Building probabilistic networks: "Where do the numbers come from?" guest editors' introduction 论文

摘要

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, information retrieval, and natural language processing. A probabilistic network (also referred to as a belief network, Bayesian network, or, somewhat imprecisely, causal network) consists of a graphical structure, encoding a domain’s variables and the qualitative relationships between them, and a quantitative part, encoding probabilities over the variables [29]. Building a probabilistic network for a domain of application involves three tasks. The first of these is to identify the variables that are of importance, along with their possible values. Once the important domain variables have been identified, the second task is to identify the relationships between the variables discerned and to express these in a graphical structure. The tasks of eliciting the variables and values of importance as well as the relationships between them from domain experts is comparable, to at least some extent, to knowledge engineering for other artificial-intelligence representations and, although it may require significant effort, is generally considered doable. The last task in building a probabilistic network is to obtain the probabilities that are required for its quantitative part. This task often appears more daunting: “Where do the numbers come from?” is a commonly asked question. The three tasks in building a probabilistic network are, in principle, performed one after the other. Building a network, however, often requires a careful trade-off between the desire for a large and rich model to obtain accurate results on the one hand, and the costs of construction and maintenance and the complexity of probabilistic inference on the other hand. In practice, therefore, building a probabilistic network is a process that iterates over these tasks until a network results that is deemed requisite. In collaboration with Finn V. Jensen and Max Henrion, we organised in 1995 a workshop devoted to the theme of obtaining the numbers, the most daunting task in building probabilistic networks [14]. The workshop was held in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI’95) and had a programme of presentations of selected contributions and ample slots for flash communications and discussion. Scientists from such disciplines as decision analysis, statistics, and computer science attended the workshop. The interest in the workshop, both during IJCAI’95 and afterwards, prompted us to follow up on the theme. The current issue of IEEE Transactions on Data and Knowledge Engineering is the result.