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Describes a number of rule interpreters, each of which is represented as a set of production rules which can be executed as an ordinary ruleset in the ELEKTRA system. The interpreters override the default behaviour of the ELEKTRA interpreter: the default behaviour is forward‐chaining; shows how to implement backward‐chaining and content‐directed control. Some of the interpreters described are capable of interpreting themselves. Shows that ELEKTRA is powerful enough to support a wide variety of different interpreters without requiring any changes to its code. None of the interpreters requires additional system code in order to work: ELEKTRA provides all of the facilities used by the rulesets as part of its standard library. Provides additional evidence of the enormous power of reflective systems and of ELEKTRA in particular.

Reactive rules are used for programming rule-based Web agents, which have the ability to detect events and respond to them automatically and can have complex structure and unpredictable behavior. The aim of this paper is to provide an appropriate formal framework for analyzing such rules.

To achieve this goal, the authors give two alternative semantics for the basic reactive rules’ families which allow us to specify reactive rule-based agents and verify their intended behavior. The first approach expresses the functionality of production and event condition action rules in terms of equations, whereas the second methodology is based in the formalism of rewriting logic. Both semantics can be expressed within the framework of CafeOBJ algebraic specification language, which then offers the verification support and have their advantages and downsides.

The authors report on experiences gained by applying those methodologies in a reactive rule-based system and compare the two methodologies.

Finally, the authors demonstrate a tool that translates a set of reactive rules into CafeOBJ rewrite rules, thus making the verification of reactive rules possible for inexperienced users.

Today the garment industry in México is vulnerable to complex problems. This type of industry is subject to influences which over time, modify the perceptions of those involved in the design process due to the fact that they face problems that have both objective and subjective characteristics. In this study the authors used interviews, direct observation methodology, and theoretical argumentation to obtain the experts knowledge as they describe the problems that arise in the process of garment design for Mexican markets. The purpose of this paper is to generate a methodology so that the expert in this field will become highly specialized, resulting in heightened abilities and reinforcing them with the methodology of soft systems and the design management model (DMM). The results suggest that they are applicable to any area of design.

The success or failure of expert system (ES) depends directly on the acquisition of knowledge (Méndez-Gurrola, 2007, 2012), to accomplish that, three large groups of techniques are used to extract that knowledge: manual, semiautomatic, and automatic. Within the group of manual techniques, interviews, protocol analysis, questionnaires, direct on site observation, and the extraction of closed curves are emphasized. This technique is chosen for its ability to extract the particular type of knowledge being sought. The knowledge of the dominion expert in the design process applied to the clothing industry in México is based on processes and at the same time is episodic, meaning that part of the procedure is automated and each step of the process triggers the next. This knowledge is also based on experience which is of an autobiographical nature.

First, when one simulates human behavior, the hardest thing is to choose a knowledge representation that conforms as closely as possible to its emulation. According to, choosing a given type of knowledge representation is an art that is discovered little by little. And this is true as one designs and assembles a representation, when one realizes how far the authors are from imitating the design of the human brain processes, and discovers or invents methodologies to achieve it, combining the results of investigations into knowledge representation, cognitive psychology, and cognitive engineering. Second, solving any complex situation in the design process function of the clothing industry is no easy task. It requires plenty of experience in the manufacturing process. One needs the ability to identify the signals emitted by complex situations, and being able to stop them in time before they create irreversible damage. By merging the soft system methodology (SSM) and DMM with the experts’ abilities and knowledge (the result being the EXITUS model (EM)), makes knowledge modeling possible. A problem cannot be solved if it is unknown, if the problem persists and grows it becomes more complex. By describing a problem, based on: its origin, its relationship, and its effects, it also confers the ability to solve it. Thereby, an SBC with the characteristics presented in this paper, not only improves the design process function as a whole, it also contributes to achieving corporate success. Finally, it influences directly on: a quality product; market positioning; and good economic results. First, the SBC-EXITUS was tested and endorsed by expert management designers. When a designer identifies a complex problem using the SBC-EXITUS system, he is capable of verifying its existence with facts and real life situations. This enables quicker decision making decisions, which saves time and money, due to the fact that a non-desirable state of affairs may be contemplated in advance. Fourth, in this project an SBC named SBC-EXITUS has been implemented using the SSM and the DMM, with the purpose of detecting possible problems in the design process of the clothing industry. Its implementation is developed by the use of production rules. Fifth, utilizing the methodology and the production rules like knowledge representation technique, make possible to acquire dominion knowledge in complex problems as in the study case clothing industry in México. This approach is also applicable to other areas of design.

Utilizing the methodology and the production rules like knowledge representation technique, make possible to acquire dominion knowledge in complex problems as in the study case clothing industry in México. This approach is also applicable to the garment industry in the international context, as well as to other areas of design such as architecture, furniture, and others. The EM is a generic methodology. In this research and case, it has been applied in a design process within the garment industry, specifically in a case in Mexico. Yet this does not limit its use in a different context and problem situation. Having in mind the difference between countries in aspects such as sponsoring, technology, worker skills, marketing, etc. the tools for diagnostics in the Appendix 2, may be adapted to each context with enriched questions directed to specific aspects involved. Every new application allows facts and production rules that make the use of ES more efficient.

The ES-EXITUS was tested and endorsed by expert management designers. When a designer identifies a complex problem using the ES-EXITUS, he is capable of verifying its existence with facts and real life situations. This enables quicker decision making decisions, which saves time and money, due to the fact that a non-desirable state of affairs may be contemplated in advance.

Solving any complex situation in the design process function of the clothing industry is no easy task. It requires plenty of experience in the manufacturing process. One needs the ability to identify the signals emitted by complex situations, and being able to stop them in time before they create irreversible damage. By merging the SSM and DMM with the experts’ abilities and knowledge (the result being the EM), makes knowledge modeling possible. A problem cannot be solved if it is unknown, if the problem persists and grows it becomes more complex. By describing a problem, based on: its origin, its relationship and its effects, it also confers the ability to solve it. Thereby, an SBC with the characteristics presented in this paper, not only improves the design process function as a whole, it also contributes to achieving corporate success. Finally, it influences directly on a quality product, market positioning, and good economic results.

The purpose of this paper was to build the MACTAK methodology, which aims to transform collective tacit knowledge into the explicit one using knowledge elicitation techniques, associated to quality tools structured by systemography, represent it in a symbolic language and production rules and model it in two expert systems which can assist the investigation of defect causes during the metal packaging production process.

The method applied in the research was classified as exploratory, because a preliminary study was conducted, to better suit the mapping methodology for eliciting collective tacit knowledge, to the reality which was intended to be known. Through studies and the application of the systemography technique, a methodology for the elicitation of collective tacit knowledge has been developed. It suggests a systematic sequence of activities, to map and transform collective tacit knowledge into the explicit one, on the production process which was studied.

The types of tacit knowledge were mapped and became explicit through the application of the methodology proposed. A knowledge management system was created, as such knowledge was validated by other mechanics during their training on the shop-floor, which resulted in a structure of unique and shared knowledge. They became explicit for being stored in a knowledge base and presented to its users through the expert system. It is concluded that the methodology of acquiring collective tacit knowledge helped on the reduction of rework index by standardizing the way used to investigate the cause of the defect, in the studied company.

The MACTAK methodology was developed for exclusive use in industrial processes where the following elements are presented: process, method, environment, raw materials, labor work, measurement and machine. In this method, the detection of the problem occurs from statistical data.

The methodology began in August 2010, and in October 2011, obtained as a result a reduction in rework cost equivalent to US$17,780.95.

The methodology is unique, as it refers to the systematic use of knowledge acquisition techniques and tools of quality, and the methodology has a characteristic of direct application in manufacturing processes. The beneficiaries, in this case, are mechanicals of production and quality inspectors that work at the operation level in the company. For the organizational and tactical level, the beneficiaries are engineers of production.

Expert systems are being effectively applied to a variety of engineering problems. A growing number of languages and development tools are available for their building. Expert systems building tools (shells) are not so flexible as the high‐level languages, but they are easier to use. The problem is that there are too many development tools on the market today, no standards for their evaluation are available, so it is quite difficult to choose the ‘best’ tool for the developer's/user's needs. This paper is an attempt to review the situation on the confused market. Eighty‐six development tools are described in a table form for easy comparisons. Tools implemented on the AI machines only are not included in this survey.

Describes an innovative pictorial representation of business rules which clarifies the interrelationships of data entities and semantic constraints to enhance the communication among different levels of management. This pictorial representation scheme can be implemented using a set of microcomputer‐based CASE tools, and the result provides a comprehensive view of all aspects of management activity.

During recent decades, the emergence of artificial intelligence (AI) from computer science, psychology and linguistics has created a great impact on the design and implementation of process planning systems. This article provides a review of the state‐of‐the‐art AI‐based automated process planning systems. A generalised framework for expert process planning systems is proposed and prospective research issues are discussed.

Planning and scheduling are forms of decision making, which play a crucial role in manufacturing as well as in service industries. In the current competitive environment, effective sequencing and scheduling has become a necessity for survival in the marketplace. A great challenge for today’s companies is not only how to adapt to this changing, competitive business environment but also how to draw a competitive advantage from the way in which they choose to do so. Intelligent solutions, based on expert systems, to solve problems in the field of production planning and scheduling are becoming more and more widespread nowadays. Proposes an expert system, which uses the prevailing conditions in the industrial environment in order to select and “fire” dynamically the most appropriate scheduling algorithm from a library of many candidate algorithms.

Erythemato-squamous disease (ESD) is one of the complex diseases related to the dermatology field. Due to common morphological features, the diagnosis of ESDs become stringent and leads to inconsistency. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for ESD is essential to ensure credibility, consistency, to resolve lack of time, labor and competence and to diminish human error.

This paper presents the design of an automatic ontology framework through data mining techniques and subsequently depicts the diagnosis of ESD using the available knowledge- and rule-based system.

The rule language (Semantic Web Rule Language) and rule engine (Jess and Drools) have been integrated to explore the severity of the ESD and foresee the most appropriate class to be suggested.

In this paper, the authors identify the efficiency of the rule engine and investigate the performance of the computational techniques in predicting ESD using three different measures.

Primarily, the approach assesses transfer time for total number of axioms exported to rule engine (Jess and Drools) while the other approach measures the number of inferred axioms (process time) using the rule engine while the third measure calculates the time to translate the inferred axioms to OWL knowledge (execution time).

A firm must plan its manufacturing activities at a variety of levels and operate these as a system. Aggregate planning is medium‐range capacity planning which typically covers a time horizon of anywhere from three to 18 months. The goal of aggregate planning is to achieve a production plan which will effectively utilize the organization′s resources to satisfy expected demand. Planners must make decisions on output rates, employment levels and changes, inventory levels and changes, back orders, and subcontracting. Aggregate planning determines not only the output levels planned but also the appropriate resource input mix to be used.