Rule-based Production System Research Articles

An approach towards the verification of expert systems using numerical petri nets

A major difficulty that occurs in the construction of large production rule-based expert systems is maintaining the correctness, consistency, and completeness of the knowledge base. A method of transforming the production rules into a numerical petri nets (NPNs) model is proposed. These NPNs are high level nets that are necessary to effectively model production rules. the net model is then analysed by using a computer-aided tool to perform reachability analysis. an algorithm is given to generate the reachability set of the nets. This allows the verification of the correctness, consistency, and completeness of the knowledge base. Examples showing the use of this approach are given.

A KNOWLEDGE-BASED NAVIGATION SCHEME FOR AUTONOMOUS LAND VEHICLES

A knowledge-based navigation system for autonomous land vehicles (ALVs) has been developed which can successfully negotiate an obstacle and threat-laden terrain, even if nothing is known beforehand about the terrain. The ALV stores new information in its memory as it travels, has the ability to backtrack out of unexpected dead ends, and performs spontaneous decision making in the field based on local sensor readings. The optimal global route of the ALV journey is obtained using dynamic programming, and decision making is accomplished via a production rule-based system. Execution examples demonstrate the power of the prototype system to solve navigation problems. This establishes the feasibility of constructing a valid ALV by combining search techniques with artificial intelligence tools such as production rule-based systems.

Development of an Expert System for the Design Synthesis and Reliability Assessment

An expert system is developed for design synthesis and reliability analysis. It uses a rule-based production system called KOPS, and reliability analysis is composed in computer codes CAT, FTAP, and IMPORTANCE. These two system parts are linked. This expert system is applied to the design of an auxiliary feedwater system with various design alternatives and performs a successful reliability analysis.

Development of An Expert Systems for Ergonomic Workplace Design and Evaluation

Industrial workplace design and evaluation is the outcome of a multi-factored process which requires diverse disciplines and interrelated techniques to consider alternative factors and to achieve an optimal design solution. However, it is still evident that industrial workplace design fails to incorporate ergonomic principles throughout all stages of design and evaluation, One approach to solve the problem is to introduce an expert system for integrating existing analytic models and expertises into a framework which guides the designer along the necessary steps to reach a solution, with explanations on its reasoning process. This paper discusses the framework of the prototype expert system being implemented using VM/PROLOG on IBM VM/CMS mainframe. The rule-based production system was selected as a representation scheme due to its versatility and expressive power. It is consisted of two main parts, First, modularized knowledge bases incorporate multidisciplinary ergonomic factors such as biomechanics, work physiology, and psychophysics. Each module stores knowledge either in the fact base or the rule base, however, massive experimental findings and table-lookups are separately stored in the external database through its interface and retrieved without bothering the main inference mechanism. Secondly, inference mechanism was built as a control mechanism, with a front-end user interface. It has a pattern-directed architecture coupled with a normal forward/backward chaining mechanism. The prototype expert system also incorporates analytical models (usually written in FORTRAN) into the reasoning process so that it is highly flexible to the problem specificity.

I-Test: integrated testing expert system for trunks

A prototype trunk-testing expert system developed by Bell Communications Research (Bellcore) is described. The integrated-testing expert-system for trunks (I-TEST) mechanizes and automates many of the conventional manual testing procedures in a switching control center (SCC) of a Bellcore client company (BCC). I-TEST demonstrates the usefulness of integrating artificial intelligence (AI) techniques into the network operations environment. I-TEST was developed in the INTELEWINDOWS environment. This environment includes a windowing system, graphical kernel system (GKS), and a set of high-performance portable C-based AI tools. I-TEST combines traditional structured procedural knowledge and a typical rule-based production system into one inference mechanism to manage a complex trunk-testing environmental in the SCC. I-TEST is an effort to use one intelligent system to integrate all of the future network testing needs, and provide an automated testing environment. The modular design and generic nature of I-TEST allow it to be easily reconfigured to include test access to additional operations systems or network elements. >

An object-oriented design system shell

We present a design system shell which can be used to experiment with principles of design and be used as a design tool where complex layers of information need to be specified about objects, such as in database design. The shell can be tailored to a variety of application areas. It is object-oriented in its implementation and structure. Objects and messages are used as the specification language. The basic ingredients of a rule-based production system are provided, with rules treated as objects and defined independently of the classes to which they are applied.

Knowledge representation and planning control in an expert system for the creative design of mechanisms

An interactive system, referred to as MECXPERT {Mechanism Expert}, has been designed with the expressed purpose of assisting nonexpert design engineers in creating mechanisms for fulfilling specific motion-conversion and/or power-transmission requirements. The particular knowledge representation scheme chosen for this application comprises a hybrid formulation of a rule-based production system with a frame-based approach. The underlying control strategy is based on a series of special-purpose, domain-specific operators whose function is to move from one problem space to another through various stages or ‘states’ that comprise the mechanism design process.The primary focus of this paper centers on the representation of knowledge and its control within an expert system for creative mechanism design. An overview summarizing the reasons for developing such an expert system is provided, and the formulation of a problem is discussed through an example taken from the design of a variable-stroke internal-combustion engine mechanism.

Strategies for Diagnosis

Diagnosis is among the dominant applications of expert systems technology today. In the past, most diagnostic systems were built using some form of rule-based production system. Recently, however, many new strategies have emerged to support much more complex reasoning for diagnosis. A survey of the architectures for the complex strategies now available for diagnosis is presented.

Execution of OPS5 production systems on a massively parallel machine

In recent years, the development of expert systems implemented by rule-based production systems has emerged as one of the dominant paradigms in the field of artificial intelligence. While production systems offer important advantages in large-scale AI applications, their use in such applications is typically very costly in execution time. In this paper, we describe an algorithm for executing production systems expressed in the OPS5 language on a massively parallel multiple-SIMD machine called NON-VON, portions of which are currently under construction at Columbia University. The algorithm, a parallel adaptation of Forgy's Rete Match, has been implemented and tested on an instruction-level simulator. We present a detailed performance analysis, based on the implemented code, for the averaged characteristics of six production systems having an average of 910 inference rules each. The analysis predicts an execution rate of more than 850 production firings per second using hardware comparable in cost to a VAX 11/780. By way of comparison, a LISP-based OPS5 interpreter running on a VAX 11/780 typically fires 1 to 5 rules per second, while a Bliss-based interpreter executes 5 to 12 rules per second.

Automated reasoning about image motion using a rule-based deduction system

A rule-based production system for the identification and structural description of image motion is presented. The system employs a set of intermediate-level motion description rules of antecedent-consequent form and a set of low-level, incomplete and possibly incorrect local spatiotemporal motion features together with a forward-backward chaining paradigm to produce or verify high-level motion descriptions. The system has been implemented in LISP and provides a basis for more complex designs, perhaps employing motion-specific frames and iterative deduction and feature extraction. Sample results and future research directions are presented.