Amusement devices: games – Including means for processing electronic data
Reexamination Certificate
2004-04-30
2010-11-23
Suhol, Dmitry (Department: 3714)
Amusement devices: games
Including means for processing electronic data
C463S007000, C463S008000, C463S030000, C463S049000
Reexamination Certificate
active
07837543
ABSTRACT:
Adaptive agents are driven by rewards they receive based on the outcome of their behavior during actual game play. Accordingly, the adaptive agents are able to learn from experience within the gaming environment. Reward-driven adaptive agents can be trained at either or both of game-time or development time. Computer-controlled agents receive rewards (either positive or negative) at individual action intervals based on the effectiveness of the agents' actions (e.g., compliance with defined goals). The adaptive computer-controlled agent is motivated to perform actions that maximize its positive rewards and minimize is negative rewards.
REFERENCES:
patent: 6195626 (2001-02-01), Stone
patent: 6199030 (2001-03-01), Stone
patent: 6487304 (2002-11-01), Szeliski
Pieter Spronck, Ida Sprinkhuizen-Kuyper and Eric Postma (2003). Online Adaptation of Game Opponent AI in Simulation and in Practice. Proceedings of the 4th International Conference on Intelligent Games and Simulation (Game-On 2003) (eds. Quasim Mehdi and Norman Gough), ISBN: 90-77381-05-8, pp. 93-100. EUROSIS, Belgium.
“Black and White: FAQ/Walkthrough”, GameFAQs.com, Apr. 22, 2002, <http://www.gamefaqs.com/computer/doswin/file/914356/11124>.
Bennewitz, M., et al., “Using EM to Learn Motion Behaviors of Persons with Mobile Robots,” Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems EPFL, Lausanne, Switzerland, Oct. 2002, pp. 502-507.
Sutton, Richard S. and Andrew G. Barto, “Reinforcement Learning: An Introduction,” MIT Press, Cambridge, MA, 1998, A Bradford Book, available at http://www-anw.cs.umass.edu/˜rich/book/the-book.html, retrieved Apr. 30, 2004, pp. 1-4.
Gold Julian
Graepel Kurt Hartwig
Herbrich Ralf
Microsoft Corporation
Pinheiro Jason
Suhol Dmitry
LandOfFree
Reward-driven adaptive agents for video games does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Reward-driven adaptive agents for video games, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Reward-driven adaptive agents for video games will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4223675