Abstract: The configuring of an electronic gaming machine (EGM) to participate in pools managed by a progressive pools managing controller (PPMC) can be complex. The process is simplified in accordance with the present disclosure by automatically transmitting from an EGM and to its corresponding PPMC, an auto-configuration request identifying the EGM and a predetermined wagering game supported by the EGM; responsively receiving from the PPMC and within the EGM a listing of one or more progressive pools that are managed by the PPMC and that have been determined within the PPMC to be progressive pools that the predetermined wagering game of the requesting EGM is qualified to or required to participate in; and causing the predetermined wagering game of the EGM to begin participating in at least one of the listed progressive pools.

Abstract: The configuring of an electronic gaming machine (EGM) to participate in pools managed by a progressive pools managing controller (PPMC) can be complex. The process is simplified in accordance with the present disclosure by automatically transmitting from an EGM and to its corresponding PPMC, an auto-configuration request identifying the EGM and a predetermined wagering game supported by the EGM; responsively receiving from the PPMC and within the EGM a listing of one or more progressive pools that are managed by the PPMC and that have been determined within the PPMC to be progressive pools that the predetermined wagering game of the requesting EGM is qualified to or required to participate in; and causing the predetermined wagering game of the EGM to begin participating in at least one of the listed progressive pools.

Abstract: An artificial intelligence (AI) learning machine is integrated into a gaming system to adaptively learn to predict when and where excessive chronological clustering of winning hits is likely to occur for a progressive jackpot (e.g., a high frequency one) within the gaming system, to preemptively prepare for such occurrences by prefunding one or more smart-awards funds to cope with such excessive chronological clusterings of hits and to use the one or more smart-awards funds to pay off not-first-in-time winners with a same jackpot award amount as is paid to the true first-in-time winner of the jackpot if such runner up winners hit their respective win conditions within a predetermined smart-awards time span after the first-in-time winner hits. This can avoid or reduce feelings of unfairness by the not-first-in-time winners especially when the latter hit just a split second after the true first-in-time winner hits the jackpot.

Abstract: An artificial intelligence (AI) learning machine is integrated into a gaming system to adaptively learn to predict when and where excessive chronological clustering of winning hits is likely to occur for a progressive jackpot (e.g., a high frequency one) within the gaming system, to preemptively prepare for such occurrences by prefunding one or more smart-awards funds to cope with such excessive chronological clusterings of hits and to use the one or more smart-awards funds to pay off not-first-in-time winners with a same jackpot award amount as is paid to the true first-in-time winner of the jackpot if such runner up winners hit their respective win conditions within a predetermined smart-awards time span after the first-in-time winner hits. This can avoid or reduce feelings of unfairness by the not-first-in-time winners especially when the latter hit just a split second after the true first-in-time winner hits the jackpot.

Abstract: An artificial intelligence (AI) learning machine is integrated into a gaming system to adaptively learn to predict when and where excessive chronological clustering of winning hits is likely to occur for a progressive jackpot (e.g., a high frequency one) within the gaming system, to preemptively prepare for such occurrences by prefunding one or more smart-awards funds to cope with such excessive chronological clusterings of hits and to use the one or more smart-awards funds to pay off not-first-in-time winners with a same jackpot award amount as is paid to the true first-in-time winner of the jackpot if such runner up winners hit their respective win conditions within a predetermined smart-awards time span after the first-in-time winner hits. This can avoid or reduce feelings of unfairness by the not-first-in-time winners especially when the latter hit just a split second after the true first-in-time winner hits the jackpot.

Abstract: An artificial intelligence (AI) learning machine is integrated into a gaming system to adaptively learn to predict when and where excessive chronological clustering of winning hits is likely to occur for a progressive jackpot (e.g., a high frequency one) within the gaming system, to preemptively prepare for such occurrences by prefunding one or more smart-awards funds to cope with such excessive chronological clusterings of hits and to use the one or more smart-awards funds to pay off not-first-in-time winners with a same jackpot award amount as is paid to the true first-in-time winner of the jackpot if such runner up winners hit their respective win conditions within a predetermined smart-awards time span after the first-in-time winner hits. This can avoid or reduce feelings of unfairness by the not-first-in-time winners especially when the latter hit just a split second after the true first-in-time winner hits the jackpot.