Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Modeling Causal Reinforcement and Undermining for Efficient CPT Elicitation
IEEE Transactions on Knowledge and Data Engineering
Enumerating Unlabeled and Root Labeled Trees for Causal Model Acquisition
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Towards Effective Elicitation of NIN-AND Tree Causal Models
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Recursive noisy OR - a rule for estimating complex probabilistic interactions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Non-impeding noisy-AND tree causal models over multi-valued variables
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
To specify a Bayes net, a conditional probability table, often of an effect conditioned on its n causes, needs to be assessed for each node. Its complexity is generally exponential in n and hence how to scale up is important to knowledge engineering. The non-impeding noisy-AND (NIN-AND) tree causal model reduces the complexity to linear while explicitly expressing both reinforcing and undermining interactions among causes. The key challenge to acquisition of such a model from an expert is the elicitation of the NIN-AND tree topology. In this work, we propose and empirically evaluate two methods that indirectly acquire the tree topology through a small subset of elicited multi-causal probabilities. We demonstrate the effectiveness of the methods in both human-based experiments and simulation-based studies.