Production Rule Language Research Articles

Failsafe: supporting product quality with knowledge-based systems

This article describes the design, development, implementation and impact of an expert system used in Quality Assurance in the food industry. The knowledge base of the system was developed by a non-programmer in a high level production rule language. The introduction of the system led to improved decision-making at the plant level and improved reporting to meet internal and external quality requirements. The system also contributed to team based learning. The system is likely to save one plant over US$100,000 due to decreased consumer complaints.

Building a composite syntax for expert system shells

The authors examine the syntactic similarities and differences of five expert system shell production-rule languages. To help knowledge engineers manage information, they develop a composite production-rule syntax that provides a common language for defining production rules. The expert system shells are the: Integrated Reasoning Shell (TIRS); Expert System Environment (ESE), KnowledgeTool; Knowledge Engineering Environment (KEE); and Official Production System Version 5 (OPS5).

Coupling production systems and database systems: a homogeneous approach

Methods for storing and manipulating large rule bases using a relational database management systems (DBMS) are discussed. An approach to decomposing and storing the condition elements in the antecedents of rules such as those used in production rule-based systems is presented. A set-oriented approach, DBCond, which uses a special data structure that is implemented using relations is proposed. A matching algorithm for DBCond uses the relational structures to efficiently identify rules whose antecedents are satisfied. The performance of DBCond is compared with that of DBRete, a DBMS implementation of the Rete match algorithm developed for use with the production rule language OPS5. DBCond is also compared with DBQuery, a method that is based on evaluating queries corresponding to the conditions in the antecedents of the rules. Improvements to the data structure and the algorithms of the DBCond method are described. An advantage of DBCond is that it is fully parallelizable, thus making it attractive for parallel computing environments. >

A denotational semantics for the Starburst production rule language

Researchers often complain that the behavior of database production rules is difficult to reason about and understand, due in part to the lack of formal declarative semantics. It has even been claimed that database production rule languages inherently cannot be given declarative semantics, in contrast to, e.g., deductive database rule languages. In this short paper we dispute this claim by giving a denotational semantics for the Starburst database production rule language.

A rule language compiler for SQL database servers

This paper presents the design and the implementation of a rule language compiler for databases. The RDL/C language supports both declarative programming based on a production rule language and C-based procedural programming. The language supports domain variables which can appear in rules. These variables are monitored by the inference engine and included in the semantics of rule firing. A partial ordering among rules is available to the user. The RDL/C compiler translates RDL/C source code into C code with embedded SQL statements. Its implementation is compared to fully integrated deductive databases and to loosely coupled systems. The contribution of the paper is to show how the rule based paradigm for database can be used as a framework for a general purpose database application generator.

An Expert System for Lettuce Handling at a Retail Store

ABSTRACT AN expert system prototype (LETTMAN) was developed to simulate the decision-making process of a produce manager in a retail store. Lettuce was the commodity studied and a produce manager served as the domain expert. From several interview sessions with the domain expert, a flowchart was developed to illustrate the decisions considered, and a knowledge base was created using the production rule language of the expert system shell Insight2+. Results of the study showed that quality determination, price adjustment, potential sales and inventory control were the primary decision areas. Evaluation of LETTMAN indicated the need to incorporate reasoning schemes other than confidence factors in modeling lettuce quality decisions.

Making deductive databases a practical technology: a step forward

Deductive databases provide a formal framework to study rule-based query languages that are extensions of first-order logic. However, deductive database languages and their current implementations do not seem appropriate for improving the development of real applications or even sample of them. Our goal is to make deductive database technology practical. The design and implementation of the RDL1 system, presented in this paper, constitute a step toward this goal. Our approach is based on the integration of a production rule language within a relational database system, the development of a rule-based programming environment and the support of system extensibility using Abstract Data Types. We discuss important practical experience gained during the implementation of the system. Also, comparisons with related work such as LDL, STARBURST and POSTGRES are given.

The design and implementation of an extendible deductive database system

This paper presents the design and implementation of a deductive database system. RDL1, is the production rule language for this system. This language is an extension of logic based languages towards the support of updates and complex domains. Different features of this language are described with its applications. The general architecture of the system is presented highlighting integration with a relational database system. Then, three original features of the system are discussed: extensibility through the definition of end user data types, query optimization techniques, and the extensibility of the control strategy. Future improvements and extensions conclude the paper.

Software Reviews : Insight 2+, Version 1.3 Publisher: Level Five Research, Inc., 503 Fifth Avenue, Indialantic, FL 32903 (telephone: 305-729-9046; bulletin board: 305-724-0909) Year of Publication: 1986 Materials: 3 DSDD disks (not copy-protected), loose-leaf reference manual Price: $485 (academic discount available) Availability: IBM PC and compatibles (Macintosh version forthcoming) System Requirements: 256K RAM, two double-sided drives, PC/MS DOS 2.0 or

This is an ideal program for Pascalphiles who are looking for an integrated expert systems development program. It has its own separate editors and compilers for building the knowledge base (rules and facts) and databases or other programs to be called up when the knowledge base runs. The knowledge base must be written in Production Rule Language (PRL), which is powerful and easy to learn.