Reducing bandwidth requirements for peer-to-peer gaming based on error difference between actual game object state and simulated game object state being below an error threshold

• US 7,925,601 B2
• Filed: 10/19/2009
• Issued: 04/12/2011
• Est. Priority Date: 11/13/2006
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Pang, Jeffrey Anson, Uyeda, Frank Christopher, Lorch, Jacob R., Douceur, John R.
• Primary Examiner(s): Sparks; Donald
• Assistant Examiner(s): Rifkin; Ben M
• Application Number:
  US12/581,807
• Publication Number:
  US 20100035695A1
• Time in Patent Office: 540 Days
• Field of Search: 706/14, 709/205
• US Class Current: 706/14
• 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 13/67 adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
• CPC Class Codes: A63F 2300/407 Data transfer via internet
• CPC Class Codes: A63F 2300/408 Peer to peer connection
• CPC Class Codes: A63F 2300/6027 using adaptive systems learning from user actions, e.g. for skill level adjustment
• CPC Class Codes: H04L 67/104 Peer-to-peer [P2P] networks
• CPC Class Codes: H04L 67/1059 Inter-group management mechanisms, e.g. splitting, merging or interconnection of groups
• CPC Class Codes: H04L 67/131 Protocols for games, networked simulations or virtual reality
• CPC Class Codes: H04L 67/30 Profiles

Abstract

Techniques enable the reduction of bandwidth requirements for peer-to-peer gaming architectures. In some embodiments, these techniques allow differentiation among players to decide which players should receive continuous updates and which should receive periodic updates. For those gaming systems receiving periodic updates, guided artificial intelligence is employed to simulate activity of a game object based on guidance provided by the periodic updates. Conversely, for those gaming systems receiving continuous updates, the continuous updates may be employed to update the activity of the game object rather than simulating the activity.

Claims

1. In a peer-to-peer multiplayer game architecture where game objects are partitioned across participating peers, a method comprising:

receiving guidance pertaining to activity of at least one game object controlled by a remote player; and

simulating the activity of the at least one game object using artificial intelligence based on the guidance;

converging from artificial intelligence simulation of the activity of the at least one game object to controlling the activity of the at least one game object via object updates, the convergence comprising;

receiving an object update from the remote player;

treating the object update as guidance;

computing an error between a simulated state of the at least one game object and an actual state of the at least one game object; and

if the computed error is less than a threshold value, warping to the at least one game object'"'"'s actual state.

2. The method as described in claim 1, further comprising prioritizing modification of an object state based on a remote procedure call received from the remote player.

3. The method as described in claim 1, further comprising deciding, by a local player, whether a non-instant hit projectile sent by a remote player strikes an avatar controlled by the local player.

4. The method as described in claim 1, further comprising prioritizing remote players based on each remote player'"'"'s attention on the local player and on the local player'"'"'s estimate of how much attention each remote player should be paying to the local player.

5. The method as described in claim 1, wherein the guidance is received directly from the remote player.

6. The method as described in claim 1, wherein the guidance comprises at least one of:

a predicted game object location, a predicted game object velocity, a predicted game object acceleration, a predicted game object behavioral parameter, a set of replay counters, or a predicted game object aiming angle.