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Colearning in Differential Games
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Variable Resolution Discretization in Optimal Control
Machine Learning
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ECML '02 Proceedings of the 13th European Conference on Machine Learning
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EWLR-6 Proceedings of the 6th European Workshop on Learning Robots
Spatial Cognition, An Interdisciplinary Approach to Representing and Processing Spatial Knowledge
Multiple Reward Criterion for Cooperative Behavior Acquisition in a Muliagent Environment
RoboCup-99: Robot Soccer World Cup III
Adaptive Representation Methods for Reinforcement Learning
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Piecewise linear value function approximation for factored MDPs
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Eighteenth national conference on Artificial intelligence
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AI Magazine
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SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
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TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
A Reinforcement Learning Algorithm in Cooperative Multi-Robot Domains
Journal of Intelligent and Robotic Systems
Incremental Learning of Linear Model Trees
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Precomputing avatar behavior from human motion data
Graphical Models - Special issue on SCA 2004
A hybrid generative and predictive model of the motor cortex
Neural Networks
Performance Loss Bounds for Approximate Value Iteration with State Aggregation
Mathematics of Operations Research
DEA: An Architecture for Goal Planning and Classification
Neural Computation
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Near-optimal character animation with continuous control
ACM SIGGRAPH 2007 papers
State Space Partition for Reinforcement Learning Based on Fuzzy Min-Max Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Least Squares SVM for Least Squares TD Learning
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
S-Learning: A Model-Free, Case-Based Algorithm for Robot Learning and Control
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Adaptive modeling and planning for reactive agents
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Closed-loop learning of visual control policies
Journal of Artificial Intelligence Research
Variable resolution discretization for high-accuracy solutions of optimal control problems
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Robot weightlifting by direct policy search
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Domain-independent, automatic partitioning for probabilistic planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Goal-directed feature learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Unified criterion of state generalization for reactive autonomous agents
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Learning visual representations for perception-action systems
International Journal of Robotics Research
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A dynamic allocation method of basis functions in reinforcement learning
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A multi-agent reinforcement learning approach to robot soccer
Artificial Intelligence Review
DCOB: Action space for reinforcement learning of high DoF robots
Autonomous Robots
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Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that neither planning nor exploration occurs uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been tested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes.