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JAIR
2007
127views more  JAIR 2007»
14 years 9 months ago
Learning Symbolic Models of Stochastic Domains
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a a new probabilistic planning rule representation to compactly ...
Hanna M. Pasula, Luke S. Zettlemoyer, Leslie Pack ...
ERSHOV
1999
Springer
15 years 2 months ago
Multi-agent Optimal Path Planning for Mobile Robots in Environment with Obstacles
Abstract. The paper describes a problem of multi-agent path planning in environment with obstacles. Novel approach to multi-agent optimal path planning, using graph representation ...
Fedor A. Kolushev, Alexander A. Bogdanov
ACCV
2006
Springer
15 years 4 months ago
A Bottom up Algebraic Approach to Motion Segmentation
We present a bottom up algebraic approach for segmenting multiple 2D motion models directly from the partial derivatives of an image sequence. Our method fits a polynomial called ...
Dheeraj Singaraju, René Vidal
AIPS
2006
14 years 11 months ago
Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
Malcolm J. A. Strens
ACCV
2006
Springer
15 years 1 months ago
Multi-camera Tracking of Articulated Human Motion Using Motion and Shape Cues
Abstract. We present a framework and algorithm for tracking articulated motion for humans. We use multiple calibrated cameras and an articulated human shape model. Tracking is perf...
Aravind Sundaresan, Rama Chellappa