Auflistung Künstliche Intelligenz 34(1) - März 2020 nach Erscheinungsdatum
1 - 10 von 18
Treffer pro Seite
Sortieroptionen
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020)
- ZeitschriftenartikelExplaining AI: Are We Ready For It?(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Wrede, Britta
- ZeitschriftenartikelThe Many AI Challenges of Hearthstone(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Hoover, Amy K.; Togelius, Julian; Lee, Scott; Mesentier Silva, FernandoSince the inception of artificial intelligence, games have benchmarked algorithmic advances. Recent success in classic board games such as Chess and Go have left space for video games that pose related yet different sets of challenges. With this shifted focus, the set of AI problems associated with video games has expanded from simply playing these games to win, to include playing games in particular styles, generating game content, modeling players, etc. Different games pose different challenges for AI systems, and several such AI challenges can typically be addressed in the same game. In this article we analyze the popular collectible card game Hearthstone published by Blizzard in 2014, and describe a varied set of interesting AI challenges it poses. Despite their popularity and associated interesting challenges, collectible card games are relatively understudied in the AI community. By analyzing a single game in-depth, we get a glimpse of the entire field of AI and games through the lens of a single game, discovering a few new variations on existing research topics.
- ZeitschriftenartikelAI for Ancient Games(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Browne, CameronThis report summarises the Digital Ludeme Project, a recently launched 5-year research project being conducted at Maastricht University. This computational study of the world’s traditional strategy games seeks to improve our understanding of early games, their development, and their role in the spread of related mathematical ideas throughout recorded human history.
- ZeitschriftenartikelMatrix- and Tensor Factorization for Game Content Recommendation(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Sifa, Rafet; Yawar, Raheel; Ramamurthy, Rajkumar; Bauckhage, Christian; Kersting, KristianCommercial success of modern freemium games hinges on player satisfaction and retention. This calls for the customization of game content or game mechanics in order to keep players engaged. However, whereas game content is already frequently generated using procedural content generation, methods that can reliably assess what kind of content suits a player’s skills or preferences are still few and far between. Addressing this challenge, we propose novel recommender systems based on latent factor models that allow for recommending quests in a single player role-playing game. In particular, we introduce a tensor factorization algorithm to decompose collections of bipartite matrices which represent how players’ interests and behaviors change over time. Extensive online bucket type tests during the ongoing operation of a commercial game reveal that our system is able to recommend more engaging quests and to retain more players than previous handcrafted or collaborative filtering approaches.
- ZeitschriftenartikelFrom Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Risi, Sebastian; Preuss, MikeThis paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI.
- ZeitschriftenartikelSpecial Issue on AI in Games(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Risi, Sebastian; Preuss, Mike
- ZeitschriftenartikelA Games Industry Perspective on Recent Game AI Developments(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Preuss, Mike; Risi, Sebastian
- ZeitschriftenartikelUncertainty Handling in Surrogate Assisted Optimisation of Games(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Volz, VanessaReal-world problems are often affected by uncertainties of different types and from multiple sources. Algorithms created for expensive optimisation, such as model-based optimisers, introduce additional errors. We argue that these uncertainties should be accounted for during the optimisation process. We thus introduce a benchmark as well as a new surrogate-assisted evolutionary algorithm to investigate this hypothesis further. The benchmark includes two function suites based on procedural content generation for games, which is a common problem observed in games research and also mirrors several types of uncertainties in the real-world. We find that observing and handling the uncertainty present in the problem can improve the optimiser, and also provides valuable insight into the function characteristics.
- ZeitschriftenartikelMachine-Learning-Based Statistical Arbitrage Football Betting(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Knoll, Julian; Stübinger, JohannesAcross countries and continents, football (soccer) has drawn increasingly more attention over the last decades and developed into a huge commercial complex. Consequently, the market of bookmakers providing the possibility to bet on the result of football matches grew rapidly, especially with the appearance of the internet. With a high number of games every week in multiple countries, football league matches hold enormous potential for generating profits over time with the use of advanced betting strategies. In this paper, we use machine learning for predicting the outcome of football league matches by exploiting data about match characteristics. Based on insights from the field of statistical arbitrage stock market trading, we show that one could generate meaningful profits over time by betting accordingly. A simulation study analyzing the matches of the five top European football leagues from season 2013/14 to 2017/18 presented economically and statistically significant returns achieved by exploiting large data sets with modern machine learning algorithms. In contrast to these modern algorithms, the break-even point could not be reached with an ordinary linear regression approach or simple betting strategies, e.g. always betting on the home team.