2013 IEEE Conference on Computational Inteligence in Games (CIG)最新文献

Bandits all the way down: UCB1 as a simulation policy in Monte Carlo Tree Search 强盗一路下来:UCB1作为蒙特卡洛树搜索的模拟策略

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633613

E. Powley, D. Whitehouse, P. Cowling

{"title":"Bandits all the way down: UCB1 as a simulation policy in Monte Carlo Tree Search","authors":"E. Powley, D. Whitehouse, P. Cowling","doi":"10.1109/CIG.2013.6633613","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633613","url":null,"abstract":"Monte Carlo Tree Search (MCTS) is a family of asymmetric anytime aheuristic game tree search algorithms which have advanced the state-of-the-art in several challenging domains. MCTS learns a playout policy, iteratively building a partial tree to store and further refine the learned portion of the policy. When the playout leaves the existing tree, it falls back to a default simulation policy, which for many variants of MCTS chooses actions uniformly at random. This paper investigates how a simulation policy can be learned during the search, helping the playout policy remain plausible from root to terminal state without the injection of prior knowledge. Since the simulation policy visits states that are previously unseen, its decisions cannot be as context sensitive as those in the tree policy. We consider the well-known Move-Average Sampling Technique (MAST), which learns a value for each move which is independent of context. We also introduce a generalisation of MAST, called N-gram-Average-Sampling-Technique (NAST), which uses as context a fixed-lengthsequence (or N-tuple) of recent moves. We compare several policies for selecting moves during simulation, including the UCB1 policy for multi-armed bandits (as used in the tree policy for the popular UCT variant of MCTS). In addition to the elegance of treating the entire playout as a series of multi-armed bandit problems, we find that UCB1 gives consistently strong performance. We present empirical results for three games of imperfect information, namely the card games Dou Di Zhu and Hearts and the board game Lord Of The Rings: The Confrontation, each of which has its own unique challenges for search-based AI.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128795754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Landscape automata for search based procedural content generation 基于搜索过程内容生成的景观自动机

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633619

D. Ashlock, C. McGuinness

引用次数: 19

Towards story-based content generation: From plot-points to maps 面向基于故事的内容生成:从情节点到地图

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633654

Josep Valls-Vargas, Santiago Ontañón, Jichen Zhu

引用次数: 20

AI for game production 游戏制作中的AI

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633663

Mark O. Riedl, Alexander Zook

引用次数: 33

Behavioral-based cheating detection in online first person shooters using machine learning techniques 使用机器学习技术的在线第一人称射击游戏中基于行为的作弊检测

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633617

Hashem Alayed, Fotos Frangoudes, C. Neuman

引用次数: 30

Mobile games with intelligence: A killer application? 智能手机游戏:杀手级应用?

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633660

P. Hingston, C. Congdon, G. Kendall

引用次数: 6

The structure of a 3-state finite transducer representation for Prisoner's Dilemma 囚徒困境的三态有限换能器表示结构

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633638

Jeffrey Tsang

引用次数: 8

A video game description language for model-based or interactive learning 用于基于模型或交互式学习的电子游戏描述语言

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633610

T. Schaul

{"title":"A video game description language for model-based or interactive learning","authors":"T. Schaul","doi":"10.1109/CIG.2013.6633610","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633610","url":null,"abstract":"We propose a powerful new tool for conducting research on computational intelligence and games. `PyVGDL' is a simple, high-level description language for 2D video games, and the accompanying software library permits parsing and instantly playing those games. The streamlined design of the language is based on defining locations and dynamics for simple building blocks, and the interaction effects when such objects collide, all of which are provided in a rich ontology. It can be used to quickly design games, without needing to deal with control structures, and the concise language is also accessible to generative approaches. We show how the dynamics of many classical games can be generated from a few lines of PyVGDL. The main objective of these generated games is to serve as diverse benchmark problems for learning and planning algorithms; so we provide a collection of interfaces for different types of learning agents, with visual or abstract observations, from a global or first-person viewpoint. To demonstrate the library's usefulness in a broad range of learning scenarios, we show how to learn competent behaviors when a model of the game dynamics is available or when it is not, when full state information is given to the agent or just subjective observations, when learning is interactive or in batch-mode, and for a number of different learning algorithms, including reinforcement learning and evolutionary search.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116062154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 207

PSMAGE: Balanced map generation for StarCraft PSMAGE:星际争霸的平衡地图生成

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633644

Alberto Uriarte, Santiago Ontañón

引用次数: 14

Deductive search for logic puzzles 逻辑谜题的演绎搜索

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633649

C. Browne

引用次数: 15