Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Probabilistic inference in multiply connected belief networks using loop cutsets
International Journal of Approximate Reasoning
On Spohn's rule for revision of beliefs
International Journal of Approximate Reasoning
Local expression languages for probabilistic dependence: a preliminary report
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Search-based methods to bound diagnostic probabilities in very large belief nets
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Reasoning with qualitative probabilities can be tractable
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Conditional logics of normality: a modal approach
Artificial Intelligence
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Plan simulation using Bayesian networks
CAIA '95 Proceedings of the 11th Conference on Artificial Intelligence for Applications
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Conditioning algorithms for exact and approximate inference in causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Conditioning algorithms for exact and approximate inference in causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Some experiments with real-time decision algorithms
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
We present an algorithm, called Predict, for updating beliefs in causal networks quantified with order-of-magnitude probabilities. The algorithm takes advantage of both the structure and the quantification of the network and presents a polynomial asymptotic complexity. Predict exhibits a conservative behavior in that it is always sound but not always complete. We provide sufficient conditions for completeness and present algorithms for testing these conditions and for computing a complete set of plausible values. We propose Predict as an efficient method to estimate probabilistic values and illustrate its use in conjunction with two known algorithms for probabilistic inference. Finally, we describe an application of Predict to plan evaluation, present experimental results, and discuss issues regarding its use with conditional logics of belief, and in the characterization of irrelevance.