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JAIR
2007
127views more  JAIR 2007»
15 years 19 days 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 ...
106
Voted
ERSHOV
1999
Springer
15 years 5 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
90
Voted
ACCV
2006
Springer
15 years 6 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
92
Voted
AIPS
2006
15 years 2 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 4 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