作者: 懦夫 時(shí)間: 2025-3-21 21:42 作者: Diastole 時(shí)間: 2025-3-22 01:27 作者: linguistics 時(shí)間: 2025-3-22 07:57
Matching Techniques, Algorithms, and Systemsiction system of Latent Factor Ranking (LFR). In this paper, we investigate the problem of integrating feature knowledge learned by FBT model in Monte Carlo Tree Search. We use the open source Go program Fuego as the test platform. Experimental results show that the FBT knowledge is useful in improv作者: MAL 時(shí)間: 2025-3-22 12:24
Matching Evaluations and Datasetsed Rollout Policy Adaptation (NRPA) is an MCTS variant that has found record-breaking solutions for puzzles and optimization problems. It learns a playout policy online that dynamically adapts the playouts to the problem at hand. We propose to enhance NRPA using more selectivity in the playouts. The作者: 難理解 時(shí)間: 2025-3-22 15:01 作者: 難理解 時(shí)間: 2025-3-22 19:42
Matching Techniques, Algorithms, and Systemstari 2600. We consider four models of neural networks which differ in size and architecture: two networks which use only information contained in the RAM and two mixed networks which use both information in the RAM and information from the screen..As the benchmark we used the convolutional model pro作者: mendacity 時(shí)間: 2025-3-22 21:18
https://doi.org/10.1007/978-981-19-8645-1understanding of which aspects of game states influence game-play. This paper presents a clustering and locally weighted regression method for modeling and imitating individual players. The algorithm first learns a generic player cluster model that is updated online to capture an individual’s game-p作者: Host142 時(shí)間: 2025-3-23 05:24 作者: 可商量 時(shí)間: 2025-3-23 06:32 作者: 暫時(shí)別動(dòng) 時(shí)間: 2025-3-23 11:36
Jean Vacelet,Nicole Boury-Esnaultions to play in the game. One method for interpreting the game rules consists of translating the GDL game description into an alternative representation that the player can use to reason more efficiently on the game. The Propositional Network (PropNet) is an example of such method. The use of PropNe作者: FEAT 時(shí)間: 2025-3-23 17:04 作者: 四目在模仿 時(shí)間: 2025-3-23 20:42 作者: 微生物 時(shí)間: 2025-3-23 22:19 作者: Throttle 時(shí)間: 2025-3-24 04:04 作者: Carbon-Monoxide 時(shí)間: 2025-3-24 09:59 作者: 嗎啡 時(shí)間: 2025-3-24 14:19 作者: 輕率的你 時(shí)間: 2025-3-24 18:23 作者: 非實(shí)體 時(shí)間: 2025-3-24 19:34
Computer Games978-3-319-57969-6Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 大吃大喝 時(shí)間: 2025-3-25 01:58 作者: 符合國情 時(shí)間: 2025-3-25 03:58 作者: ARM 時(shí)間: 2025-3-25 08:57 作者: 愛好 時(shí)間: 2025-3-25 12:27
Integrating Factorization Ranked Features in MCTS: An Experimental Studyiction system of Latent Factor Ranking (LFR). In this paper, we investigate the problem of integrating feature knowledge learned by FBT model in Monte Carlo Tree Search. We use the open source Go program Fuego as the test platform. Experimental results show that the FBT knowledge is useful in improv作者: Hemiplegia 時(shí)間: 2025-3-25 19:19
Nested Rollout Policy Adaptation with Selective Policiesed Rollout Policy Adaptation (NRPA) is an MCTS variant that has found record-breaking solutions for puzzles and optimization problems. It learns a playout policy online that dynamically adapts the playouts to the problem at hand. We propose to enhance NRPA using more selectivity in the playouts. The作者: 增強(qiáng) 時(shí)間: 2025-3-25 20:36
A Rollout-Based Search Algorithm Unifying MCTS and Alpha-Betang moves. In order to combine the strategic strength of MCTS and the tactical strength of minimax, . have been introduced in prior work, embedding shallow minimax searches into the MCTS framework. This paper continues this line of research by integrating MCTS and minimax even more tightly into one r作者: mortgage 時(shí)間: 2025-3-26 04:03 作者: inspiration 時(shí)間: 2025-3-26 05:44 作者: Mere僅僅 時(shí)間: 2025-3-26 10:24
AI Wolf Contest — Development of Game AI Using Collective Intelligence —sent the Werewolf game as a standard game problem in the AI field. It is similar to game problems such as Chess, Shogi, Go, and Poker. The Werewolf game is a communication game that requires several AI technologies such as multi-agent coordination, intentional reading, and understanding of the theor作者: 平息 時(shí)間: 2025-3-26 12:51 作者: epicardium 時(shí)間: 2025-3-26 19:16 作者: 健忘癥 時(shí)間: 2025-3-27 00:50
A General Approach of Game Description Decomposition for General Game Playingn be significantly sped up by the decomposition of the problem in sub-problems analyzed separately. Our program can decompose game descriptions with any number of players while addressing the problem of joint moves. This approach is used to identify perfectly separable sub-games but can also decompo作者: 使服水土 時(shí)間: 2025-3-27 01:28
1865-0929 r games; combinatorial game theory; applications; computational creativity; computational game theory; evaluation and analysis; game design; knowledge representation; machine learning; multi-agent systems; opponent modeling; planning.?.978-3-319-57968-9978-3-319-57969-6Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: obstruct 時(shí)間: 2025-3-27 05:22 作者: CHIDE 時(shí)間: 2025-3-27 11:15 作者: indifferent 時(shí)間: 2025-3-27 14:33
A Rollout-Based Search Algorithm Unifying MCTS and Alpha-Betanode—by increasing the MCTS value estimates for the best move found by alpha-beta. A single parameter, the probability of executing MCTS rollouts vs. alpha-beta rollouts, makes it possible for the hybrid to subsume both MCTS as well as alpha-beta search as extreme cases, while allowing for a spectru作者: probate 時(shí)間: 2025-3-27 19:40 作者: 埋葬 時(shí)間: 2025-3-27 22:12 作者: depreciate 時(shí)間: 2025-3-28 03:17
https://doi.org/10.1007/978-981-19-8645-1lay tendencies. The models can then be used to play the game or for analysis to identify how different players react to separate aspects of game states. The method is demonstrated on a tablet-based trajectory generation game called ..作者: 自制 時(shí)間: 2025-3-28 07:35 作者: meditation 時(shí)間: 2025-3-28 12:00
Conference proceedings 2017ing Agents, GIGA 2016, held in conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, in New York, USA, in July 2016.The 12 revised full papers presented were carefully reviewed and selected from 25 submissions. The papers address all aspects of artificial intelli作者: 極大的痛苦 時(shí)間: 2025-3-28 17:54 作者: gusher 時(shí)間: 2025-3-28 21:56
Taxonomy Matching Using Background Knowledgeystem trains a Q-network capable of strong play with no search. After two weeks of Q-learning, NeuroHex achieves respective win-rates of 20.4% as first player and 2.1% as second player against a 1-s/move version of MoHex, the current ICGA Olympiad Hex champion. Our data suggests further improvement might be possible with more training time.作者: 孤僻 時(shí)間: 2025-3-29 00:32 作者: PANIC 時(shí)間: 2025-3-29 03:28
Learning from the Memory of Atari 2600posed in [.] and received comparable results in all considered games. Quite surprisingly, in the case of Seaquest we were able to train RAM-only agents which behave better than the benchmark screen-only agent. Mixing screen and RAM did not lead to an improved performance comparing to screen-only and RAM-only agents.作者: multiply 時(shí)間: 2025-3-29 10:55
Clustering-Based Online Player Modelinglay tendencies. The models can then be used to play the game or for analysis to identify how different players react to separate aspects of game states. The method is demonstrated on a tablet-based trajectory generation game called ..作者: 親密 時(shí)間: 2025-3-29 15:10
A General Approach of Game Description Decomposition for General Game Playingse serial games composed of two subgames and games with compound moves while avoiding, unlike previous works, to rely on syntactic elements that can be eliminated by simply rewriting the GDL rules. We tested our program on 40 games, compound or not, and we can decompose 32 of them successfully in less than 5?s.作者: 拾落穗 時(shí)間: 2025-3-29 15:49
1865-0929 Workshop, CGW 2016, and the 5th Workshop on General Intelligence in Game-Playing Agents, GIGA 2016, held in conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, in New York, USA, in July 2016.The 12 revised full papers presented were carefully reviewed and selec作者: overbearing 時(shí)間: 2025-3-29 21:02
Matching Evaluations and Datasetsyout policy online that dynamically adapts the playouts to the problem at hand. We propose to enhance NRPA using more selectivity in the playouts. The idea is applied to three different problems: Bus regulation, SameGame and Weak Schur numbers. We improve on standard NRPA for all three problems.
