Please use this identifier to cite or link to this item:
Title: A non-stationary partially-observable markov decision process for planning in volatile environments
Authors: Chatzis, Sotirios P. 
Kosmpoulos, Dimitrios 
Keywords: Bayesian inference;Non-stationarity;Partially observable Markov decision process
Category: Computer and Information Sciences
Field: Engineering and Technology
Issue Date: 1-Jun-2014
Publisher: National Technical University of Athens
Source: OPT-i 2014 - 1st International Conference on Engineering and Applied Sciences Optimization, Proceedings 2014-January, pp. 3020-3025
Conference: International Conference on Engineering and Applied Sciences Optimization 
Abstract: In this paper, we consider the problem of timely optimizing and adapting the structure of public transit networks, taking into consideration the dynamic changes in average traffic congestion and average gross demand trends for specific transit routes, as well as the targets set by policy makers regarding maximum customer waiting times, and fuel economy and environmental preservation goals. To solve this problem, we sketch in this paper a method that allows for automatically determining the number of bus stops, and their optimal ordering and placement, based on ideas from the field of Machine Learning. Most importantly, our method allows for the continuous dynamic adaptation of the structure of the public transit system to echo any significant changes in customers behavior or the traffic landscape. To achieve our goals, we devise a novel Partially Observable Markov Decision Process (POMDP) model with dynamic nonparametric Bayesian nature, and devise efficient learning and planning algorithms based on this model.
ISBN: 978-960999946-5
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Page view(s) 20

Last Week
Last month
checked on Jun 14, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.