Expertsystems are widely employed to solve the complex and critical problems in many domains like medicine, manufacturing and agriculture being one of them. Expert system can be defined as a tool for information generation from knowledge . They depend on inference and specific expertise of a human expert. They are widely used in situations where human experts are not readily available. In such situations they help to take timely and precise decisions. Expertsystems are computer programs that are capable of acting in accordance to a human reasoning process, giving similar advices and making similar decisions parallel to what a human expertise might achieve . Expertsystems can become beneficial assistants to human decision makers, capable in the gathering of vast amount of information and experience from multiple human experts of numerous disciplines and providing valuable recommendations to users. For developing an expert system it is important to extract knowledge from the domain expert, this is achieved by “Knowledge Engineer” . This knowledge is then converted into a computer program. Knowledge and experience helps in generating information . When a less experienced person in a domain seeks advice from an expert of the domain, the expert uses his/her knowledge and experience to generate the piece of information. This could be the most precious information as it is generated out of years of work and experience and interaction with other experts and practitioners of the domain. This piece of information may not be included in any form like text, audio, video or other. Incase it is included, it may not be linked with its scientific origin, especially if is a combination of different domains and methods. This information could be the result for solving a problem hence it becomes all the more precious . Hence it is important to record this information for the benefit of the community. In the beginning the focus was only on textual information. Images were very difficult to be included in an information system. Recently with the advancement in technology, it has been made possible to include images as well in the systems . Advancement in data processing has led to generating efficient and useful information from data. Expertsystemstechnology can play a very important role in generating information from knowledge .
The paper describes some results from an environmental Telematics project (ECOSIM) and two Esprit projects (HITERM, SIMTRAP), as well as a EUREKA EUROENVIRON project (AIDAIR), and applications in cities such as Vienna, Berlin, Geneva, Basel, Milano, Athens, Gdansk, and Izmir. Strategies for the integration of monitoring, GIS, and modeling are presented, that use a common client-server architecture, an object oriented design, embedded expertsystemstechnology, and a multi-media user interface to support easy access, and easy use of complex analytical tools for urban environmental management.
This Chapter presents the evolution of the expertsystems paradigm for fault diagnosis in technical systems and processes. Fault diagnosis is becoming one of the largest domains where expertsystems are find application from their early stages. The process of diagnosing faulty conditions varies widely across to different approaches to systems diagnosis. The application of decision-making knowledge based methods to fault detection allows an in-depth diagnosis by simulating the human reasoning activity. Most of the past applications have been rule based while the automation of the diagnostic process including real-time data and/or modelling techniques added a new dimension to diagnostic task by detecting and predicting faults on line. Combination of expertsystemstechnology with other artificial intelligent methods or with specific classical numerical methods adds more effectiveness to the diagnostic task. These knowledge based diagnostic techniques are presented in this Chapter, technical details of their implementation are provided, the advantages and drawbacks of every technique are outlined, examples from recent research work of expert diagnostic practice in industry are presented and current research trends are underlined to help the reader to delve into the matter.
The development environment includes the activities and support that are necessary to acquire and represent the knowledge as well as to make inferences and provide explanations. The major players in this environment are the knowledge engineer and the domain expert who act as builders. Once the system is completed it is used for consultation by the nonexpert user via the consultation environment.
As shown in Figure 2, a structure of an expert system includes 8 parts: knowledge base, inference machine, knowledge acquisition, explanatory mechanism, blackboard system, man-machine interface for experts and Interface for users. Through knowledge acquisition interface, users can establish knowledge base and get fact information into the blackboard system, then inference machine extracts factual information from blackboard system and matches rules in knowledge base repeatedly, in this process, intermediate conclusions will be put to blackboard system, until the system gets final conclusion output according to user questions. Explanatory mechanism explains the final conclusions and gives the solving process.
Shape rolling is widely employed in the production of long workpieces with appropriate cross-section profiles for other industrial applications. In the development of shape rolling systems, roll pass design (RPD) plays an essential role on the quality control of products, service life of rolls, productivity of rolling systems, as well as energy consumption of rolling operations. This study attempts to establish a generic strategy based on hybrid modeling and an improved genetic algorithm, to support the optimizations of RPD and shape rolling operations at a systematic perspective. Objectives include improving the quality and efficiency of RPD, reducing energy consumption of shape rolling, as well as releasing the demands on costly trails and expert knowledge in RPD. Hybrid modeling based on cross-disciplinary knowledge is developed to overcome the limitations of isolated single-disciplinary models. And conventional genetic algorithm is improved for the implementation of optimal design. Targeting to integrate empirical data and published reliable solutions into optimizations, a parameters estimation method is proposed to transfer the initially misaligned models into a uniform pattern. A tool based on the Matlab platform is developed to demonstrate the optimal design operations, with case studies involved to validate the proposed methodology.
APM WinMachine software is flexible, reliable means of design and analysis; runs on most popu- lar operating systems of computers – from PC to workstations and supercomputers. Despite the fact that APM WinMachine program has a variety of sophisticated options, its organizational structure and user «friendly» graphical interface makes learning and application of the program very con- venient. At the same time the program is com- pleted with documentation that enables to deal with the order of implementation of complex works online. The system «menu» includes «intui- tive» features, helping the user to control the pro- gram effectually. Output data can be entered using the manipulator «mouse», keyboard or by combin-
 K.-ching Ying, S.-wei Lin, Z.-jung Lee, and Y.-tim Lin, “ExpertSystems with Applications An ensemble approach applied to classify spam e-mails,” ExpertSystems With Applications, vol. 37, no. 3, pp. 2197-2201, 2010.  C. Lopes, P. Cortez, P. Sousa, M. Rocha, and M. Rio, “ExpertSystems with
(2) VEGES is a multilingual expert system for the diagnosis of pests, disease and nutritional disorder of six green house vegetable viz., pepper, lettuce, cucumber, bean, tomato, and aborigine. It provides the user with a diagnosis on the basis of a brief description of the external appearance of the affected plant. It then suggests method to remedy the problem (e.g., fertilizer, adjustment, fungicides or pesticide applications). The system is accompanied by a new language translation module which allows a non specialist user (e.g. extension officer) to translate the knowledge base to the native language or dialect of the local farmers.
These are computer systems or programs that use artificial intelligence techniques to solve problems that ordinarily require a knowledgeable human. The method used to construct such systems, knowledge engineering, extracts a set of rules and data from an expert or several experts through extensive questioning. This material is then organized in a format suitable for representation in a computer and a set of tools for inquiry, manipulation, and response are applied. While such systems do not often replace the human experts, they can serve as useful adjuncts or assistants; for example, an aid to geologists in interpreting mineral data. Expertsystems imitate human experts in many different fields of expertise. Such systems contain rules (such as decision tables) that help common individual answer expert questions. This is a classical example of how deskilling can affect people: imagine you are an expert rock identifier and people from around the world treat you like a living national treasure because of your
principles of legal expert system design are independent of Neota Logic’s software or any other platform. Students who have a software background can learn these principles to code the law directly (as some of our students have done.) And in the future, a new platform may come along that offers more capabilities and greater ease of use. For the present, though, Neota Logic’s software is a good choice for this class.
Knowledge factor is absolutely important over other factors, i.e., Skill, Experience, Attitude factors. Assistant Systems Engineer level employees are expected to possess the high levels of expert knowledge in dot net, expert knowledge in Java, having quick learning skills, experience of working in teams of software development and testing, and attitude of ambitious to the career development in the software design and development. As per the personal conversations with the evaluator, they often find people with low levels of quick learning skills. This is true as per the evaluations. This is the reason that human capital in skills factors at this level is just “GOOD”. Learning management teams should focus on these aspects to develop these learning skills. Overall Human Capital in grade-C1Y employees is found to be “Very Good” with membership value of 0.43. Human capital filled in grade- C1Y is about 69% and there is still a gap of 31%.
Through its brands, BEI Kimco, BEI Sensors, BEI PSSC, Crouzet, Crydom, Kavlico, Newall and Systron Donner Inertial, CST offers customizable, reliable and efficient components for mission- critical systems in Aerospace & Defense, Transportation, Energy & Infrastructures, Commercial & Industrial OEMs, Medical, Food and Beverage and Building Equip- ment markets.
Some of these mechanisms have become political realities during the course of ZEP ’ s existence. Political support for CCS in EU federal level policy has been on the rise since around 2005, paralleling increased political attention to the technology in several of its member states, and developed alongside a number of EU efforts to meet climate change mitigation commitments . In line with recommendations made in ZEP ’ s SRA, regulatory standards in the form of a CCS directive, the inclusion of CCS in the EU’s ETS carbon market, and the formalized goal of up to 12 large-scale CCS demonstration plants across the EU have all been formulated and pursued by the EC and the EU Council of Ministers in this period. Nilsson and colleagues have argued that there is a tendency for strategic planning units in public policy to ignore political and institutional pathways and barriers to desirable futures . ZEP actually appears to be highly aware of the importance of such pathways in their research recommenda- tions. But when it comes to public communication materials explaining the form and purpose of CCS, the institutional and socio-political implications of the technology—the broader economic and environmental governance contexts that would lend meaning to CCS—are rarely discussed. This contrast is striking in light of the EC’s stated ambitions regarding expert advice, to which we now turn.
Intelligent help systems capable of providing context sensitive help to software system users. These systems are able to infer the correct level of help needed to provide because they can a) make inferences about the level of skill of the user and b) utilize deep knowledge about the software application itself. Using these areas of knowledge it is possible to identify the types of mistakes that users of varying skill levels are likely to make. Novice users who have no conceptual insight into an application tend to make syntactic and semantic mistakes, naive users tend to make more semantic mistakes whereas expert users tend to make thematic mistakes - i.e. inferring incorrectly that one way of assembling commands to solve a particular problem can be generalized to solve another problem using a comparable sequence of commands.
to preserve appropriate set of knowledge to a given business activity in knowledge bases and to pass this knowledge in the moment of need to the appropriate employees in the form of multimedia teaching ap- plications including problem solving by the means of the expert consultation with the included expert system. Such solutions can be qualified as the expert eLearning. The expert eLearning then represents the functional link of two components: expert system and eLearning in one integral.
Uncertainty exists whenever expertsystems have to make non-categorical decisions. Un- certainty can be represented using probability theory, certainty factors, fuzzy logic and the Dempster-Shafer theory of evidence 12]. The literature mentions the representation of un- certainty in only a few of the systems. The Upjohn troubleshooting system developed by 58] was implemented in M.1 which is rule-based expert system software which deals with uncertainty by the use of MYCIN style certainty factors. The capability of M.1 in dealing with uncertainty was used to weigh evidence and thereby establish priority for ecient trou- bleshooting. PROTEIN used certainty factors as well. PIA handled uncertainty \...in an ad-hoc fashion, basically by noting any causes for uncertainty in working memory, assigning a subjective weight, and accumulating the weights associated with each proposed identi- cation" 70]. The Pascal reimplementation of the ESCA REPS also incorporated weighting factors 84].