Bayesian Network Properties and Information Format Affect Causal Reasoning Ability

For the sake of measuring the reliability of actual face recognition system with continuous variables, after analyzing system structure, common failures, influencing factors of reliability, and maintenance data of a public security face recognition system in use, we propose a reliability evaluation model based on Continuous Bayesian Network. We design a Clique Tree Propagation algorithm to reason and solve the model, which is realized by R programs, and as a result, the reliability coefficient of the actual system is obtained. Subsequently, we verify the Continuous Bayesian Network by comparing its evaluation results with those of traditional Bayesian Network and Ground Truth. According to these evaluation results, we find out some weaknesses of the system and propose some optimization strategies by the way of finding the right remedies and filling in blanks. In this paper, we synthetically apply a variety of methods, such as qualitative analysis, quantitative analysis, theoretical analysis, and empirical analysis, to solve the unascertained causal reasoning problem. The evaluation method is reasonable and valid, the results are consistent with realities and objective, and the proposed strategies are very operable and targeted. This work is of theoretical significance to research on reliability theory. It is also of practical significance to the improvement of the system’s reliability and the ability of public order maintenance.

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Software Project and Quality Modelling Using Bayesian Networks

Advances in Computational Intelligence and Robotics - Artificial Intelligence Applications for Improved Software Engineering Development ◽

10.4018/978-1-60566-758-4.ch001 ◽

2011 ◽

pp. 1-25 ◽

Cited By ~ 3

Author(s):

Norman Fenton ◽

Peter Hearty ◽

Martin Neil ◽

Lukasz Radlinski

Keyword(s):

Software Engineering ◽

Software Development ◽

Bayesian Networks ◽

Bayesian Network ◽

Real World ◽

Causal Reasoning ◽

Software Project ◽

Limited Data ◽

Short Overview ◽

Software Development Environments

This chapter provides an introduction to the use of Bayesian Network (BN) models in Software Engineering. A short overview of the theory of BNs is included, together with an explanation of why BNs are ideally suited to dealing with the characteristics and shortcomings of typical software development environments. This theory is supplemented and illustrated using real world models that illustrate the advantages of BNs in dealing with uncertainty, causal reasoning and learning in the presence of limited data.

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Collision Avoidance in Unstructured Environments for Autonomous Robots: A Behavioural Modelling Approach

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.3559 ◽

2011 ◽

Vol 403-408 ◽

pp. 3559-3569

Author(s):

Chika O. Yinka-Banjo ◽

Isaac O. Osunmakinde ◽

Antoine Bagula

Keyword(s):

Bayesian Network ◽

Collision Avoidance ◽

Autonomous Robots ◽

Reasoning Ability ◽

Degrees Of Belief ◽

Bayesian Network Model ◽

Degree Of Belief ◽

Unstructured Environment ◽

Good Potential ◽

Modelling Approach

Collision avoidance is one of the important safety key operations that needs attention in the navigation system of an autonomous robot. In this paper, a Behavioural Bayesian Network approach is proposed as a collision avoidance strategy for autonomous robots in an unstructured environment with static obstacles. In our approach, an unstructured environment was simulated and the information of the obstacles generated was used to build the Behavioural Bayesian Network Model (BBNM). This model captures uncertainties from the unstructured environment in terms of probabilities, and allows reasoning with the probabilities. This reasoning ability enables autonomous robots to navigate in any unstructured environment with a higher degree of belief that there will be no collision with obstacles. Experimental evaluations of the BBNM show that when the robot navigates in the same unstructured environment where knowledge of the obstacles is captured, there is certainty in the degree of belief that the robot can navigate freely without any collision. When the same model was tested for navigation in a new unstructured environment with uncertainties, the results showed a higher assurance or degrees of belief that the robot will not collide with obstacles. The results of our modelling approach show that Bayesian Networks (BNs) have good potential for guiding the behaviour of robots when avoiding obstacles in any unstructured environment.

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A New Ensemble Learning Algorithm Combined with Causal Analysis for Bayesian Network Structural Learning

Symmetry ◽

10.3390/sym12122054 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2054

Author(s):

Ming Li ◽

Ren Zhang ◽

Kefeng Liu

Keyword(s):

Bayesian Network ◽

Ensemble Learning ◽

Causal Reasoning ◽

Learning Algorithm ◽

Training Sample ◽

Causal Analysis ◽

Local Optimum ◽

Structural Learning ◽

Ensemble Learning Algorithm ◽

Bagging Method

The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing BN structure. Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (EL) and causal analysis to propose a new BN structural learning algorithm named C-EL. Combined with the Bagging method and causal Information Flow theory, the EL mechanism for BN structural learning is established. Base learners of EL are trained by using various SS algorithms. Then, a new causality-based weighted ensemble way is proposed to achieve the fusion of different BN structures. To verify the validity and feasibility of C-EL, we compare it with six different SS algorithms. The experiment results show that C-EL has high accuracy and a strong generalization ability. More importantly, it is capable of learning more accurate structures under the small training sample condition.

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