DECISION ANALYSIS USING IC-BAGS

2004 ◽  
Vol 03 (01) ◽  
pp. 101-108 ◽  
Author(s):  
KANKANA CHAKRABARTY
Keyword(s):  
Expert Systems ◽  
Decision Analysis ◽  
Knowledge Base ◽  
Confidence Level ◽  
Rule Based ◽  
Bias Factor ◽  
Minimum Numbers ◽  
Requirement Specifications ◽  
Integrated Problems

In this paper, we discuss the notion of IC-Bags as introduced by the author, some issues related to IC-Bag based systems and their application in decision analysis. Sometimes it is observed that the semi-structured or unstructured nature of the problems addressed by expert systems can be perceived as the cause for the inability to develop precise requirement specifications. IC-Bags can serve as a tool for building rule-based decision analysis systems which can deal with situations where the count of the objects are not necessarily fixed. We discuss the semi-structured specifications involving maximum and minimum numbers of applications related to the problem-oriented methodology for the integrated problems involving IC-Bags. In this context, the total bias factor and the confidence level of the knowledge base are considered.

Database tools to acquire knowledge for rule-based expert systems

1997 ◽  
Vol 39 (9) ◽  
pp. 607-616 ◽  
Author(s):  
M.M.O. Owrang ◽  
F.H. Grupe
Keyword(s):  
Expert Systems ◽  
Rule Based

Some applications of fuzzy logic in rule-based expert systems

2002 ◽  
Vol 19 (4) ◽  
pp. 208-223 ◽  
Author(s):  
Trung T. Pham ◽  
Guanrong Chen
Keyword(s):  
Fuzzy Logic ◽  
Expert Systems ◽  
Rule Based

Rule-based expert systems for network management and operations: an introduction

IEEE Network ◽  
1988 ◽  
Vol 2 (5) ◽  
pp. 7-21 ◽  
Author(s):  
R.N. Cronk ◽  
P.H. Callahan ◽  
L. Bernstein
Keyword(s):  
Expert Systems ◽  
Network Management ◽  
Rule Based

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

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