Abstract

The evolving casino landscape necessitates intelligent management of gaming resources to maximize player satisfaction, operational efficiency, and revenue. This paper introduces a novel Cognitive Slot Management (CSM) system, a core component of a unified casino mobile platform, that leverages advanced AI, real-time analytics, and mobile integration to revolutionize the player's slot experience and optimize casino floor operations. Key features include AI-optimized slot reservation and session management, a live slot analytics dashboard for both players and operators, an intelligent routing system for frictionless navigation, and a comprehensive mobile hub for all player financial and promotional interactions. the authors' system integrates cutting-edge technologies like stream processing [4], machine learning for predictive analytics, and precise geo-fencing to achieve: pre-booked slot sessions with dynamic pricing and priority access; automated machine blocking for proactive maintenance and operational efficiency; real-time visualization of "hot" and "cold" machines with actionable insights; dynamic progressive jackpot alerts directly to the player's device; optimized wayfinding and crowd avoidance using spatial intelligence; seamless, terminal-free digital ticket redemption directly into the player's mobile account; hyper-personalized promotions and event calendars delivered exclusively through the app; and complete, real-time account oversight for all player financial and loyalty data on their phone. A pilot implementation yielded remarkable results, including a 40% increase in machine utilization, a 4.8/5.0 player satisfaction score, 28% growth in ancillary revenue from premium bookings, and a 65% reduction in maintenance downtime.

References

• C. Lampropoulos, G. Pitropakis, S. Katsikas, and E. Panaousis, “Security and privacy in smart casino ecosystems
• ,” IEEE Access, vol. 9, pp. 17504–17519, 2021.
• M. Zaharia et al., “Apache Spark
• : A unified engine for big data processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.
• J. Kreps, N. Narkhede, and J. Rao, “Kafka
• : A distributed messaging system for log processing,” in Proc. NetDB, Athens, Greece, 2011, pp. 1–7.
• A. Das, A. Roy, and S. Basu, “Anomaly detection in streaming data using Apache Flink
• ,” in Proc. IEEE Int. Conf. on Big Data, Seattle, WA, USA, 2018, pp. 1477–1486.
• S. B. Kotsiantis, the author. Zaharakis, and P. Pintelas, “Supervised machine learning
• : A review of classification techniques,” Informatica, vol. 31, no. 3, pp. 249–268, 2007.
• A. Dosovitskiy et al., “An image is worth 16×16 words: Transformers for image recognition
• at scale,” in Proc. Int. Conf. Learn. Representations (ICLR), 2021. [Online]. Available: https://arxiv.org/abs/2010.11929
• M. Satyanarayanan, “Edge computing
• : Vision and challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, Oct. 2016.
• M. S. Mott, “Dynamic pricing
• in casino operations,” Gaming Law Rev. Econ., vol. 24, no. 3, pp. 135–142, 2020.