John R. Douceur, Jacob R. Lorch, Frank Uyeda, and Randall C. Wood
In the context of online role-playing games, we evaluate offloading AI computation from game servers to game clients. In this way, the aggregate resources of thousands of participating client machines can enhance game realism in a way that would be prohibitively expensive on a central server. Because offloading can add significant latency to a computation normally executing within a game server’s main loop, we introduce the mechanism of AI partitioning: splitting an AI into a high-frequency but computationally simple component on the server, and a lowfrequency but computationally intensive component offloaded to a client. By designing the client-side component to be stateless and deterministic, this approach also facilitates rapid handoff, preemptive migration, and replication, which can address the problems of client failure and exploitation. To explore this approach, we develop an improved AI for tactical navigation, a challenging task to offload because it is highly sensitive to latency. Our improvement is based on calculating influence fields, partitioned into server-side and client-side components by means of a Taylor series approximation. Experiments on a Quake-based prototype demonstrate that this approach can substantially improve the AI’s abilities, even with server-clientserver latencies up to one second.
|Published in||Proceedings of the 17th International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV)|
|Publisher||Association for Computing Machinery, Inc.|
Copyright © 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or email@example.com. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.