IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Abductive reasoning in Bayesian belief networks using a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Validation of relative feature importance using natural data
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
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The feasibility of diagnostic reasoning in a Bayesian belief network, based on a genetic algorithm is demonstrated. The reasoning process described here is an example of approximate reasoning. Since exact abduction in a network modelling the ''classical diagnostic problem'' is NP-hard, inexact or approximate reasoning attracts much attention. The results of the present study indicate that in a given context of observed symptoms, a genetically generated population of possible solutions retains much of the diagnostic power contained in the full model: the disease probabilities as occuring in this population and as calculated from the full model are strongly rank-correlated. Moreover, the disease-symptom correlations are retained in the genetically generated population. This is important, since these probabilities and correlations are dynamic quantities which depend on the context of observed symptoms. The genetic algorithm may be seen as a procedure to dynamically generate diagnostic protocols.