Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Partial abductive inference in Bayesian belief networks using a genetic algorithm
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Parameter adjustment in Bayes networks. the generalized noisy OR-gate
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Influence diagrams with multiple objectives and tradeoff analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A factorization approach to evaluating simultaneous influence diagrams
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Strategic planning can be schematised as a decision making process where, given a general outline of the desirable future, the decision makers need to choose a set of actions that should coherently lead a system (corporation, institution, city, region, etc.) toward that future. A more sophisticated case is when rather than only choosing actions, the decision maker also decides the allocation of available resources among different available actions. We show that in most cases the problem can be faced using a particular Decision Network with multiple objectives, in which actions are applied simultaneously and are modelled by variables representing the efforts spent on them. The main advantage of the proposed Simultaneous Decision Network is that it can be easily built by a panel of domain experts, under the assumption of the noisy-OR causal interaction. The problem of finding the best strategy in terms of resource allocation is formulated as a combinatorial optimisation, and solved through a multi-objective meta heuristic approach.