LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Precomputing avatar behavior from human motion data
Graphical Models - Special issue on SCA 2004
Automatic basis function construction for approximate dynamic programming and reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Composition of complex optimal multi-character motions
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Fat graphs: constructing an interactive character with continuous controls
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Precomputed search trees: planning for interactive goal-driven animation
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Proceedings of the 2007 symposium on Interactive 3D graphics and games
Responsive characters from motion fragments
ACM SIGGRAPH 2007 papers
Near-optimal character animation with continuous control
ACM SIGGRAPH 2007 papers
The Journal of Machine Learning Research
Simulating competitive interactions using singly captured motions
Proceedings of the 2007 ACM symposium on Virtual reality software and technology
Interaction patches for multi-character animation
ACM SIGGRAPH Asia 2008 papers
Learning representation and control in continuous Markov decision processes
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
An analysis of Laplacian methods for value function approximation in MDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Real-time planning for parameterized human motion
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Value function approximation in zero-sum markov games
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Generating avoidance motion using motion graph
MIG'11 Proceedings of the 4th international conference on Motion in Games
Interactive buildup of animation sequences with captured motion data
Computer Animation and Virtual Worlds
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Learning motion controllers with adaptive depth perception
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Learning motion controllers with adaptive depth perception
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games. We present a fully automatic method for generating intelligent real-time controllers for characters in such a game. Our approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness. Our results exhibit nuanced strategies based on unpredictability, such as feints and misdirection moves, which take into account and exploit the possible strategies of an adversary. The controllers are generated by examining the interaction between the rules of the game and the motions generated from a parametric motion graph. This involves solving a large-scale planning problem, so we also describe a new technique for scaling this process to higher dimensions.