Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A greedy knowledge acquisition method for the rapid prototyping of knowledge structures
Proceedings of the 3rd international conference on Knowledge capture
Learning the DAG of bayesian belief networks by asking (conditional) (in-)dependence questions
Proceedings of the fifth international conference on Knowledge capture
| Hi-index | 0.00 |
Bayesian belief networks (BBNs) are a standard tool for building intelligent systems in domains with uncertainty for diagnostics, therapy planning and user-modelling. Modelling their qualitative and quantitative parts requires sometimes subjective data acquired from domain experts. This can be very time consuming and stressful-causing a knowledge acquisition bottleneck. The main goal of this paper is the presentation of a new knowledge acquisition procedure for rapid prototyping the qualitative part of BBNs. Experts have to provide only simple judgements about the causal precedence in pairs of variables. From these data a new greedy algorithm for the construction of transitive closures generates a Hasse diagram as a first approximation for the qualitative model. Then experts provide only simple judgements about the surplus informational value of variables for a target variable shielded by a Markov blanket (wall) of variables. This two-step procedure allows for very rapid prototyping. In a case-study we and two expert cardiologists developed a first 39 variables prototype BBN within two days.