PARTITIONED ARTIFICIAL INTELLIGENCE FOR NETWORKED GAMES

• US 20120142430A1
• Filed: 02/13/2012
• Published: 06/07/2012
• Est. Priority Date: 02/11/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Douceur, John R., Calligaro, Michael P., Wood, Randall C., Lorch, Jacob R.
• Granted Patent: US 9,327,194 B2
• Time in Patent Office: Days
• US Class Current: 463/42
• CPC Class Codes: A63F 13/10 Control of the course of the game, e.g. start, progess, end
• CPC Class Codes: A63F 13/12 involving interaction between a plurality of game devices, e.g. transmisison or distribution systems protocols for networked virtual reality, networked games H04L29/06034
• CPC Class Codes: A63F 13/30 Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
• CPC Class Codes: A63F 13/34 using peer-to-peer connections
• CPC Class Codes: A63F 13/355 Performing operations on behalf of clients with restricted processing capabilities, e.g. servers transform changing game scene into an encoded video stream for transmitting to a mobile phone or a thin client
• CPC Class Codes: A63F 13/358 Adapting the game course according to the network or server load, e.g. for reducing latency due to different connection speeds between clients
• CPC Class Codes: A63F 13/45 Controlling the progress of the video game
• CPC Class Codes: A63F 13/60 Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor
• CPC Class Codes: A63F 2300/534 for network load management, e.g. bandwidth optimization, latency reduction
• CPC Class Codes: A63F 2300/538 for performing operations on behalf of the game client, e.g. rendering
• CPC Class Codes: A63F 2300/6009 for importing or creating game content, e.g. authoring tools during game development, adapting content to different platforms, use of a scripting language to create content
• CPC Class Codes: A63F 2300/63 for controlling the execution of the game in time

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.

Claims

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.

2. The system as recited in claim 1, the acts further comprising structuring the client-side AI component to be deterministic to produce identical tuning parameters if identical glimpses of a game state of the online game are sent to the client-side AI component.

3. The system as recited in claim 1, the acts further comprising:

replicating the client-side AI component; and

offloading the replicated client-side AI component to one or more other gaming client devices of one or more other game players of the online game.

4. The system as recited in claim 3, the acts further comprising:

redundantly sending a same glimpse of a game state to the gaming client device and the one or more other gaming client devices; and

applying a plurality voting scheme to the tuning parameters received from the gaming client device and tuning parameters received from the one or more other gaming client devices to overcome a tampered state of one of the gaming client device and the one or more other gaming client devices.

5. The system as recited in claim 3, the acts further comprising:

redundantly sending a same glimpse of a game state to the gaming client device and the one or more other gaming client devices; and

accepting tuning parameters that are first received from among the gaming client device and the one or more other gaming client devices to overcome a client failure.

6. The system as recited in claim 1, wherein the server-side AI component and the client-side AI component are configured to tolerate remote computation latency.

7. The system as recited in claim 1, wherein the artificial intelligence process is related to tactical navigation of the online game, and the tactical navigation employs an aggregate vector field to determine movements of gaming characters, and wherein the server-side AI component selects which direction to move a gaming character of the gaming characters via the aggregate vector field calculated as a sum of influence fields radiating from the gaming characters in the game.

8. The system as recited in claim 7, the acts further comprising:

partitioning a calculation of the aggregate vector field between the server-side AI component and the client-side AI component via Taylor-series approximations;

receiving, from the client-side AI component, coefficients for the Taylor-series approximations based on influence fields emanating from a local subset of the gaming characters, the coefficients comprising the tuning parameters for the server-side AI component; and

summing the Taylor-series approximations of the influence fields radiating from the gaming characters, the Taylor-series approximations running at substantially a same rate as the gaming server device'"'"'s game loop.

9. The system as recited in claim 1, wherein the client-side AI component performs AI computations for a gaming character that is in a different area of a game world than a gaming character of the gaming client'"'"'s local player.

10. The system as recited in claim 1, the acts further comprising temporally limiting an effect of the gaming client device on the server-side AI component by preemptively migrating the client-side AI component via assigning successive computations for a gaming character to another gaming client device.

11. A method implemented on a gaming server device that hosts an online game, the method 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, 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.

12. The method as recited in claim 11, wherein the server-side AI component selects which direction to move a gaming character of the gaming characters via the aggregate vector field calculated as a sum of influence fields radiating from the gaming characters in the online game.

13. The method as recited in claim 12, wherein the scalar coefficients are computed based on a glimpse of a game state provided by the gaming server device when the gaming character of the game characters is at a given position in a virtual space.

14. The method as recited in claim 11, wherein each descriptor includes a position of a respective gaming character, a strength weighting of a respective influence field, and an exponential decay rate of the respective influence field.

15. A method implemented on a gaming server device that hosts an online game, the method comprising:

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.

16. The method as recited in claim 15, further comprising:

redundantly sending the glimpse of the part of the game state to one or more other gaming client devices; and

accepting tuning parameters that are first received from among the gaming client device and the one or more gaming other client devices to overcome a client failure.

17. The method as recited in claim 15, further comprising:

redundantly sending the glimpse of the part of the game state to one or more other gaming client devices; and

applying a plurality voting scheme to tuning parameters received from the gaming client device and the one or more other gaming client devices to overcome a tampered state of one of the gaming client device and the one or more other gaming client devices.

18. The method as recited in claim 15, wherein the client-side AI component provides a stateless and deterministic segment of a gaming AI, each glimpse-advice exchange between the gaming server device and the gaming client device comprising a standalone module of the gaming AI that can be executed interchangeably by the gaming client device, the standalone module running independently of prior computations and without dependence on a client-side state that has been carried forward.

19. The method as recited in claim 15, further comprising using the tuning parameters from the client-side AI component as the advice for improving behavior of a gaming character of the online game.

20. The method as recited in claim 15, wherein the gaming server device is independent of the advice from the client-side AI component to satisfactorily run the online game.