C Language Integrated Production System Research Articles

A fuzzy expert system for mitigation of risks and effective control of gas pressure reduction stations with a real application

Environmental changes and increased uncertainty due to technical damage, explosions and large fires have caused the risk of an inevitable element in the gas industry. This study purposes developing a new hybrid fuzzy expert system as a decision support system to mitigate the risk associated with gas transmission stations. The designed knowledge-based system combines the procedural and descriptive rules based on experts’ judgments to analyze the complex relationships between the different components of a gas pressure reduction station. The developed fuzzy expert system is coded in C language integrated production system (CLIPS) and is linked with MATLAB software for calling fuzzy functions. A real case study of gas pressure reduction stations in Iranian gas industry is conducted to validate the proposed expert system model. The expert system provides more than one thousand rules based on expert knowledge to prevent the pressure drop and the quality loss of gas or shutting off gas flow which accordingly increases gas flow stability. The proposed expert system could minimize the risk of hazardous scenarios, such as leakage and corrosion, in the gas industry and provide an acceptable precision in the provision of periodic control strategies and appropriate response under an emergency condition.

Prototyping Rule-Based Expert Systems with the Aid of Model Transformations

The problem of improving efficiency of intelligence systems engineering remains a relevant topic of scientific research. One of the trends in this area is the use of the principles of cognitive (visual) modelling and design as well as approaches based on generative programming and model transformations. This paper aims to describe the implementation and application of model transformations for prototyping rule-based knowledge bases and expert systems. The implementation proposed uses the main principles of the Model Driven Architecture (MDA) (e.g., model types and creation stages) and considers the features of developing intelligent systems. Therefore, the current research employs the following tools: Ontologies for the representation of the computation-independent model; the author’s original notation, namely, the Rule Visual Modelling Language (RVML) to create the platform-independent and platform-specific models; the C Language Integrated Production System (CLIPS) and the Drools Rule Language (DRL) as the programming languages (as the platforms). The approach proposed targets non-programmers (domain experts and analytics) and makes the design process of rule-based expert systems and knowledge bases more efficient. The paper also presents a detailed description of the main elements of the approach including models, transformations and a specialised software (Personal Knowledge Base Designer).

Medical Diagnosis Expert System for Malaria and Related Diseases for Developing Countries

There is a strong need for cost-effective technologies to manage disease processes and thus reduce morbidity and mortality in the developing countries. Yet bringing intelligent healthcare informatics to bear on the dual problems of reducing healthcare costs and improving quality and outcomes is a challenge even in countries with a reasonably developed technology infrastructure. This paper focused at how appropriate an ap-plication of Medical Diagnosis Expert System (MDES) is to manage diseases in developing countries. MDES is usually designed to enable clinicians to identify diseases and describe methods of treatment to be carried out taking into account the user capability. The MDES described here is implemented using the C Language Integrated Production System (CLIPS). The CLIPS is an expert system, which has a shell composed of four modules: the user interface, the explanation system, the inference engine and the knowledge base editor. In the system, a number of patient cases will be selected as prototypes and stored in a separate database. The knowledge is acquired from literature review, human experts and the internet of the specific domain and is used as a base for analysis, diagnosis and recommendations.

An Automated Approach for Web Services Architectural Style Selection

Selection of an appropriate architectural style is vital to the success a web service. The nature of architecture design and selection for service oriented computing applications is quite complex as compared to traditional software architecture. Web Services have complex and rigorous architectural styles with their own underlying architectural characteristics. Due to this, selection for accurate architectural style for web services development has become more complex decision to be made by architects. Architectural style selection is a multi-criteria decision and demands lots of experience in service oriented computing. There is a huge gap for automated selection of web services architectural styles. Decision support systems are good solution to simplify the selection process of a particular architectural style. Our research suggests an automated approach using DSS for selection of architectural styles while developing a web service to cater FRs & NFRs (Functional & Non Functional Requirements). Our proposed mechanism helps architects to select right web service architectural pattern according to domain, and non-functional requirements without compromising quality. In this paper a rule base DSS has been developed using CLIPS (C Language Integrated Production System) to support decision process in multi-criteria requirements. To select suitable web service, system takes architectural characteristics, domain requirements and software architect preferences for NFRs as input by applying rule base approach. Next Weighted Sum Model has been applied to prioritize quality attributes and domain requirements. Scores are calculated using multiple criterions to choose the final architecture style.

A Survey on State-of-the-Art Knowledge-based System Development and Issues

The human brain can store countless folds of knowledge. Still, we cannot fully utilize a single human brain to solve a specific problem. Knowledge-based systems (KBSs) are computer programs specifically developed to perform problem solving like human experts. These systems effectively expand efficiency and flawlessly solve problems in various fields. The structure of a KBS can be divided into five standard components, which are described in detail. The component used for storage is called a knowledgebase, while an inference engine is a software module that processes the knowledge stored in the knowledgebase. Different tools, shells and programming languages can be used to develop and utilize KBSs. The most popular development languages are List Programming (Lisp), Prolog, Java Expert Systems Shell (JESS) and C Language Integrated Production System (CLIPS). This paper highlights various known issues in the phases of the development, deployment and maintenance of KBSs. Developers, as well as end users, may face various issues in the life cycle of these systems. The key personalities involved in the development and maintenance of KBSs are knowledge engineers and domain knowledge experts. Most issues are related to the experts’ availability, their behavior and long-term cooperation.

Developing a dynamic inference expert system to support individual learning at work

AbstractIn response to the rapid growth of information in recent decades, knowledge‐based systems have become an essential tool for organizational learning. The application of electronic performance‐support systems in learning activities has attracted considerable attention from researchers. Nevertheless, the vast, ever‐increasing amount of information is creating management problems regarding the efficiency and accuracy of knowledge retrieval. This study aims to develop a dynamic inference expert system (DIES) to solve these problems. DIES integrates web technology, knowledge management, Extensible Markup Language (XML) and C Language Integrated Production System (CLIPS) to facilitate the convenience of knowledge retrieval and the efficiency of learning at work. The advantages of XML include scalability, sharing, easy readability and the ability to enhance the efficient management of knowledge‐based systems. In DIES, organizational knowledge is automatically generated as machine‐readable language and knowledge description, using CLIPS as a rule‐conduction tool to develop specific organizational knowledge bases. A data source consisting of 22 participants from different industries was employed to examine the effectiveness of DIES. Results indicate that participants intend to apply DIES within their industries, with a 95% probability of attaining above‐average satisfaction.

Research on Knowledge-Based Injection Mould Project Design System

The intelligent design technology based on the Expert System (ES) was applied into injection mould design. The experts’ knowledge and experience related to the injection mould project design (IMPD) was expressed in the form of production rule. The knowledge base of intelligent project design of injection mould was constructed. The knowledge automatic reasoning via CLIPS (C Language Integrated Production System) knowledge deducing machine was realized. The achievement of the injection mould project’s intelligent design was reached. The research on the injection mould intelligent design based on CLIPS was made. The intelligent design system for injection mould project (IDSIMP) was developed. The injection mould design project could be provided to the injection mould design engineers via the system. The system is of better application value on engineering.

CLIPS based decision support system for water distribution networks

Abstract. The difficulty in knowledge representation of a water distribution network (WDN) problem has contributed to the limited use of artificial intelligence (AI) based expert systems (ES) in the management of these networks. This paper presents a design of a Decision Support System (DSS) that facilitates "on-demand'' knowledge generation by utilizing results of simulation runs of a suitably calibrated and validated hydraulic model of an existing aged WDN corresponding to emergent or even hypothetical but likely scenarios. The DSS augments the capability of a conventional expert system by integrating together the hydraulic modelling features with heuristics based knowledge of experts under a common, rules based, expert shell named CLIPS (C Language Integrated Production System). In contrast to previous ES, the knowledge base of the DSS has been designed to be dynamic by superimposing CLIPS on Structured Query Language (SQL). The proposed ES has an inbuilt calibration module that enables calibration of an existing (aged) WDN for the unknown, and unobservable, Hazen-Williams C-values. In addition, the daily run and simulation modules of the proposed ES further enable the CLIPS inference engine to evaluate the network performance for any emergent or suggested test scenarios. An additional feature of the proposed design is that the DSS integrates computational platforms such as MATLAB, open source Geographical Information System (GIS), and a relational database management system (RDBMS) working under the umbrella of the Microsoft Visual Studio based common user interface. The paper also discusses implementation of the proposed framework on a case study and clearly demonstrates the utility of the application as an able aide for effective management of the study network.

ESP: An expert system for poisoning diagnosis and management

We describe a clinical decision support system (CDSS) designed to provide timely information germane to poisoning. The CDSS aids medical decision making through recommendations to clinicians for immediate evaluation. The system is implemented as a rule-based expert system with two major components: the knowledge base and the inference engine. The knowledge base serves as the database which contains relevant poisoning information and rules that are used by the inference engine in making decisions. This expert system accepts signs and symptoms observed from a patient as input and presents a list of possible poisoning types with the corresponding management procedures which may be considered in making the final diagnosis. A knowledge acquisition tool (KAT) that allows toxicological experts to update the knowledge base was also developed. This article describes the architecture of the fully featured system, the design of the CDSS and the KAT as web applications, the utilisation of the inferencing mechanism of C Language Integrated Production System (CLIPS), which is an expert system shell that helps the system in decision-making tasks, the methods used as well as problems encountered. We also present the results obtained after testing the system and propose some recommendations for future work.

CAP_USO: Um sistema especialista para determinação da capacidade de uso da terra

The objective of this paper was to develop an expert system (CAP_USO) to determine the land use capability in order to aid and speed up the decision-making process in land use planning. The system was developed by using CLIPS (C Language Integrated Production System), an expert system designed by NASA (National Aeronautics and Space Administration). The slope and twelve other factors that limit the land use were considered in order to determine the land capability. The validation of the system showed that the CAP_USO was rigorous to determine the land capability because it considers the slope and takes into account other factors that present major restrictions to the land use. The technique for expert systems showed to be efficient to determine land capability, which facilitates the manipulation and the processing of the necessary information.

Development of Expert Systems for the Design of a Hot-Forging Process Based on Material Workability

Most of the time (and cost) involved in planning hot forging process is related to activities strongly dependent on human expertise, intuition, and creativity, and also to iterative procedure involving extensive experimental work. In this paper, the development of an expert system for forging process design, which emphasizes materials' workability, is discussed. Details of the forging process design expert system, its basic modules, design and implementation details, and deliverables are explained. The system uses the vast database available on the hot workability of more than 200 technologically important materials and the knowledge acquired from a materials' expert. The C Language Integrated Production System (CLIPS) has been adopted to develop this expert system. The expert system can address three types of functions, namely, forging process design, materials information system, and forging defect analysis. The expert system will aid and prompt a novice engineer in designing a forging process by providing accurate information of the process parameters, lubricants, type of machine, die material, and type of process (isothermal versus non-isothermal) for a given material with a known specification or code and prior history.

Knowledge acquisition assisted by CLIPS programming

Knowledge acquisition is important and machine learning techniques can be used to achieve automated knowledge acquisition. This article examines how knowledge acquisition can be assisted by programming using CLIPS (an acronym for C Language Integrated Production System). A machine learning preprocessor has been developed for the CLIPS environment, so that the CLIPS rule-base can be expanded by adding rules generated through machine learning techniques. The paper also shows how knowledge updating can be supported in the CLIPS environment itself. Operational engineering knowledge is captured in a data structure called a decision tree, and its structure can be updated when new knowledge is acquired. In addition, some advanced features are also briefly discussed, including using COOL (the CLIPS object-oriented language) for knowledge acquisition in a software product recommendation system, as well as the design of a self-evolving knowledge-acquisition tool.

Extending CLIPS to support temporal representation and reasoning

Applications using expert systems for monitoring and control problems often require the ability to represent temporal knowledge and to apply reasoning based on that knowledge. Incorporating temporal representation and reasoning into expert systems leads to two problems in development: dealing with an implied temporal order of events using a non‐procedural tool; and maintaining the large number of temporal relations that can occur among facts in the knowledge base. In this paper we explore these problems by using an expert system shell, CLIPS (C Language Integrated Production System), to create temporal relations using common knowledge‐based constructs. We also build an extension to CLIPS through a user‐defined function which generates the temporal relations from those facts. We use the extension to create and maintain temporal relations in a workflow application that monitors and controls an engineering design change review process. We also propose a solution to ensure truth maintenance among temporally related facts that links our temporal extension to the CLIPS facility for truth maintenance.

Knowledge-Based System for Forecasting Snow Avalanches of Chowkibal-Tangdhar Axis (J&K

In this paper an attempt has been made to use artificial intelligence in avalanche forecasting and to develop arule-based expert system for predicting direct action avalanches of Chowkibal-Tangdhar axis (JLK). Using C-language integrated production system (CLIPS), procedural knowledge is represented in the form of rules. Thecondition attributes of the rulebased system are 28 variables selected from 1 154 samples of snow-met and snowprofile data.The relative contribution of each variable on avalanche days and non-avalanche days and their influence on sitewise release of avalanche was studied to formulate 358 rules. These rules, which include 173decision rules, were finally implemented and validated for running the model. Sixty-three samples of snow-metdata and pit profile data attributing to avalanche days and 54 samples of non-avalanche days were run on themodel. The results show that the knowledge-based model can predict avalanche days with 76 per cent efficiency.The misclassified results accounted for 28.2 percent of 117 test samples.

EXPIDER: A channel routing expert for VLSI design

Channel routing plays an important role in VLSI (Very Large Scale Integration) design. Because of the ill-structured nature of the problem, algorithmic approaches fail to provide satisfactory solutions. Techniques using expert knowledge have been solicited and developed successfully. In this paper, we present an expert system, EXPIDER, for channel routing. Wires are placed on the tracks from the boundaries toward the middle of a channel on a track-by-track basis, with the help of weighted vertical constraint graphs. Heuristics are applied to escape from blocked states and to do directed routing without search. Tuning knowledge is provided to reduce the wire length and the number of vias. The routing area can be determined automatically without human intervention. The system was implemented in CLIPS (C Language Integrated Production System) and has been tested successfully on many cases. Some results, together with a comparison with other methods, are shown at the end of the paper.

ECDEX: A knowledge-based approach to conceptual design of engineering systems

This paper describes a knowledge-based approach to the domain independent conceptual design phase. We describe the development of “ECDEX”, the “Engineering Conceptual Design Expert” that generates concept variants (concept alternatives) during the conceptual design phase of engineering systems. ECDEX was developed using an expert system shell called CLIPS (C Language Integrated Production System) and a C-program interface to aid the quantitative phase of concept evaluation. ECDEX internally develops function structures that are dependent on the number of solution principles for each sub-function selected during the process. After the function structures are developed, ECDEX searches for solution principles for each of the sub-functions considering that every combination needs to begin with the system input and end with the system output. After all combinations are developed, ECDEX performs the concept evaluation using evaluation criteria defined by the designer providing a set of ‘concept variants’. ECDEX is applied to three test cases; an impulse loading test rig, a hydro-electric generation plant and a fishing reel. The results from these three test cases demonstrate that ECDEX generates satisfactory conceptual designs of engineering systems.

Object/rule integration in CLIPS

Abstract: This paper gives a brief overview of the C Language Integrated Production System (CLIPS) with a focus on the object‐oriented features. The advantages of an object data representation over the traditional working memory element (WME), i.e. facts, are discussed and the implementation of the Rete inference algorithm in CLIPS is presented in detail. A few methods for achieving pattern‐matching on objects with the current inference engine are given and, finally, the paper examines the modifications necessary to the Rete algorithm to allow direct object pattern‐matching.

Debugging expert systems using a dynamically created hypertext network

The labor intensive nature of expert system writing and debugging motivated this study. The hypothesis is that a hypertext based debugging tool is easier and faster than one traditional tool, the graphical execution trace. HESDE (Hypertext Expert System Debugging Environment) uses Hypertext nodes and links to represent the objects and their relationships created during the execution of a rule based expert system. HESDE operates transparently on top of the CLIPS (C Language Integrated Production System) rule based system environment and is used during the knowledge base debugging process. During the execution process HESDE builds an execution trace. Use of facts, rules, and their values are automatically stored in a Hypertext network for each execution cycle. After the execution process, the knowledge engineer may access the Hypertext network and browse the network created. The network may be viewed in terms of rules, facts, and values. An experiment was conducted to compare HESDE with a graphical debugging environment. Subjects were given representative tasks. For speed and accuracy, in eight of the eleven tasks given to subjects, HESDE was significantly better.

Fully automated reconstruction of three-dimensional vascular tree structures from two orthogonal views using computational algorithms and productionrules

A system for reconstructing 3-D vascular structures from two orthogonally projected images is presented. The formidable problem of matching segments between two views is solved using knowledge of the epipolar constraint and the similarity of segment geometry and connectivity. The knowledge is represented in a rule-based system, which also controls the operation of several computational algorithms for tracking segments in each image, representing 2-D segments with directed graphs, and reconstructing 3-D segments from matching 2-D segment pairs. Uncertain reasoning governs the interaction between segmentation and matching; it also provides a framework for resolving the matching ambiguities in an iterative way. The system was implemented in the C Ianguage and the C Language Integrated Production System (CLIPS) expert system shell. Using video images of a tree model, the standard deviation of reconstructed centerlines was estimated to be 0.8 mm (1.7 mm) when the view direction was parallel (perpendicular) to the epipolar plane. Feasibility of clinical use was shown using x-ray angiograms of a human chest phantom. The correspondence of vessel segments between two views was accurate. Computational time for the entire reconstruction process was under 30 s on a workstation. A fully automated system for two-view reconstruction that does not require the a priori knowledge of vascular anatomy is demonstrated.

A comparative evaluation of expert system tools

The results of an assessment of the functionality and performance of tools with different architectural approaches are presented. The goal was to gain insight into the advantages and disadvantages of the various architectures. The tools evaluated in the study were (1) the Automated Reasoning Tool for Information Management (ARTIM), (2) the C Language Integrated Production System (CLIPS), (3) the Knowledge Engineering System (KES), (4) Level 5, and (5) VAX OPS5. Emphasis was on tools implemented in languages other than Lisp. KES and Level 5 were found to be inadequate for an application that required constructive problem solving, but both tools could be expected to provide improved performance for problems based on a heuristic classification approach due to their backward-chaining orientation. On the other hand, ART-IM, CLIPS, and VAX OPS5 might prove less effective for heuristic classification problem solving than for synthesis because they lack support for backward chaining. The results reinforce the fact that all expert system tools have strengths and weaknesses and that no single tool is dominant for a wide spectrum of applications or over a wide range of functionality.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>