Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space

2005 ◽  
pp. 63-75 ◽  
Author(s):  
M. Julia Flores ◽  
José A. Gámez ◽  
Serafín Moral
Keyword(s):  
Bayesian Networks ◽  
Abductive Inference

Partial Abductive Inference in Bayesian Networks by Using Probability Trees

2004 ◽  
pp. 146-154 ◽  
Author(s):  
Luis M. de Campos ◽  
José A. Gámez ◽  
Serafín Moral
Keyword(s):  
Bayesian Networks ◽  
Abductive Inference ◽  
Probability Trees

Abductive inference in Bayesian networks using distributed overlapping swarm intelligence

2014 ◽  
Vol 19 (4) ◽  
pp. 981-1001 ◽  
Author(s):  
Nathan Fortier ◽  
John Sheppard ◽  
Shane Strasser
Keyword(s):  
Bayesian Networks ◽  
Swarm Intelligence ◽  
Abductive Inference

Constraint Handling Using Tournament Selection: Abductive Inference in Partly Deterministic Bayesian Networks

2009 ◽  
Vol 17 (1) ◽  
pp. 55-88 ◽  
Author(s):  
Severino F. Galán ◽  
Ole J. Mengshoel
Keyword(s):  
Bayesian Networks ◽  
Conditional Probability ◽  
Evolutionary Approach ◽  
Abductive Inference ◽  
The Novel ◽  
Number Of Zeros ◽  
Novel Approach ◽  
Tournament Selection ◽  
Most Probable Explanation ◽  
Areas Of Interest

Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal explanations. A problem with the traditional evolutionary approach is this: As the number of constraints determined by the zeros in the conditional probability tables grows, performance deteriorates because the number of explanations whose probability is greater than zero decreases. To minimize this problem, this paper presents and analyzes a new evolutionary approach to abductive inference in BNs. By considering abductive inference as a constraint optimization problem, the novel approach improves performance dramatically when a BN's conditional probability tables contain a significant number of zeros. Experimental results are presented comparing the performances of the traditional evolutionary approach and the approach introduced in this work. The results show that the new approach significantly outperforms the traditional one.

An Evolutionary Computing Approach to Probabilistic Reasoning on Bayesian Networks

1996 ◽  
Vol 4 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Carlos Rojas-Guzmán ◽  
Mark A. Kramer
Keyword(s):  
Bayesian Networks ◽  
Probabilistic Reasoning ◽  
Bayesian Belief Networks ◽  
Exact Algorithms ◽  
Abductive Inference ◽  
Distributed Network ◽  
A Posteriori ◽  
System Description ◽  
Most Probable Explanation ◽  
Computing Approach

Bayesian belief networks can be used to represent and to reason about complex systems with uncertain or incomplete information. Bayesian networks are graphs capable of encoding and quantifying probabilistic dependence and conditional independence among variables. Diagnostic reasoning, also referred to as abductive inference, determining the most probable explanation (MPE), or finding the maximum a posteriori instantiation (MAP), involves determining the global most probable system description given the values of any subset of variables. In some cases abductive inference can be performed with exact algorithms using distributed network computations, but the problem is NP-hard, and complexity increases significantly with the presence of undirected cycles, the number of discrete states per variable, and the number of variables in the network. This paper describes an approximate method composed of a graph-based evolutionary algorithm that uses nonbinary alphabets, graphs instead of strings, and graph operators to perform abductive inference on multiply connected networks for which systematic search methods are not feasible. The motivation, basis, and adequacy of the method are discussed, and experimental results are presented.

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