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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
ICRA
2003
IEEE
124views Robotics» more  ICRA 2003»
15 years 3 months ago
Self-assembly in space via self-reconfigurable robots
― Self-assembly systems in space are arguably within the reach of today’s technology based on the research and development of self-reconfigurable robots on earth. This paper pr...
Wei-Min Shen, Peter M. Will, Berok Khoshnevis
IROS
2008
IEEE
142views Robotics» more  IROS 2008»
15 years 4 months ago
Probabilistic mapping of dynamic obstacles using Markov chains for replanning in dynamic environments
— Robots acting in populated environments must be capable of safe but also time efficient navigation. Trying to completely avoid regions resulting from worst case predictions of...
Florian Rohrmüller, Matthias Althoff, Dirk Wo...
CONCURRENCY
2006
92views more  CONCURRENCY 2006»
14 years 9 months ago
What makes workflows work in an opportunistic environment?
In this paper, we examine the issues of workflow mapping and execution in opportunistic environments such as the grid. As applications become ever more complex, the process of cho...
Ewa Deelman, Tevfik Kosar, Carl Kesselman, Miron L...
MASA
2001
Springer
15 years 2 months ago
Replanning in a Resource-Based Framework
An important aspect of agents is how they construct a plan to reach their goals. Since most agents live in a dynamic environment, they also will often be confronted with situations...
Roman van der Krogt, André Bos, Cees Wittev...