Decision making is an important element throughout the life-cycle of large-scale projects. Decisions are critical as they have a direct impact upon the success/outcome of a project and are affected by many factors including the certainty and precision of information. In this paper we present an evidential reasoning framework which applies Dempster–Shafer Theory and its variant Dezert–Smarandache Theory to aid decision makers in making decisions where the knowledge available may be imprecise, conflicting and uncertain. This conceptual framework is novel as natural language based information extraction techniques are utilized in the extraction and estimation of beliefs from diverse textual information sources, rather than assuming these estimations as already given. Furthermore we describe an algorithm to define a set of maximal consistent subsets before fusion occurs in the reasoning framework. This is important as inconsistencies between subsets may produce results which are incorrect/adverse in the decision making process. The proposed framework can be applied to problems involving material selection and a Use Case based in the Engineering domain is presented to illustrate the approach.
- Jean Golding
- Evidential reasoning; Dezert–Smarandache Theory; Information extraction; Textual entailment; Natural language processing; Discounting techniques; Information fusion; Knowledge and data engineering
Browne, F., Rooney, N., Liu, W., Bell, D., Wang, H., Taylor, P. S., & Jin, Y. (2013). Integrating textual analysis and evidential reasoning for decision making in Engineering design. Knowledge-Based Systems, 52, 165-175. https://doi.org/10.1016/j.knosys.2013.07.014