Evaluating Go game records for prediction of player attributes

2007 IEEE Symposium on Computational Intelligence and Games, 2007

Accuracy of evaluation functions is one of the critical factors in computer game players. Evaluation functions are usually constructed manually as a weighted linear combination of evaluation features that characterize game positions. Selecting evaluation features and tuning their weights require deep knowledge of the game and largely alleviates such efforts.

International Journal of Simulation, 2001

Advances in Intelligent Systems and Computing

Electronic sports have become the absolute choice for players, and these days just like onlookers, backing a worldwide media outlet. Esports examination has advanced to address the necessity for information-driven criticism and is centred around digital competitor assessment, methodology and expectation. Previous researches have utilized game data from a diversity of player ranking from casual (non-professionals) to proficient. However, proficient players had carried on uniquely in contrast to hobbyist and less skilled players. Given the nearly constrained stockpile of expert information, a key inquiry is in this way whether the given match dataset can be utilized to make information-driven models which foresee winners in accomplished matches and give an actual in-game stats for spectators and broadcasters to see. Here we will display that stats, even though there is a somewhat less accuracy, the acquired data has been utilized for anticipating the result of skilled matches, with appropriately improved configurations.

Go is an ancient board game that poses unique opportunities and challenges for artificial intelligence. Currently, there are no computer Go programs that can play at the level of a good human player. However, the emergence of large repositories of games is opening the door for new machine learning approaches to address this challenge. Here we develop a machine learning approach to Go, and related board games, focusing primarily on the problem of learning a good evaluation function in a scalable way. Scalability is essential at multiple levels, from the library of local tactical patterns, to the integration of patterns across the board, to the size of the board itself. The system we propose is capable of automatically learning the propensity of local patterns from a library of games. Propensity and other local tactical information are fed into recursive neural networks, derived from a probabilistic Bayesian network architecture. The recursive neural networks in turn integrate local information across the board in all four cardinal directions and produce local outputs that represent local territory ownership probabilities. The aggregation of these probabilities provides an effective strategic evaluation function that is an estimate of the expected area at the end, or at various other stages, of the game. Local area targets for training can be derived from datasets of games played by human players. In this approach, while requiring a learning time proportional to N 4 , skills learned on a board of size N 2 can easily be transferred to boards of other sizes. A system trained using only 9 × 9 amateur game data performs surprisingly well on a test set derived from 19 × 19 professional game data. Possible directions for further improvements are briefly discussed.

The 16th International Conference on the Foundations of Digital Games (FDG) 2021, 2021

League of Legends (LoL) is the most widely played multiplayer online battle arena (MOBA) game in the world. An important aspect of LoL is competitive ranked play, which utilizes a skill-based matchmaking system to form fair teams. However, players' skill levels vary widely depending on which champion, or hero, that they choose to play as. In this paper, we propose a method for predicting game outcomes in ranked LoL games based on players' experience with their selected champion. Using a deep neural network, we found that game outcomes can be predicted with 75.1% accuracy after all players have selected champions, which occurs before gameplay begins. Our results have important implications for playing LoL and matchmaking. Firstly, individual champion skill plays a significant role in the outcome of a match, regardless of team composition. Secondly, even after the skill-based matchmaking, there is still a wide variance in team skill before gameplay begins. Finally, players should only play champions that they have mastered, if they want to win games. CCS CONCEPTS • Information systems → Massively multiplayer online games; • Applied computing → Computer games.

Theoretical Computer Science, 2005

This paper presents a learning system for scoring final positions in the Game of Go. Our system learns to predict life and death from labelled game records. 98.9% of the positions are scored correctly and nearly all incorrectly scored positions are recognized. By providing reliable score information our system opens the large source of Go knowledge implicitly available in human game records, thus paving the way for a successful application of machine learning in Go.

IEEE Transactions on Computational Intelligence and Ai in Games, 2009

The MoGo program combines all-moves-as-first (AMAF)/rapid action value estimation (RAVE) values, online "upper confidence tree (UCT)-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: 1) the weakness in corners, 2) the scaling over time, 3) the behavior in handicap games, and 4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan (3D) with: 1) good skills for fights, 2) weaknesses in corners, in particular, for "semeai" situations, and 3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in AI and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer Chess or Chinese Chess in the future. Index Terms-Computational intelligence, computer Go, game, MoGo, Monte Carlo tree search (MCTS).

ArXiv, 2021

Competitive online games use rating systems to match players with similar skills to ensure a satisfying experience for players. In this paper, we focus on the importance of addressing different aspects of playing behavior when modeling players for creating match-ups. To this end, we engineer several behavioral features from a dataset of over 75,000 battle royale matches and create player models based on the retrieved features. We then use the created models to predict ranks for different groups of players in the data. The predicted ranks are compared to those of three popular rating systems. Our results show the superiority of simple behavioral models over mainstream rating systems. Some behavioral features provided accurate predictions for all groups of players while others proved useful for certain groups of players. The results of this study highlight the necessity of considering different aspects of the player’s behavior such as goals, strategy, and expertise when making assignm...

2007

Accuracy of evaluation functions is one of the critical factors in computer game players. Evaluation functions are usually constructed manually as a weighted linear combination of evaluation features that characterize game positions. Selecting evaluation features and tuning their weights require deep knowledge of the game and largely alleviates such efforts.

arXiv (Cornell University), 2017

The game Starcraft is one of the most interesting arenas to test new machine learning and computational intelligence techniques; however, StarCraft matches take a long time and creating a good dataset for training can be hard. Besides, analyzing match logs to extract the main characteristics can also be done in many different ways to the point that extracting and processing data itself can take an inordinate amount of time and of course, depending on what you choose, can bias learning algorithms. In this paper we present a simplified dataset extracted from the set of matches published by Robinson and Watson, which we have called RedDwarfData, containing several thousand matches processed to frames, so that temporal studies can also be undertaken. This dataset is available from GitHub under a free license. An initial analysis and appraisal of these matches is also made.