منابع مشابه
Point-based approximations for fast POMDP solving
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. Furthermore, until recently, the efficient approximations that were available offered few theoretical guarantees r...
متن کاملAccelerated Vector Pruning for Optimal POMDP Solvers
Partially Observable Markov Decision Processes (POMDPs) are powerful models for planning under uncertainty in partially observable domains. However, computing optimal solutions for POMDPs is challenging because of the high computational requirements of POMDP solution algorithms. Several algorithms use a subroutine to prune dominated vectors in value functions, which requires a large number of l...
متن کاملPoint-Based Policy Transformation: Adapting Policy to Changing POMDP Models
Motion planning under uncertainty that can efficiently take into account changes in the environment is critical for robots to operate reliably in our living spaces. Partially Observable Markov Decision Process (POMDP) provides a systematic and general framework for motion planning under uncertainty. Point-based POMDP has advanced POMDP planning tremendously over the past few years, enabling POM...
متن کاملAccelerating Point-Based POMDP Algorithms via Greedy Strategies
Many planning tasks of autonomous robots can be modeled as partially observable Markov decision process (POMDP) problems. Point-based algorithms are well-known algorithms for solving large-scale POMDP problems. Several leading point-based algorithms eschew some flawed but very useful heuristics to find an -optimal policy. This paper aims at exploiting these avoided heuristics by a simple framew...
متن کاملPoint-Based POMDP Algorithms: Improved Analysis and Implementation
Existing complexity bounds for point-based POMDP value iteration algorithms focus either on the curse of dimensionality or the curse of history. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. We also discuss recent improvements to our (point-based) heuristic search value iteration algorithm. Our ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
سال: 2008
ISSN: 1083-4419
DOI: 10.1109/tsmcb.2008.928222