XESS: The XML expert system shell

The XML Expert System Shell (XESS) was designed to alleviate some of the difficulties associated with translating a knowledge base from one expert system to another. The major goal of XESS is to allow programmers to model an expert system, complete with traditional facts and rules, in an XML-based language that leverages the universally understood terms used when teaching artificial intelligence to students. XML, the extensible markup language, is a text-based standard for information interchange between disparate systems1; it was originally designed to represent data in an easily parsable, human readable format2. While some extensions of the XML specification, particularly the Simple Object Access Protocol (SOAP), have long since abandoned human readability, the core XML specification is still used frequently to produce documents that can easily be exchanged between computational platforms and created or understood by human beings. The XESS-XML language inherits all of the usability of XML; it can be edited by hand in any text editor, is human readable, and can be parsed using XML parsers commonly available in any modern programming language. The XML Schema specification provides a mechanism for explicitly defining the content of an XML document so that a document can be validated3,4,5. XML schemas specify the make-up of an XML document in exacting detail6, using a pseudo-object-oriented syntax to specify exactly which entities are allowed in the document, the attributes of those entities, where they are allowed in the document, and how often they may occur. The XESS-XML language is defined as a fully extensible XML Schema, which can be used to validate any knowledge base written in the language. The Schema provides entities for common facts (e.g. predictes, structs) and a robust syntax for expressing rules in an if-then-else format, as well as the actions that should be taken in the event that a rule is fired. Additionally, because XML schemas are fully extensible, the XESS schema may be extended to add additional functionality such as support for fuzzy logic, new clause types, or new actions to be taken when rules are fired. In addition to the XML language, XESS also includes an object oriented interpreter specification that defines a robust set of language independent APIs for interacting with the expert system. This interpreter specification is meant to set expectations, both for XESS developers and users, as to the features provided by the XESS API regardless of the language in which the interpreter has been implemented. As part of the specification, the XESS API also provides object oriented definitions for XESS plug-ins; a plug-in is capable of translating from an XESS document to the native language of a specific expert system shell in a generic way (i.e. not specific to any one rule set) and back again. This allows users to express custom expert system shells in the XESS-XML language, parse them using an XESS interpreter written in any language, and translate them to a specific expert system shell through the use of an XESS plug-in without needing to learn the specific expert system shell language or rewriting the knowledge base once for each shell tested

Expert systems and finite element structural analysis - a review

Finite element analysis of many engineering systems is practised more as an art than as a science . It involves high level expertise (analytical as well as heuristic) regarding problem modelling (e .g. problem specification,13; choosing the appropriate type of elements etc .), optical mesh design for achieving the specified accuracy (e .g . initial mesh selection, adaptive mesh refinement), selection of the appropriate type of analysis and solution13; routines and, finally, diagnosis of the finite element solutions . Very often such expertise is highly dispersed and is not available at a single place with a single expert. The design of an expert system, such that the necessary expertise is available to a novice to perform the same job even in the absence of trained experts, becomes an attractive proposition. 13; In this paper, the areas of finite element structural analysis which require experience and decision-making capabilities are explored . A simple expert system, with a feasible knowledge base for problem modelling, optimal mesh design, type of analysis and solution routines, and diagnosis, is outlined. Several efforts in these directions, reported in the open literature, are also reviewed in this paper

Aeration System Design for Flat Grain Storages with an Expert System

An expert system, Aeration System Design (ASD), was developed for the design of aeration systems for farm-sized flat grain storages. ASD requests information about the storage problem from a user and generates a custom design drawing, component specification list, and management recommendations. The knowledge base was derived from publications and experts. ASD represents the first attempt to consolidate aeration system design guidelines and procedures for flat grain storage into an expert system. ASD uses illustrations to communicate concepts and terminology more clearly with users. A feature of ASD allows an expert to change the design guidelines and factors. For example, alternative methods of determining the layout and length of ducts can be selected. ASD offers the capability of rapidly designing an aeration system and changing design guidelines to study the effects upon the design

An Enhanced Spatial Reasoning Ontology for Maritime Anomaly Detection

International audienceAlthough originally conceived as a conceptual object for modelling knowledge, current ontologies do not make it possible to manipulate spatial knowledge. However, spatial knowledge is an essential component of any modelling specification. This problem provided the motivation for the creation of an expert system driven by an ontology. The system enables experts in the maritime domain to characterise abnormal ship behaviour based on formal semantic properties. Users are able to specify and execute spatial rules that are directly integrated into the ontology and a map interface linked to the ontology displays the results of the inferences obtained

Control system design using artificial intelligence

Includes bibliography.Successful multivariable control system design demands knowledge, skill and creativity of the designer. The goal of the research described in this dissertation was to investigate, implement, and evaluate methods by which artificial intelligence techniques, in a broad sense, may be used in a design system to assist the user. An intelligent, interactive, control system design tool has been developed to fulfil this aim. The design tool comprises two main components; an expert system on the upper level, and a powerful CACSD package on the lower level. The expert system has been constructed to assist and guide the designer in using the facilities provided by the underlying CACSD package. Unlike other expert systems, the user is also aided in formulating and refining a comprehensive and achievable design specification, and in dealing with conflicts which may arise within this specification. The assistance is aimed at both novice and experienced designers. The CACSD package includes a synthesis program which attempts to find a controller that satisfies the design specification. The synthesis program is based upon a recent factorization theory approach, where the linear multivariable control system design problem is translated into, and techniques efficiency solved as, a quadratic programming problem, which significantly improve the time and space of this method have been developed, making it practical to solve substantial multivariable design problems using only a microcomputer. The design system has been used by students at the University of Cape Town. Designs produced using the expert system tool are compared against those produced using classical design methods

A Very High Level Logic Synthesis

The evolution of Computer Aided Design (CAD) calls for the incorporation of design specifications into a microelectronics system development cycle. This expansion requires the establishment of a new generation of CAD procedures, defined as Very High Level Logic Synthesis (VHLLS). The fundamental characteristics of open-ended VHLLS are: (1) front-end graphical interface; (2) time encapsulation; and (3) automatic translation into a behavioral description. Consequently, the VHLLS paradigm represents an advanced category of CAD-based microelectronics system design, built on a deep usage of expert systems and intelligent methods. Artificial Intelligence (AI) formalisms such as Knowledge Representation System (KRS) are necessary to model properties related to the very high level of specification such as: dealing with ambiguities and inconsistencies, reasoning, computing high-level specification, etc. A prototype VHLLS design suite, called Specification Procedure for Electronic Circuits in Automation Language (SPECIAL), is defined, compared with today\u27s commercial tools and verified using numerous design examples. As a result, a new family of formal and accelerated development methodologies has become feasible with a better understanding of formalized knowledge driving these design processes

Experiments in representing design knowledge for arid lands design

Thesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture, 1987.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH.Includes bibliographical references (leaves 54-55).This thesis proposes, through a multi-layered exploration, the development of a system of computer tools for architects. The research consists of a series of "design sessions" in the context of a desert design problem. The goal is to create a knowledge-based system using a commercially available expert shell, which provides the designer with an automated interface to visual references. Data can be seen as a collection of things, while knowledge can be similarly seen as a collection of relationships between things. An expert shell is literally a program that is "empty" of knowledge, and into which a designer puts know ledge: a knowledge-base is the result. The shell itself acts as a means of manipulating that knowledge-base by an inference process that is activated by rules, or hypotheses and tests. The experimental framework of the thesis is devised to evaluate both type of inference processes in relation to their capabilities for representing design knowledge. The design problem serves to outline a methodology for understanding the process of design, but it also is the means by which a design grammar and syntax appropriate to the automated system are formally described. The intent is not to compile a vast domain of knowledge on all issues of arid lands design, but to focus on a specific architectural response to the climate: the relationship between the primary structural system and the secondary closure system. The design of a window system is the vehicle for documenting observations of the way visual references are used. From this process a descriptive system and body of "expert" rules are developed to define the function of the automated environment. The larger goal is to then relate the syntactical environment to a general image referencing system so that the expert system can act as a personal design consultant. The image referencing system is a distinct and important component of the automated environment, and as such a detailed specification of its nature and operation is intended to show the interdependence of the knowledge-base and a visual database.by Andrew M. Bennett.M.Arch

Multisensor Data Fusion Implementation for a Sensor Based Fertilizer Application System

"Mapping systems" (“mapping approach”), real-time sensor-actuator systems ("sensor approach") or the combination of both (“Real-time approach with map overlay”) determine the process control in mobile application systems for spatially variable fertilization. Within the integrated research project “Information Systems Precision Farming Duernast” (IKB Duernast) the implementation of the “Real-time approach with map overlay” was done for intensive nitrogen fertilization. The bottom line of this sophisticated approach is a comprehensive situation assessment, a typical multisensor data fusion task. Based on a functional and procedural modelling of the multisensor data fusion and decision making process, it could be pointed out that an expert system is an adequate fusion paradigm and algorithm. Therefore, a software simulation with an expert system as core element was implemented to fuse on-line sensor technology measurements (REIP), maps (yield, EM38, environmental constraints, draft force) and user inputs in order to derive an application set point in real-time. The development of an expert system can be viewed as a structured transformation in five levels from the “specification level”, the “task level”, the “problem solving level” and the “knowledge base level” to the “tool level”. In the “tool level” the hybrid expert system shell JESS (Java Expert System Shell) was selected for implementation due to the results of preceding levels. Knowledge acquisition was done within another IKB-subproject by the means of data mining. Typical and maximal times of 10 ms and 60 ms for one fusion cycle were measured running this application on a 32-bit processor hardware (Intel Pentium III Mobile, 1 GHz)

ConEditor+: Capture and Maintenance of Constraints in Engineering Design

The Designers' Workbench is a system, developed to support designers in large organizations, such as Rolls-Royce, by making sure that the design is consistent with the specification for the particular design as well as with the company’s design rule book(s). Currently, to capture the constraint information, a domain expert (design engineer) has to work with a knowledge engineer to identify the constraints, and it is then the task of the knowledge engineer to encode these into the Workbench's knowledge base (KB). This is an error prone and time consuming task. It is highly desirable to relieve the knowledge engineer of this task, and so we have developed a tool, ConEditor+ that enables domain experts themselves to capture and maintain these constraints. The tool allows the user to combine selected entities from the domain ontology with keywords and operators of a constraint language to form a constraint expression. Further, we hypothesize that to apply constraints appropriately, it is necessary to understand the context in which each constraint is applicable. We refer to this as "application conditions". We show that an explicit representation of application conditions, in a machine interpretable format, along with the constraints and the domain ontology can be used to support the verification and maintenance of constraints