The Shortest Path Problem in Uncertain Domains -- an Agent based Approach with Bayesian Networks

Dangelmaier, Wilhelm; Klöpper, Benjamin; Wienstroer, Jens; Timm, Thorsten

doi:10.1109/isda.2006.253739

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paradigm of Bayesian thinking teaches us that we can estimate variables like travel times by partial knowledge of the influences and their conditional probability distribution. In [2] we introduced a MAS that implements a distributed Bayesian network. Vehicle agents are able to build a dynamic and localized probability model using this distributed Bayesian network.…”

Section: Travel Time Forecasting By Probabilistic Reasoningmentioning

confidence: 99%

See 1 more Smart Citation

Risk Averse Shortest Path Planning in Uncertain Domains

Dangelmaier

Klöpper

Wienstroer

et al. 2006

2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference On

Self Cite

View full text Add to dashboard Cite

Real world problems, e.g. from transport domain, are typically non-deterministic and uncertain. Although there are some approaches, which try to forecast uncertain parameters like travel time, the uncertainty is rarely included in the planning process. In this paper a probabilistic forecasting method for travel time in a railway network is introduced which considers the dependencies between decisions during the planning process. The information provided by forecasting is used to develop a risk averse shortest path algorithm which minimizes the risk of delay. 1

show abstract

Section: Travel Time Forecasting By Probabilistic Reasoningmentioning

confidence: 99%

“…An important subtask during the competition is to search for an optimal path. In [2] our MAS was extended to cope with probabilistic travel times within the railway network. A distributed Bayesian network, implemented by expert agents, was used to provide estimation in the form of probability density functions.…”

Section: Introductionmentioning

confidence: 99%