# Knowledge Acquisition, Representation \& Manipulation in Decision Support Systems

## TL;DR

This paper discusses a methodology for data analysis and knowledge acquisition in decision support systems, emphasizing belief networks, Dempster-Shafer theory, and user-friendly interfaces for probabilistic reasoning.

## Contribution

It introduces a practical approach combining belief networks and Dempster-Shafer theory with interface design for improved decision support systems.

## Key findings

- Application of Dempster-Shafer theory to belief revision
- Development of an interface for probabilistic and DS belief networks
- Discussion of implementation issues and user requirements

## Abstract

In this paper we present a methodology and discuss some implementation issues for a project on statistical/expert approach to data analysis and knowledge acquisition. We discuss some general assumptions underlying the project. Further, the requirements for a user-friendly computer assistant are specified along with the nature of tools aiding the researcher. Next we show some aspects of belief network approach and Dempster-Shafer (DST) methodology introduced in practice to system SEAD. Specifically we present the application of DS methodology to belief revision problem. Further a concept of an interface to probabilistic and DS belief networks enabling a user to understand the communication with a belief network based reasoning system is presented