-27 15:55 作者: Precursor 時(shí)間: 2025-3-27 21:50
Communications in Computer and Information Sciencehttp://image.papertrans.cn/c/image/233545.jpg作者: 全神貫注于 時(shí)間: 2025-3-27 23:17 作者: 1FAWN 時(shí)間: 2025-3-28 04:15
Care of the Patient with Fatigue,eering, and Phantom Go. Additionally, H-MCTS is studied, which uses Sequential Halving at the root of the search tree, and UCB elsewhere. Experimental results reveal that H-MCTS performs the best in Go Fish, whereas its performance is on par in Lost Cities and Phantom Domineering. Sequential Halving作者: 完全 時(shí)間: 2025-3-28 06:47
Care of the Patient Who Misuses Drugs, to the genome rearrangement problem also called sorting by reversals (SBR). The pancake problem is a NP-hard problem. Until now, the best theoretical R-approximation was 2 with an algorithm, which gives a 1.22 experimental R-approximation on stacks with a size inferior to 70. In the current work, w作者: 歡樂(lè)中國(guó) 時(shí)間: 2025-3-28 11:22 作者: 正常 時(shí)間: 2025-3-28 17:24
Te Puna - A New Zealand Mission Stationtion five times, are revealed. The paper describes its .-based variable-depth search mechanism. Search enhancements such as multi-cut forward pruning and Realization Probability Search are shown to improve the agent considerably. Additionally, features of the static evaluation function are presented作者: Conclave 時(shí)間: 2025-3-28 19:13 作者: Abrade 時(shí)間: 2025-3-29 00:43
High Energy Neutrino Telescopes,rns in a new domain. Networks trained for a new domain are able to improve performance by selectively routing activation through previously learned neural structure, regardless of how or for what it was learned. We consider a neuroevolution implementation of the approach with application to reinforc作者: Terrace 時(shí)間: 2025-3-29 04:47
Wide Angle Air Cerenkov Detectors,systems, is a time-consuming and error-prone activity. In order to counter these difficulties, efforts have been made in various communities to learn the models from input data. One learning approach is to learn models from example transition sequences. Learning state transition systems from example作者: Aerophagia 時(shí)間: 2025-3-29 07:37
Heinrich J. V?lk,Felix A. Aharonianrm well in the absence of domain knowledge. Several approaches have been proposed to add heuristics to MCTS in order to guide the simulations. In GGP those approaches typically learn heuristics at runtime from the results of the simulations. Because of peculiarities of GGP, it is preferable that the作者: thyroid-hormone 時(shí)間: 2025-3-29 11:59 作者: 沒(méi)血色 時(shí)間: 2025-3-29 18:21 作者: 碌碌之人 時(shí)間: 2025-3-29 21:40
https://doi.org/10.1007/978-3-319-39402-2algorithms; agents; artificial intelligence; computer games; game tree search; linear programming; machine
