Subjective bayesian networks and human-in-the-loop situational understanding
Author
Type :
Conference paper
Publication Status :
Published
Access :
restrictedAccess
Abstract
In this paper we present a methodology to exploit human-machine coalitions for situational understanding. Situational understanding refers to the ability to relate relevant information and form logical conclusions, as well as identify gaps in information. This process for comprehension of the meaning information requires the ability to reason inductively, for which we will exploit the machines’ ability to ‘learn’ from data. However, important phenomena are often rare in occurrence with high degrees of uncertainty, thus severely limiting the availability of instance data for training, and hence the applicability of many machine learning approaches. Therefore, we present the benefits of Subjective Bayesian Networks—i.e., Bayesian Networks with imprecise probabilities—for situational understanding, and the role of conversational interfaces for supporting decision makers in the evolution of situational understanding.
Source :
International Workshop on Graph Structures for Knowledge Representation and Reasoning GKR 2017: Graph Structures for Knowledge Representation and Reasoning, Part of the Lecture Notes in Computer Science book series (LNCS, volume 10775)
Date :
2018-03-21
Volume :
10775 LNAI
Publisher :
Springer
Collections
Share this page