Automatically assigned DDC number: 00631

Manually assigned DDC number: 00631

Number of references: 16

Title: Approximation Algorithms for Solving Cost Observable Markov Decision Processes

Author:

Subject: Valentina Bayer Approximation Algorithms for Solving Cost Observable Markov Decision Processes

Description: Introduction Partial observability is a result of noisy or imperfect sensors that are not able to reveal the real state of the world. For example, consider a robot moving in a building. If its sensors detect only walls, then rooms with similar configurations will look the same. People have to deal with partial observability, too. If there is a truck in front of his/her car, the driver will have a limited visibility of the road ahead. The problems that suffer from partial observability have been modelled as Partially Observable Markov Decision Processes (POMDPs). They have been studied by researchers in Operations Research and Artificial Intelligence for the past 30 years. Nevertheless, solving for the optimal solution or for close approximations to the optimal solution is known to be at least NP-hard ([26]). Current algorithms are very expensive and do not scale well. Many applications can be modelled as POMDPs: quality control, autonomous robots, weapon allocation, medical d

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1999-01-28

Pubyear: 1998

Format: ps

Identifier: http://citeseer.ist.psu.edu/331675.html

Source: http://www.cs.orst.edu/~bayer/research/proposal.ps

Language: en

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