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Auflistung nach Schlagwort "Competition"

1 - 4 von 4
  • Zeitschriftenartikel
    Esport
    (Business & Information Systems Engineering: Vol. 64, No. 3, 2021) Werder, Karl
  • Zeitschriftenartikel
    Kollaboration oder Wettbewerb: ein Vergleich der Motivation beim Game-based Learning
    (HMD Praxis der Wirtschaftsinformatik: Vol. 56, No. 1, 2019) Eckardt, Linda; Finster, Rebecca
    Spielerisches Lernen ist ein aktueller Trend. Sogenanntes Game-based Learning (GBL) führt zu einer positiven Beeinflussung von Spaß, Motivation und Engagement. In entsprechenden Anwendungen sind jedoch häufiger Wettbewerbselemente als kollaborative Elemente integriert, obwohl dies negative Auswirkungen auf ein erfolgreiches Lernen haben kann. In diesem Beitrag wird daher untersucht, ob eine GBL Anwendung nur mit Kollaboration genauso motiviert zu lernen, wie eine Anwendung mit Kollaboration und Wettbewerb. Die Ergebnisse der Studie zeigen, dass kollaboratives GBL genauso motiviert wie GBL mit einer Kombination aus Kollaboration und Wettbewerb. Playful learning is a current trend. Game-based learning (GBL) leads to a positive influence on fun, motivation and commitment. In such applications, competitive elements are more often integrated than collaborative elements, although this can have negative effects on successful learning. Therefore, we examine in this paper, whether a GBL application with collaboration is as motivating for learning as an application with a combination of collaboration and competition. The results of the study show that collaborative GBL is as motivating as GBL is with a combination of collaboration and competition.
  • Zeitschriftenartikel
    Self-learning Agents for Recommerce Markets
    (Business & Information Systems Engineering: Vol. 66, No. 4, 2024) Groeneveld, Jan; Herrmann, Judith; Mollenhauer, Nikkel; Dreeßen, Leonard; Bessin, Nick; Tast, Johann Schulze; Kastius, Alexander; Huegle, Johannes; Schlosser, Rainer
    Nowadays, customers as well as retailers look for increased sustainability. Recommerce markets – which offer the opportunity to trade-in and resell used products – are constantly growing and help to use resources more efficiently. To manage the additional prices for the trade-in and the resale of used product versions challenges retailers as substitution and cannibalization effects have to be taken into account. An unknown customer behavior as well as competition with other merchants regarding both sales and buying back resources further increases the problem’s complexity. Reinforcement learning (RL) algorithms offer the potential to deal with such tasks. However, before being applied in practice, self-learning algorithms need to be tested synthetically to examine whether they and which work in different market scenarios. In the paper, the authors evaluate and compare different state-of-the-art RL algorithms within a recommerce market simulation framework. They find that RL agents outperform rule-based benchmark strategies in duopoly and oligopoly scenarios. Further, the authors investigate the competition between RL agents via self-play and study how performance results are affected if more or less information is observable (cf. state components). Using an ablation study, they test the influence of various model parameters and infer managerial insights. Finally, to be able to apply self-learning agents in practice, the authors show how to calibrate synthetic test environments from observable data to be used for effective pre-training.
  • Zeitschriftenartikel
    The 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, Julian
    This 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.