Counterfactual Regret Minimization を用いたトレーディングカードゲームの戦略計算

学位の種別: 修士University of Tokyo(東京大学

A Deep Learning Agent for Games with Hidden Information

The goal of this project is to develop an agent capable of playing a particular game at an above average human level. In order to do so we investigated reinforcement and deep learning techniques for making decisions in discrete action spaces with hidden information. The methods we used to accomplish this goal include a standard word2vec implementation, an alpha-beta minimax tree search, and an LSTM network to evaluate game states. Given just the rules of the game and a vector representation of the game states, the agent learned to play the game by competitive self play. The emergent behavior from these techniques was compared to human play

Enhancing cloud security through the integration of deep learning and data mining techniques: A comprehensive review

Cloud computing is crucial in all areas of data storage and online service delivery. It adds various benefits to the conventional storage and sharing system, such as simple access, on-demand storage, scalability, and cost savings. The employment of its rapidly expanding technologies may give several benefits in protecting the Internet of Things (IoT) and physical cyber systems (CPS) from various cyber threats, with IoT and CPS providing facilities for people in their everyday lives. Because malware (malware) is on the rise and there is no well-known strategy for malware detection, leveraging the cloud environment to identify malware might be a viable way forward. To avoid detection, a new kind of malware employs complex jamming and packing methods. Because of this, it is very hard to identify sophisticated malware using typical detection methods. The article presents a detailed assessment of cloud-based malware detection technologies, as well as insight into understanding the cloud's use in protecting the Internet of Things and critical infrastructure from intrusions. This study examines the benefits and drawbacks of cloud environments in malware detection, as well as presents a methodology for detecting cloud-based malware using deep learning and data extraction and highlights new research on the issues of propagating existing malware. Finally, similarities and variations across detection approaches will be exposed, as well as detection technique flaws. The findings of this work may be utilized to highlight the current issue being tackled in malware research in the future

IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION

The gaming industry has become one of the largest and most profitable industries today. According to market research, the industry revenues will pass $200 Billion and are expected to reach another$20 Billion in 2024. With the industry growing rapidly, players have become more demanding, expecting better content and quality. This means that game studios need new and innovative ways to make their games more enjoyable. One technique used to improve the player experience is DDA (Dynamic Difficulty Adjustment). It leverages the current player state to perform different adjustments during the game to tune the difficulty delivered to the player to be more in line with their expectations and capabilities. In this thesis, we will explore and test the ability to obtain the difficulty level in which a player should be placed initially, by using previous gaming information from platforms like Steam, combined with different machine learning (ML) algorithms and data analyses., In doing so, we can create a pre-assessment of the player as a way of improving DDA’s initial state and the overall gaming experience of players

INTEGRATING DIGITAL TWIN CONCEPTS TO ENHANCE AGILITY OF THE UNITED STATES MARINE CORPS’ DECISION SUPPORT FRAMEWORK

Digital twin (DT) application and related technology has the potential to enhance the accuracy of wargame simulations in order to provide risk-informed decision support recommendations. A DT of the operating environment could theoretically be developed to continuously gather data from the operating forces and create computational models or simulations to test battlespace conditions. Effective implementation of DT can provide commanders with timely updates and adjustments to recommendations, aiding the decision-making process. Real-time updates would then inform commanders if the previously recommended course of action is no longer considered optimal based on the continuously running simulations. This thesis performs a qualitative assessment on the integration of a DT-enabled decision support system into the Marine Corps planning process and as an effective tool for leadership at various levels of command. The researchers determined that the wargaming process can be enhanced by incorporating real-time data into simulated future conflict to facilitate the inclusion of data analysis into time-sensitive decisions and potentially improve the management of uncertainty in the decision-making process. Leaders would benefit from increased awareness and quantitative assistance with resource allocation decisions. Expected challenges will be the digitization process of the operating force as well as acculturating leaders to the new technology.Approved for public release. Distribution is unlimited.Major, United States Marine CorpsCaptain, United States Marine Corp

Feedback-Based Gameplay Metrics and Gameplay Performance Segmentation: An audio-visual approach for assessing player experience.

Gameplay metrics is a method and approach that is growing in popularity amongst the game studies research community for its capacity to assess players’ engagement with game systems. Yet, little has been done, to date, to quantify players’ responses to feedback employed by games that conveys information to players, i.e., their audio-visual streams. The present thesis introduces a novel approach to player experience assessment - termed feedback-based gameplay metrics - which seeks to gather gameplay metrics from the audio-visual feedback streams presented to the player during play. So far, gameplay metrics - quantitative data about a game state and the player's interaction with the game system - are directly logged via the game's source code. The need to utilise source code restricts the range of games that researchers can analyse. By using computer science algorithms for audio-visual processing, yet to be employed for processing gameplay footage, the present thesis seeks to extract similar metrics through the audio-visual streams, thus circumventing the need for access to, whilst also proposing a method that focuses on describing the way gameplay information is broadcast to the player during play. In order to operationalise feedback-based gameplay metrics, the present thesis introduces the concept of gameplay performance segmentation which describes how coherent segments of play can be identified and extracted from lengthy game play sessions. Moreover, in order to both contextualise the method for processing metrics and provide a conceptual framework for analysing the results of a feedback-based gameplay metric segmentation, a multi-layered architecture based on five gameplay concepts (system, game world instance, spatial-temporal, degree of freedom and interaction) is also introduced. Finally, based on data gathered from game play sessions with participants, the present thesis discusses the validity of feedback-based gameplay metrics, gameplay performance segmentation and the multi-layered architecture. A software system has also been specifically developed to produce gameplay summaries based on feedback-based gameplay metrics, and examples of summaries (based on several games) are presented and analysed. The present thesis also demonstrates that feedback-based gameplay metrics can be conjointly analysed with other forms of data (such as biometry) in order to build a more complete picture of game play experience. Feedback based game-play metrics constitutes a post-processing approach that allows the researcher or analyst to explore the data however they wish and as many times as they wish. The method is also able to process any audio-visual file, and can therefore process material from a range of audio-visual sources. This novel methodology brings together game studies and computer sciences by extending the range of games that can now be researched but also to provide a viable solution accounting for the exact way players experience games