Response-time optimization of rule-based expert systems

1994 ◽  
Author(s):  
Blaz Zupan ◽  
Albert M. K. Cheng
Keyword(s):  
Response Time ◽  
Expert Systems ◽  
Rule Based ◽  
Time Optimization

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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