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Coupé, Veerle M.H.; Gaag, Linda C. van der

doi:10.1023/a:1016398407857

Cited by 94 publications

(4 citation statements)

References 14 publications

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“…Sensitivity analysis allows us to identify which of the parameter values from the CPT or probability (node state) are related to the value of the parameter in question [61]. Due to the structure of the evidence (there is no evidence downstream of the concerned variable), the functional connection is linear [62]. Hugin-Expert's sensitivity analysis feature was used to determine the factors with higher values when evaluating road incidents.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network

et al. 2022

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Understanding the causes and effects of road accidents is critical for developing road and action plans in a country. The causation hypothesis elucidates how accidents occur and may be applied to accident analysis to more precisely anticipate, prevent, and manage road safety programs. Driving behavior is a critical factor to consider when determining the causes of traffic accidents. Inappropriate driving behaviors are a set of acts taken on the roadway that can result in aberrant conditions that may result in road accidents. In this study, using Al-Ahsa city in Saudi Arabia’s Eastern Province as a case study, a Bayesian belief network (BBN) model was established by incorporating an expectation–maximization algorithm. The model examines the relationships between indicator variables with a special focus on driving behavior to measure the uncertainty associated with accident outcomes. The BBN was devised to analyze intentional and unintentional driving behaviors that cause different types of accidents and accident severities. The results showed when considering speeding alone, there is a 26% likelihood that collision will occur; this is a 63% increase over the initial estimate. When brake failure was considered in addition to speeding, the likelihood of a collision jumps from 26% to 33%, more than doubling the chance of a collision when compared to the initial value. These findings demonstrated that the BBN model was capable of efficiently investigating the complex linkages between driver behavior and the accident causes that are inherent in road accidents.

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Section: Sensitivity Analysismentioning

confidence: 99%

Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network

et al. 2022

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“…Behaviour sensitivity tests were conducted on the outcome nodes leading to the decision node. For example, the "sensitivity set" (Coupé & van der Gaag, 2002) for the node "Burning conditions, Day 1" (circled in red, Figure 6) was evaluated in Hugin. The more intense the colour of the node, the more the target node is sensitive to the values of the intensely coloured Figure 6: "Sensitivity set" for "Burning conditions, Day 1", circled in red nodes' CPTs.…”

Section: Ddn Validationmentioning

confidence: 99%

An adaptive, probabilistic, cognitive agent architecture for modelling sugarcane growers’ operational decision-making

Price¹,

Moodley

Pillay

et al. 2022

SACJ

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Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments

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“…The functional relationship between outputs and the BN model probabilities has been extensively studied in the literature (Castillo et al, 1997;Coupé and Van Der Gaag, 2002;Laskey, 1995;Rohmer and Gehl, 2020;Van Der Gaag et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks

Ballester-Ripoll

Leonelli

2023

International Journal of Approximate Reasoning

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Untitled

Cited by 94 publications

References 14 publications

Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network

Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network

An adaptive, probabilistic, cognitive agent architecture for modelling sugarcane growers’ operational decision-making

The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks

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