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→‎KBE and CAX: Rewrote the section. Kept one of the three links. Other two were very theoretical.
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==History ==
==History ==
KBE developed in the 1980's. It was part of the initial wave of investment in Artificial Intelligence for business that fueled expert systems. Like expert systems it relied on what at the time were leading edge advances in corporate [[information technology]] such as [[PC Computing|PC]]'s, [[Workstation (computer hardware)|workstations]], and [[Client-server programming|client-server architectures]]. These same technologies were also facilitating the growth of [[CAx]] and [[CAD]] software. CAD tended to drive leading edge technologies and even push them past their current limits.<ref>{{cite web|last1=Switlik|first1=John|title=Knowledge Based Engineering (KBE): Update|url=http://web.archive.org/web/20120324223121/http://legacy.coe.org/newsnet/Oct05/index.cfm|website=coe.org|publisher=COE|accessdate=6 July 2014|archiveurl=http://web.archive.org/web/20120324223121/http://legacy.coe.org/newsnet/Oct05/index.cfm|archivedate=24 March 2012|date=October/November 2005}}</ref> The best example of this was [[object-oriented programming]] and [[Object-oriented database|database]] technology which was adapted by CAD when most corporate information technology shops were dominated by [[relational databases]] and [[procedural programming]].<ref>{{cite journal|last1=Spooner|first1=David|title=Towards an Object-Oriented Data Model for a Mechanical CAD Database System|journal=On Object-Oriented Database Systems Topics in Information Systems|date=1991|pages=189-205|url=http://link.springer.com/chapter/10.1007/978-3-642-84374-7_13?no-access=true|accessdate=6 July 2014}}</ref>


As with expert systems, KBE suffered a downturn during the [[AI Winter]].<ref>{{cite web|title=AI Winter|url=http://www.ainewsletter.com/newsletters/aix_0501.htm#w|website=http://www.ainewsletter.com|publisher=ainewsletter|accessdate=6 July 2014|quote=the AI Winter of the late 80s. The phrase was coined by analogy with "nuclear winter" - the theory that mass use of nuclear weapons would blot out the sun with smoke and dust, causing plunging global temperatures, a frozen Earth, and the extinction of humanity. The AI Winter merely caused the extinction of AI companies, partly because of the hype over expert systems and the disillusionment caused when business discovered their limitations.}}</ref> Also, as with expert systems and artificial intelligence technology in general there was renewed interest with the Internet. In the case of KBE the interest was perhaps strongest in the [[business to business]] type of [[electronic commerce]] and technologies that facilitate the definition of industry standard vocabularies and [[Ontologies (computer science)|ontologies]] for [[Product life cycle|manufactured products]]. The semantic net is the vision of Tim Berners Lee for the next generation of the Internet. This will be a knowledge-based Internet built on ontologies and frame technologies that were also enabling technologies for KBE. Important technologies for the Semantic Net are [[XML]], [[Resource Description Framework|RDF]], and [[Web Ontology Language|OWL]].<ref>{{cite journal|last=Berners-Lee|first=Tim|first2=James|last2=Hendler|first3=Ora|last3=Lassila|title=The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities|journal=Scientific American|date=May 17, 2001|url=http://www.cs.umd.edu/~golbeck/LBSC690/SemanticWeb.html}}</ref> The semantic net has excellent potential for KBE and KBE ontologies and projects are a strong area for current research.<ref>{{cite journal|last1=Zhang|first1=W.Y.|last2=Yun|first2=J.W.|title=Exploring Semantic Web technologies for ontology-based modeling in collaborative engineering design|journal=The International Journal of Advanced Manufacturing Technology|date=April 2008|volume=36|issue=9-10|pages=833-843|url=http://link.springer.com/article/10.1007/s00170-006-0896-5|accessdate=6 July 2014}}</ref>
KBE essentially was a complementary development to [[CAx]] <ref>COE Newsnet 10/05 [http://legacy.coe.org/newsnet/Oct05/index.cfm#1 Knowledge Based Engineering (KBE: Update)]</ref> and can be dated from the 1980s (See Also, [[ICAD (software)|ICAD]]). [[CAx]] has been developing along with Information Technology after making large strides in the 1970s.

KBE technologies suffered a downslide during [[AI Winter]]. While KBE had sufficient success stories that sustained it long enough into the 1990s, very high [[user expectations|expectations]] and the inability to meet them with KBE resulted in it being considered obsolete in a way similar to [[LISP]] [[AI]] designs.

KBE, as implemented with [[ICAD (software)|ICAD]] can be thought of as an advanced form of computer applications (in some forms with an extreme [[end-user computing]] flavor) that support [[Product Lifecycle Management|PLM]] and [[CAx]].

Success of early KBE prototypes was remarkable; eventually this led to KBE being considered as the basis for generative design with many expectations for hands-off performance where there would be limited human involvement in the design process.


==KBE and product lifecycle management ==
==KBE and product lifecycle management ==

Revision as of 02:54, 6 July 2014

This article is about the use of knowledge-based technology in the CAD domain. For development of expert systems in all domains, see Knowledge engineering.

Knowledge-based engineering (KBE) is the application of knowledge-based systems technology to the domain of manufacturing design and production. The design process is inherently a knowledge intensive activity so a great deal of the emphasis for KBE is on the use of knowledge based technology to support computer-aided design (CAD) however knowledge based techniques (e.g. knowledge management) can be applied to the entire product lifecycle.

The CAD domain has always been an early adopter of software engineering techniques used in knowledge-based systems such as object-orientation and rules. Knowledge-based engineering integrates these technologies with CAD and other traditional engineering software tools.

Benefits of KBE include improved collaboration of the design team due to knowledge management, improved re-use of design artifacts, and automation of major parts of the product lifecycle.[1]

Overview

KBE can be defined as engineering on the basis of knowledge models. Such knowledge models are the result of knowledge modeling that uses knowledge representation techniques to create the computer interpretable models. The knowledge models can be imported in and/or stored in specific engineering applications that enable engineers to specify requirements or create designs on the basis of the knowledge in such models. There are various methods available for the development of knowledge models, most of them are system dependent. An example of a system-independent language for the development machine-readable ontology databases, including support for basic engineering knowledge, is called Gellish English. An example of a CAD-specific system that can store knowledge and use it for design is the CATIA program through its KnowledgeWare module. An example of a CAD-independent, language-based KBE system with full compiler and support for runtime application deployment is Genworks GDL from Genworks International.[2]

KBE can have a wide scope that covers the full range of activities related to Product Lifecycle Management and Multidisciplinary design optimization. KBE's scope would include design, analysis (computer-aided engineering – CAE), manufacturing, and support. In this inclusive role, KBE has to cover a large multi-disciplinary role related to many computer aided technologies (CAx).

KBE also has more general overtones. One of its roles is to bridge knowledge management and design automation.[3] Knowledge processing is a recent advance in computing. It has played a successful role in engineering and is now undergoing modifications (to be explained). An example of KBE’s role is generative mechanical design.[4] There are others. KBE can be thought of as an advanced form of computer applications (in some forms with an extreme end-user computing flavor) that support PLM and CAx.

There are similar techniques, such as electronic design automation. AAAI provides a long list of engineering applications.some of which are within the KBE umbrella. At some point, the concept of KBE might split into several sub-categories as MCAD and ECAD are just two of many possible types of design automation.

History

KBE developed in the 1980's. It was part of the initial wave of investment in Artificial Intelligence for business that fueled expert systems. Like expert systems it relied on what at the time were leading edge advances in corporate information technology such as PC's, workstations, and client-server architectures. These same technologies were also facilitating the growth of CAx and CAD software. CAD tended to drive leading edge technologies and even push them past their current limits.[5] The best example of this was object-oriented programming and database technology which was adapted by CAD when most corporate information technology shops were dominated by relational databases and procedural programming.[6]

As with expert systems, KBE suffered a downturn during the AI Winter.[7] Also, as with expert systems and artificial intelligence technology in general there was renewed interest with the Internet. In the case of KBE the interest was perhaps strongest in the business to business type of electronic commerce and technologies that facilitate the definition of industry standard vocabularies and ontologies for manufactured products. The semantic net is the vision of Tim Berners Lee for the next generation of the Internet. This will be a knowledge-based Internet built on ontologies and frame technologies that were also enabling technologies for KBE. Important technologies for the Semantic Net are XML, RDF, and OWL.[8] The semantic net has excellent potential for KBE and KBE ontologies and projects are a strong area for current research.[9]

KBE and product lifecycle management

Product Lifecycle Management (PLM) is the management of the manufacturing process of any industry that produces goods. It can span the full product lifecycle from idea generation to implementation, delivery, and disposal. KBE at this level will deal with product issues of a more generic nature than it will with CAx. A natural area of emphasis is on the production process, however lifecycle management can cover many more issues such as business planning, marketing, etc. An advantage of using KBE is to get the automated reasoning and knowledge management services of a knowledge-based environment integrated with the many diverse but related needs of lifecycle management. KBE supports the decision processes involved with configuration, trades, control, management, and a number of other areas, such as optimization.

KBE and CAx

CAx refers to the domain of computer aided tools for analysis and design. CAx spans multiple domains. Examples are computer aided design of manufactured parts, software, the architecture of buildings, etc. Although each specific domain of CAx will have very different kinds of problems and artifacts they all share common issues as well such as having to manage collaboration of sophisticated knowledge workers, design and re-use of complex artifacts, etc.

Essentially KBE extends, builds on, and integrates with the CAx domain typically referred to as Computer Aided Design (CAD). In this sense KBE is analogous to Knowledge-Based Software Engineering which extended the domain of Computer Aided Software Engineering with knowledge based tools and technology. What KBSE was to software and CASE KBE is to manufactured products and CAD.

KBE and knowledge management

One of the most important knowledge based technologies for KBE is knowledge management. Knowledge management tools support a wide spectrum repository, i.e., a repository that can support all different types of work artifacts: informal drawings and notes, large database tables, multimedia and hypertext objects, etc. Knowledge management provides the various group support tools to help diverse stake holders collaborate on the design and implementation of products. It also provides tools to automate the design process (e.g. rules) and to facilitate re-use. [10]

KBE methodology

The development of KBE applications concerns the requirements to identify, capture, structure, formalize and finally implement knowledge. Many different so-called KBE platforms support only the implementation step which is not always the main bottleneck in the KBE development process. In order to limit the risk associated with the development and maintenance of KBE application there is a need to rely on an appropriate methodology for managing the knowledge and maintaining it up to date. As example of such KBE methodology the EU project MOKA "Methodology and tools Oriented to Knowledge based Applications" propose solutions which focus on the structuration and formalization steps as well as links to the implementation.[11]

An alternative to MOKA is to use general knowledge engineering methods that have been developed for expert systems across all industries [12] or to use general software development methodologies such as the Rational Unified Process or Agile methods.

Languages for KBE

Two critical issues for the languages and formalisms used for KBE are:

  • Knowledge-based vs. procedural programming
  • Standardization vs. proprietary

Knowledge-based vs. procedural programming

A fundamental trade-off identified with knowledge representation in artificial intelligence is between expressive power and computability. As Levesque demonstrated in his classic paper on the topic the more powerful a knowledge representation formalism one designs the closer the formalism will come to the expressive power of first order logic. As Levesque also demonstrated the closer a language is to First Order Logic the more probable that it will allow expressions that are undecidable or require exponential processing power to complete.[13] In the implementation of KBE systems this trade off is reflected in the choice to use powerful knowledge based environments or more conventional procedural and object-oriented programming environments.

Standardization vs. proprietary

There is a trade off between using standards such as STEM and vendor or business specific proprietary languages. Standardization facilitates knowledge sharing, integration, and re-use. Proprietary formats (such as CATIA) can provide competitive advantage and powerful features beyond current standardization.[14]

Genworks GDL, a commercial product whose core is based on the AGPL-licensed Gendl Project[15] addresses the issue of application longevity by providing a high-level declarative language kernel which is a superset of a standard dialect of the Lisp programming language (ANSI Common Lisp, or CL).

In 2006 the Object Management Group released a KBE services RFP document and requested feedback.[16] To date no OMG specification for KBE exists however there is an OMG standard for CAD services.[17]

KBE in Academia

Implementations

The following KBE development packages are commercially available:

For CAD

For General-purpose development of Web-deployed applications

For analysis, design and engineering processes

KBE futures, KBE theory

KBE, as a particular example of KBS, is a multi-disciplinary framework that has more than practical considerations. Not only will KBE require successful handling of issues of the computational (Ontology, Artificial Intelligence, Entscheidungsproblem, Interactive computation, Category Theory, ...) and logic (non-monotonic issues related to the qualification, frame, and ramification problems)), it will touch upon all sciences that deal with matter, its manipulations, and the related decisions. In a sense, Product Lifecycle Management allows us to have the world as a large laboratory for experimental co-evolution of our knowledge and artificial co-horts. As noted in ACM Communications, "Computers will grow to become scientists in their own right, with intuitions and computational variants of fascination and curiosity." [18] What better framework is there to explore the "increasingly complicated mappings between the human world and the computational"?

A continuing theme will be resolving the contextual definitions for KBE into a coherent discipline and keeping a handle on managing the necessary quantitative comparisons. One issue considers what limits there may be to the computational; this study requires a multi-disciplinary focus and an understanding of the quasi-empirical. Given the knowledge focus of KBE, another issue involves what limits there might be to a computational basis for knowledge and whether these are overcome with the more advanced types of human-machine interface.

It is important not to treat the KBE technology in isolation, but focus more on its role in the overall Product Development Process (PDP). During development, it is important to streamline the process from knowledge capture towards software implementation. To this end, close-coupling between Knowledge Management and KBE is desired. Transitions from data and information inside a Knowledge Base towards software code is of particular relevance. The best results can be achieved by using model-driven software development principles, which includes automatic code generation and round-tripping. The use of KBE during a PDP not only requires the ability to easily set-up (existing) KBE applications from a knowledge level, but also the ability to store back the results after execution of the tool. In order to use KBE on a strategical level as decision-making and planning support mechanism, it is important to relate results back to the system engineering domain (requirements, functions, options, embodiment). From deployment perspective, a better integration with other IT tools should be realized. Couplings between KBE applications, Knowledge Bases and Simulation Workflow Management software are of particular importance. The iProd project tries to take KBE to the next level by addressing these aspects.[19] The iProd framework uses KBE technology as a reasoning mechanism to infer new product knowledge and, as a means to automate virtual execution (CAE simulation) and as MDO-enabler. On an IT level, it prototypes KB-KBE couplings (code generation, round-tripping, results storage and automatic workflow generation) and SWFM-KBE integration (on the basis of the software-as-a-service paradigm).

See also

References

  1. ^ "Knowledge Based Engineering". http://www.technosoft.com. Technosoft. Retrieved 5 July 2014. {{cite web}}: External link in |website= (help)
  2. ^ "GDL Overview Tutorial Presentation". http://www.genworks.com. Genworks®International and Delft University of Technology. 2012. Retrieved 3 July 2014. {{cite web}}: External link in |website= (help)
  3. ^ Prasad, Brian. "What Distinguishes KBE from Automation". coe.org. Retrieved 3 July 2014. {{cite web}}: Check |archive-url= value (help); External link in |deadurl= (help); Unknown parameter |deadurl= ignored (|url-status= suggested) (help)
  4. ^ Dreneau, Fabrice; Boces, Oliver. "Generative Mechanical Design: An Asset for Small & Medium-Sized Businesses". web.archive.org. coe.org. Retrieved 3 July 2014.
  5. ^ Switlik, John (October/November 2005). "Knowledge Based Engineering (KBE): Update". coe.org. COE. Retrieved 6 July 2014. {{cite web}}: Check |archiveurl= value (help); Check date values in: |date= (help)
  6. ^ Spooner, David (1991). "Towards an Object-Oriented Data Model for a Mechanical CAD Database System". On Object-Oriented Database Systems Topics in Information Systems: 189–205. Retrieved 6 July 2014.
  7. ^ "AI Winter". http://www.ainewsletter.com. ainewsletter. Retrieved 6 July 2014. the AI Winter of the late 80s. The phrase was coined by analogy with "nuclear winter" - the theory that mass use of nuclear weapons would blot out the sun with smoke and dust, causing plunging global temperatures, a frozen Earth, and the extinction of humanity. The AI Winter merely caused the extinction of AI companies, partly because of the hype over expert systems and the disillusionment caused when business discovered their limitations. {{cite web}}: External link in |website= (help)
  8. ^ Berners-Lee, Tim; Hendler, James; Lassila, Ora (May 17, 2001). "The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities". Scientific American.
  9. ^ Zhang, W.Y.; Yun, J.W. (April 2008). "Exploring Semantic Web technologies for ontology-based modeling in collaborative engineering design". The International Journal of Advanced Manufacturing Technology. 36 (9–10): 833–843. Retrieved 6 July 2014.
  10. ^ Sainter, P (September 10–13, 2000). "PRODUCT KNOWLEDGE MANAGEMENT WITHIN KNOWLEDGE-BASEDENGINEERING SYSTEMS". Proceedings of DETC’00ASME 2000 Design Engineering Technical ConferenceAnd Computers and Information in Engineering Conference. Baltimore, Maryland: ASME. Retrieved 4 July 2014.{{cite journal}}: CS1 maint: date format (link)
  11. ^ "MOKA: A Framework for Structuring and Representing Engineering Knowledge". web.archive.org. Esprit Project. Retrieved 5 July 2014. {{cite web}}: Check |archiveurl= value (help)
  12. ^ Kendal, S.L.; Creen, M. (2007), An introduction to knowledge engineering, London: Springer, ISBN 978-1-84628-475-5, OCLC 70987401
  13. ^ Levesque, Hector; Ronald Brachman (1985). "A Fundamental Tradeoff in Knowledge Representation and Reasoning". In Ronald Brachman and Hector J. Levesque (ed.). Reading in Knowledge Representation. Morgan Kaufmann. p. 49. ISBN 0-934613-01-X. The good news in reducing KR service to theorem proving is that we now have a very clear, very specific notion of what the KR system should do; the bad new is that it is also clear that the services can not be provided... deciding whether or not a sentence in FOL is a theorem... is unsolvable.
  14. ^ Wilson, Walter. "A Language For Engineering Design" (PDF). http://step.nasa.gov. Lockheed Martin. Retrieved 4 July 2014. {{cite web}}: External link in |website= (help)
  15. ^ "Genworks". http://www.genworks.com. Retrieved 4 July 2014. {{cite web}}: External link in |website= (help)
  16. ^ "KBE Services for PLM RFP". omg.org. Object Management Group. 2006. Retrieved 4 July 2014.
  17. ^ "Computer Aided Design Services Specification". omg.org. Object Management Group. January 2005. Retrieved 4 July 2014.
  18. ^ ACM Communications, 11/09 Deep Data Dives Discover Natural Laws
  19. ^ iProd project (integrated management of product heterogeneous data)
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