KNOWLEDGE BASED DECISION SUPPORT SYSTEM

Another important emerging DSS sub-specialty is the study of knowledge-based decision support systems (KBDSS), which are hybrid systems of DSS and ES that help solve a broad range of organizational problems. In integrating DSS and ES, two basic approaches are discernible and labeled expert support systems (ESS) and intelligent support systems (ISS) (King 1993). The key differences between these two systems are as follows. ESS is to replace human expertise with machine expertise, while ISS are to amplify the memory and intelligence of humans and groups (King 1993). A broad range of real-world managerial problems can be better solved by using the analysis of both quantitative and qualitative data. Few would disagree with the notion that there are considerable benefits from integrating DSS and ES. The new integrated system (ESS or ISS) can support decision makers by harnessing the expertise of key organizational members. A bottleneck in the development of knowledge-based systems such as ESS is knowledge acquisition, which is a part of knowledge engineering – the process includes representation, validation, inferencing, explanation and maintenance.

increasing number of systems are incorporating domain knowledge, modelling, and analysis systems to provide users the capability of intelligent assistance. Knowledge base modules are being used to formulate problems and decision models, and analyse and interpret the results. Some systems are adding knowledge-based modules to replace human judgments. Managerial judgements have been used to ascertain (assess) future uncertainty and to select assumptions on which decision models can be based. Some decisions are both knowledge and data intensive. Consequently, a large amount of data usually requires considerable efforts for their interpretation and use.

The knowledge-based DSS include a knowledge management component which stores and manages a new class of emerging AI tools such as machine learning and case-based reasoning and learning. These tools can obtain knowledge from prior data, decisions and examples (cases), and contribute to the creation of DSS to support repetitive, complex real-time decision making. Machine learning refers to computational methods/tools of a computer system to learn from experience (past solutions), data and observations, and consequently alter its behaviour, triggered by a modification to the stored knowledge. Artificial neural networks and genetic algorithms are the most notable approaches to machine learning.

The role of knowledge-based DSS should be to allow experts to broaden and expand their expertise, not to narrow it down.