作者: Insufficient 時(shí)間: 2025-3-21 20:27 作者: lattice 時(shí)間: 2025-3-22 01:34 作者: colloquial 時(shí)間: 2025-3-22 08:11
Christian Kahl,Gerald Raj Sundramm, and risk associated with events, on players’ trust in the monitoring system. In this paper, we describe the game and its theoretical underpinnings, and present preliminary results from a trial where players interacted with two systems that have different levels of accuracy.作者: STERN 時(shí)間: 2025-3-22 10:39 作者: 清楚 時(shí)間: 2025-3-22 13:02 作者: 清楚 時(shí)間: 2025-3-22 20:37
Applying Anytime Heuristic Search to Cost-Optimal HTN Planning, prioritizes the most abstract tasks. They also highlight that the best heuristic formulation for the three domains is computed from linear combinations of optimistic and pessimistic cost estimations.作者: vanquish 時(shí)間: 2025-3-22 23:25 作者: Magnitude 時(shí)間: 2025-3-23 04:33
Conference proceedings 2018rence on Artificial Intelligence, IJCAI 2017, in Melbourne, Australia, in August 2017.. The 12 full papers presented in this volume were carefully reviewed and selected from 18 submissions. They cover a wide range of topics related to computer games; discussing six abstract games: Chinese Checkers, 作者: 難聽(tīng)的聲音 時(shí)間: 2025-3-23 09:17 作者: 神圣將軍 時(shí)間: 2025-3-23 12:40
1865-0929 efully reviewed and selected from 18 submissions. They cover a wide range of topics related to computer games; discussing six abstract games: Chinese Checkers, Chinese Dark Chess, Hex, Othello, Poker, and SameGame..978-3-319-75930-2978-3-319-75931-9Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: inclusive 時(shí)間: 2025-3-23 16:43
Disorders of the Back and Neck, games using a Convolutional Neural Network, the Math Kernel Library and TensorFlow framework. We also analyze effects of asynchronous computations on the convergence of reinforcement learning algorithms.作者: Clinch 時(shí)間: 2025-3-23 21:19 作者: NOCT 時(shí)間: 2025-3-24 01:04
1865-0929 onal Conference on Artificial Intelligence, IJCAI 2017, in Melbourne, Australia, in August 2017.. The 12 full papers presented in this volume were carefully reviewed and selected from 18 submissions. They cover a wide range of topics related to computer games; discussing six abstract games: Chinese 作者: 干旱 時(shí)間: 2025-3-24 05:00
https://doi.org/10.1007/978-3-662-59752-1 policy from one move to the next. We test PPAF with memorization (PPAFM) against PPAF and UCT for Atarigo, Breakthrough, Misere Breakthrough, Domineering, Misere Domineering, Knightthrough, Misere Knightthrough and Nogo.作者: DOLT 時(shí)間: 2025-3-24 10:15 作者: Pander 時(shí)間: 2025-3-24 14:40
Memorizing the Playout Policy, policy from one move to the next. We test PPAF with memorization (PPAFM) against PPAF and UCT for Atarigo, Breakthrough, Misere Breakthrough, Domineering, Misere Domineering, Knightthrough, Misere Knightthrough and Nogo.作者: 災(zāi)難 時(shí)間: 2025-3-24 16:36
A Study of Forward Versus Backwards Endgame Solvers with Results in Chinese Checkers,n a comprehensive study identifying the choice of solver enhancements. This paper represents preliminary work in this direction, exploring several types of forward and backwards solvers, and reporting preliminary results on small versions of Chinese Checkers.作者: Confound 時(shí)間: 2025-3-24 20:29 作者: FACT 時(shí)間: 2025-3-25 00:53 作者: FLEET 時(shí)間: 2025-3-25 04:44 作者: 殘廢的火焰 時(shí)間: 2025-3-25 08:44
Computer Hex Algorithm Using a Move Evaluation Method Based on a Convolutional Neural Network, professional human moves. Hex is a two-player connection game, which is included in the Computer Olympiad. It is important to consider cell adjacency on the board for a better Hex strategy. To evaluate cell adjacency from various perspectives properly, we propose a CNN model that evaluates all cand作者: Erythropoietin 時(shí)間: 2025-3-25 15:39 作者: Optometrist 時(shí)間: 2025-3-25 16:51
Deep Reinforcement Learning with Hidden Layers on Future States,ing direct evaluation of the current state and actions. This is in stark contrast to the algorithms for traditional board games such as Chess or Go, where a look-ahead search mechanism is indispensable to build a strong agent. In this paper, we present a novel deep reinforcement learning architectur作者: 狂亂 時(shí)間: 2025-3-25 23:12
Neural Fictitious Self-Play in Imperfect Information Games with Many Players,can be used to (essentially weakly) solve two-player limit Texas Hold’em, but it cannot be applied to large multi-player games due to the problem of space complexity. In this paper, we use Neural Fictitious Self-Play (NFSP) to calculate approximate Nash equilibrium solutions for imperfect informatio作者: 善于騙人 時(shí)間: 2025-3-26 02:06 作者: 記憶法 時(shí)間: 2025-3-26 08:05 作者: 小步舞 時(shí)間: 2025-3-26 08:51
Distributed Nested Rollout Policy for SameGame,uding .. In this paper, we design several parallel and distributed NRPA-based search techniques, and we provide a number of experimental insights about their execution. Finally, we use our best implementation to discover 15 better scores for 20 standard SameGame boards.作者: 繞著哥哥問(wèn) 時(shí)間: 2025-3-26 13:57
A Study of Forward Versus Backwards Endgame Solvers with Results in Chinese Checkers,hile significant work has been done on building solvers for many games, including Chess and Checkers, we were surprised to find that there has not been a comprehensive study identifying the choice of solver enhancements. This paper represents preliminary work in this direction, exploring several typ作者: 懶惰民族 時(shí)間: 2025-3-26 18:20
Validating and Fine-Tuning of Game Evaluation Functions Using Endgame Databases,designed to assess a position by considering the location and the material value of all pieces on board. Normally, an evaluation function . is manually designed, which requires a large amount of expert knowledge. Usually, . must be able to evaluate any position. Theoretically, a huge table that stor作者: Anticoagulants 時(shí)間: 2025-3-26 22:54
Applying Anytime Heuristic Search to Cost-Optimal HTN Planning,nch-and-bound with a heuristic search, which can also be used as a near-optimal algorithm given a time limit. It also includes different heuristics based on weighted cost estimations and different decomposition strategies. The different elements from this framework are empirically evaluated on three作者: ostrish 時(shí)間: 2025-3-27 02:31
A Game for Eliciting Trust Between People and Devices Under Diverse Performance Conditions,etirement village, where residents live in smart homes equipped with monitoring systems. Players, who “work” in the village, need to trade-off the time spent on administrative tasks (which enable them to earn extra income) against the time spent ensuring the welfare of the residents. The scenario of作者: disciplined 時(shí)間: 2025-3-27 07:12 作者: 精美食品 時(shí)間: 2025-3-27 13:31 作者: Flagging 時(shí)間: 2025-3-27 17:06
Disorders of the Back and Neck,m exceptionally suitable for CPU computations. However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions..In this work we present our results on learning strategies in Atari作者: 動(dòng)作謎 時(shí)間: 2025-3-27 18:54 作者: 開(kāi)頭 時(shí)間: 2025-3-28 00:00 作者: ADORN 時(shí)間: 2025-3-28 02:25
https://doi.org/10.1007/978-3-662-59752-1ing direct evaluation of the current state and actions. This is in stark contrast to the algorithms for traditional board games such as Chess or Go, where a look-ahead search mechanism is indispensable to build a strong agent. In this paper, we present a novel deep reinforcement learning architectur作者: CULP 時(shí)間: 2025-3-28 09:38 作者: 帳單 時(shí)間: 2025-3-28 12:23 作者: sperse 時(shí)間: 2025-3-28 17:08 作者: ovation 時(shí)間: 2025-3-28 22:24 作者: 爆炸 時(shí)間: 2025-3-29 00:14 作者: 錢財(cái) 時(shí)間: 2025-3-29 04:28
Shantini Thuraiselvam,Rui Bao Thangdesigned to assess a position by considering the location and the material value of all pieces on board. Normally, an evaluation function . is manually designed, which requires a large amount of expert knowledge. Usually, . must be able to evaluate any position. Theoretically, a huge table that stor作者: 聽(tīng)寫 時(shí)間: 2025-3-29 09:31 作者: 委屈 時(shí)間: 2025-3-29 15:25 作者: 戲法 時(shí)間: 2025-3-29 16:16
Distributed Nested Rollout Policy for SameGame,uding .. In this paper, we design several parallel and distributed NRPA-based search techniques, and we provide a number of experimental insights about their execution. Finally, we use our best implementation to discover 15 better scores for 20 standard SameGame boards.作者: 好色 時(shí)間: 2025-3-29 21:43
https://doi.org/10.1007/978-3-319-75931-9artificial intelligence; 3D; computer games; computer graphics; computer vision; game theory; image analys作者: Limerick 時(shí)間: 2025-3-30 02:11
978-3-319-75930-2Springer International Publishing AG, part of Springer Nature 2018作者: mighty 時(shí)間: 2025-3-30 04:02
Communications in Computer and Information Sciencehttp://image.papertrans.cn/c/image/233544.jpg作者: 索賠 時(shí)間: 2025-3-30 10:59
Computer Hex Algorithm Using a Move Evaluation Method Based on a Convolutional Neural Network,ow that the proposed model can be used as a better ordering function than the ordering function created by minimax tree optimization, and we obtained a win rate of 49.0% against MoHex 2.0 (30?s/move).作者: Hectic 時(shí)間: 2025-3-30 15:07
