Künstliche Intelligenz 34(1) - März 2020

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  • Zeitschriftenartikel
    A Games Industry Perspective on Recent Game AI Developments
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Preuss, Mike; Risi, Sebastian
  • Zeitschriftenartikel
    Special Issue on AI in Games
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Risi, Sebastian; Preuss, Mike
  • Zeitschriftenartikel
    AI for Ancient Games
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Browne, Cameron
    This 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.
  • Zeitschriftenartikel
    Uncertainty Handling in Surrogate Assisted Optimisation of Games
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Volz, Vanessa
    Real-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.
  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020)
  • Zeitschriftenartikel
    Matrix- and Tensor Factorization for Game Content Recommendation
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Sifa, Rafet; Yawar, Raheel; Ramamurthy, Rajkumar; Bauckhage, Christian; Kersting, Kristian
    Commercial 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.
  • Zeitschriftenartikel
    Extensional Paramodulation for Higher-Order Logic and Its Effective Implementation Leo-III
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Steen, Alexander
    Automation of classical higher-order logic faces various theoretical and practical challenges. On a theoretical level, powerful calculi for effective equality reasoning from first-order theorem proving cannot be lifted to the higher-order domain in a simple manner. Practically, implementations of higher-order reasoning systems have to incorporate procedures that often have high time complexity or are not decidable in general. In my dissertation, both the theoretical and the practical challenges of designing an effective higher-order reasoning system are studied. The resulting system, the automated theorem prover Leo-III, is one of the most effective and versatile systems, in terms of supported logical formalisms, to date.
  • Zeitschriftenartikel
    Search, Abstractions and Learning in Real-Time Strategy Games
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Barriga, Nicolas A.
    Real-Time Strategy Games’ large state and action spaces pose a significant hurdle to traditional AI techniques. We propose decomposing the game into sub-problems and integrating the partial solutions into action scripts that can be used as abstract actions by a search or machine learning algorithm. The resulting high level algorithm produces sound strategic choices, and can then be combined with a low-level search algorithm to refine tactical choices. We show strong results in SparCraft, Starcraft: Brood War and $$\mu $$ μ RTS against state-of-the-art agents. We expect advances in RTS AI can be used in commercial videogames for playtesting and game balancing, while also having possible real-world applications.
  • Zeitschriftenartikel
    Behind DeepMind’s AlphaStar AI that Reached Grandmaster Level in StarCraft II
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Risi, Sebastian; Preuss, Mike
  • Zeitschriftenartikel
    Machine-Learning-Based Statistical Arbitrage Football Betting
    (KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Knoll, Julian; Stübinger, Johannes
    Across 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.