Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An Introduction to the Modeling of Neural Networks
An Introduction to the Modeling of Neural Networks
Learning Bayesian Networks
Learn to Tango with D
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Solving ramified optimal transport problem in the bayesian influence diagram framework
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Stochastic techniques in influence diagrams for learning bayesian network structure
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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The problem of solving general Bayesian influence diagrams is well known to be NP-complete, whence looking for efficient approximate stochastic techniques yielding suboptimal solutions in reasonable time is well justified. The purpose of this paper is to propose a new stochastic algorithm for strategy optimisation in Bayesian influence diagrams. The underlying idea is an extension of that presented in [2] by Chen who developed a self-annealing algorithm for optimal tour generation in traveling salesman problems (TSP). Our algorithm generates optimal decision strategies by iterative self-annealing reinforced search procedure, gradually acquiring new information while driven by information already acquired. The effectiveness of our method has been tested on computer-generated examples.