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Auflistung Künstliche Intelligenz 34(1) - März 2020 nach Erscheinungsdatum
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- ZeitschriftenartikelThe AI Settlement Generation Challenge in Minecraft(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Salge, Christoph; Green, Michael Cerny; Canaan, Rodrigo; Skwarski, Filip; Fritsch, Rafael; Brightmoore, Adrian; Ye, Shaofang; Cao, Changxing; Togelius, JulianThis article outlines what we learned from the first year of the AI Settlement Generation Competition in Minecraft, a competition about producing AI programs that can generate interesting settlements in Minecraft for an unseen map. This challenge seeks to focus research into adaptive and holistic procedural content generation. Generating Minecraft towns and villages given existing maps is a suitable task for this, as it requires the generated content to be adaptive, functional, evocative and aesthetic at the same time. Here, we present the results from the first iteration of the competition. We discuss the evaluation methodology, present the different technical approaches by the competitors, and outline the open problems.
- ZeitschriftenartikelArtificial Intelligence and Games(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Lucas, Simon
- ZeitschriftenartikelExtensional Paramodulation for Higher-Order Logic and Its Effective Implementation Leo-III(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Steen, AlexanderAutomation 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.
- 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.
- Zeitschriftenartikel2018 IEEE Conference on Computational Intelligence and Games (CIG 2018)(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Winands, Mark H. M.
- ZeitschriftenartikelBehind DeepMind’s AlphaStar AI that Reached Grandmaster Level in StarCraft II(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Risi, Sebastian; Preuss, Mike
- ZeitschriftenartikelFormation of a Research Discipline Artificial Intelligence and Intellectics at the Technical University of Munich(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Bibel, Wolfgang; Furbach, UlrichAcademic Computer Science emerged in Germany at the end of the 1960s. In 2017, the Munich universities celebrated “50 Years of Computer Science in Munich”. To this occasion various events were held; also, there was a special issue in the Informatik Spektrum, the official journal of the Gesellschaft für Informatik e.V. (GI – the German Society for Computer Science) and of associated organizations, as well as an anthology on Computer Science in Munich [ 1 ]. One year later, the present authors published a tribute to the research group for Artificial Intelligence/Intellectics at the TUM in a volume of the Deutsche Museum’s Preprints series [ 2 ], of which the present article is a very brief summary—for much more detailed information and impressions of former group members please refer to this booklet. The Munich group for Artificial Intelligence/Intellectics came into being thanks to academic freedom at German universities, in this case the Technical University of Munich (TUM): A single young scientist is enthusiastic about an idea, a new idea, which has not yet been worked on or supported by any professor at the TUM: Artificial Intelligence or Intellectics. The scientist initiates relationships with other colleagues, nationally and internationally; he is successful, receives research funding, and establishes a research group that asserted itself over almost four decades and influenced and advanced the field. The present article provides a brief history of the group.
- 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.
- 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