PARTITIONED ARTIFICIAL INTELLIGENCE FOR NETWORKED GAMES
First Claim
1. A system implemented on a gaming server device, the system comprising:
- partitioning an artificial intelligence (AI) process for the online game into a tunable server-side AI component and a client-side AI component that provides tuning parameters for the server-side AI component;
running the server-side AI component;
offloading the client-side AI component to a gaming client device of a game player of the online game, the client-side AI component being independent of prior computations of previous glimpses of game states of the online game; and
receiving tuning parameters from the client-side AI component to tune the server-side AI component.
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Accused Products
Abstract
Partitioned artificial intelligence (AI) for networked gaming. An exemplary system splits the AI into a computationally lightweight server-side component and a computationally intensive client-side component to harness the aggregate computational power of numerous gaming clients. Aggregating resources of many, even thousands of client machines enhances game realism in a manner that would be prohibitively expensive on the central server. The system is tolerant of latency between server and clients. Deterministic and stateless client-side components enable rapid handoff, preemptive migration, and replication of the client-side AI to address problems of client failure and game exploitation. The partitioned AI can support tactical gaming navigation, a challenging task to offload because of sensitivity to latency. The tactical navigation AI calculates influence fields partitioned into server-side and client-side components by means of a Taylor-series approximation.
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Citations
20 Claims
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1. A system implemented on a gaming server device, the system comprising:
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partitioning an artificial intelligence (AI) process for the online game into a tunable server-side AI component and a client-side AI component that provides tuning parameters for the server-side AI component; running the server-side AI component; offloading the client-side AI component to a gaming client device of a game player of the online game, the client-side AI component being independent of prior computations of previous glimpses of game states of the online game; and receiving tuning parameters from the client-side AI component to tune the server-side AI component. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method implemented on a gaming server device that hosts an online game, the method comprising:
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partitioning an artificial intelligence (AI) process for the online game into a tunable server-side AI component and a client-side AI component that provides tuning parameters for the server-side AI component, the artificial intelligence process being related to tactical navigation of the online game that employs an aggregate vector field to determine movements of gaming characters, wherein the client-side AI component uses an array of descriptors for each gaming character to calculate scalar coefficients for Taylor-series approximations of the aggregate vector field; running the server-side AI component on the gaming server device; offloading the client-side AI component to a gaming client device of a game player of the online game; and receiving tuning parameters from the client-side AI component to tune the server-side AI component. - View Dependent Claims (12, 13, 14)
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15. A method implemented on a gaming server device that hosts an online game, the method comprising:
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running a server-side AI component on the gaming server device; soliciting tuning parameters as advice from a client-side AI component by sending a glimpse of part of a game state to a gaming client device that runs the client-side AI component; and receiving tuning parameters from the client-side AI component to tune the server-side AI component, the tuning parameters comprising coefficients representing behavior possibilities based at least in part on the glimpse of the part of the game state. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification