Abstract not found

The number of knowledge-based systems that build on Bayesian belief networks is increasing. The construction of such a network however requires a large number of probabilities in numerical form. This is often considered a major obstacle, one of the reasons being that experts are reluctant to provide numerical probabilities. The use of verbal probability expressions as an additional method of eliciting probabilistic information may to some extent remove this obstacle. In this paper, we review studies that address the communication of probabilities in words and/or numbers. We then describe our own experiments concerning the development of a probability scale that contains words as well as numbers. This scale appears to be an aid for researchers and domain experts during the elicitation phase of building a belief network and might help users understand the output of the network.

Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision,

We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group's expertise.

Diagnosing ventilator-associated pneumonia in mechanically ventilated patients in intensive care units is seen as a clinical challenge. The difficulty in diagnosing ventilator-associated pneumonia stems from the lack of a simple yet accurate diagnostic test. To assist clinicians in diagnosing and treating patients with pneumonia, a decision-theoretic network had been designed with the help of domain experts. A major limitation of this network is that it does not represent pneumonia as a dynamic process that evolves over time. In this paper, we construct a dynamic Bayesian network that explicitly captures the development of the disease over time. We discuss how probability elicitation from domain experts served to quantify the dynamics involved and how the nature of the patient data helps reduce the computational burden of inference. We evaluate the diagnostic performance of our dynamic model for a number of real patients and report promising results. 1

CONTENTS 1. Introduction 2. Practical Reasoning 3. Argument Schemes and Defeasibility 4. Decision Calculi 5. Reasoning under Resource Constraints 6. Moral Considerations 7. Deliberation Dialogue 8. Interface Design 9. Evaluation 10. Summary Group 2 2 Practical Reasoning 1 Introduction When faced with difficult decisions about what to do, decision makers benefit from good advice. Good advice comes most reliably from advisors with relevant expertise. As well, good advice has at least three other essential features. First, the advice should be presented in a form which can be readily understood by the decision maker. Second, there should be ready access to both the information and the thinking that underpins the advice. Third, if decision making involves details which are at all unusual, the decision maker needs to be able to discuss those details with their advisors. Computer based systems are being increasingly used to assist people in decision making. Su

In many realistic problem domains, the main variable of interest behaves monotonically in the observable variables, in the sense that higher values for the variable of interest become more likely with higher-ordered observations. This type of knowledge appears to naturally emerge from experts during knowledge elicitation, without explicit prompting from the knowledge engineer. The experts ’ concept of monotonicity, however, may not correspond to the mathematical concept of monotonicity in Bayesian networks. We present a method that provides both for verifying whether or not a network exhibits the properties of monotonicity suggested by the experts and for studying the violated properties with the experts. We illustrate the application of our method for a real Bayesian network in veterinary science. 1

Human judgment is an essential source of Bayesian probabilities but is plagued by incoherence when complex or conditional events are involved. We consider a method for adjusting estimates of chance over Boolean events so as to render them probabilistically coherent. The method works by searching for a sparse distribution that approximates a target set of judgments. (We show that sparse distributions suce for this purpose.) The feasibility of our method was tested by randomly generating sets of coherent and incoherent estimates of chance over 30 to 50 variables.

Unwieldy probability entry interfaces are the norm for Bayesian Network knowledge engineers. This requires domain experts to provide unreasonably accurate probability estimates. To solve these problems, we have developed two applications: CPTable improves direct probability entry, and provides node customisation and sliding scale binary elicitation; Verbal Elicitor allows entry of probability values in ordinary English. The domain expert selects a verbal cue such as "unlikely" or "almost certain." The probabilities are then set manually or optimised to minimise probabilistic incoherency.

In this paper we present a new method (EBBN) that aims at reducing the need to elicit formidable amounts of probabilities for Bayesian belief networks, by reducing the number of probabilities that need to be specified in the quantification phase. This method enables the derivation of a variable’s conditional probability table (CPT) in the general case that the states of the variable are ordered and the states of each of its parent nodes can be ordered with respect to the influence they exercise. EBBN requires only a limited amount of probability assessments from experts to determine a variable’s full CPT and uses piecewise linear interpolation. The number of probabilities to be assessed in this method is linear in the number of conditioning variables. EBBN’s performance was compared with the results achieved by applying both the normal copula vine approach from Hanea & Kurowicka (2007), and by using a simple uniform distribution. 1