%0 Journal Article %1 schreiber94 %A Schreiber, Guus %A Wielinga, Bob %A d. de Hoog, Robert %A Akkermans, Hans %A v. d. van de Velde, Walter %D 1994 %J IEEE Expert (IEEE Intelligent Systems) %K AI acquisition knowledge methodology %P 28--38 %T CommonKADS: A comprehensive methodology for KBS development %U http://www.cs.toronto.edu/~nernst/papers/schreiber-commonkads.pdf %V December

@article{schreiber94, added-at = {2006-10-23T17:25:28.000+0200}, author = {Schreiber, Guus and Wielinga, Bob and d. de Hoog, Robert and Akkermans, Hans and v. d. van de Velde, Walter}, biburl = {https://www.bibsonomy.org/bibtex/2bf949b33569e217df0fd317e7ecd4020/grahl}, citeulike-article-id = {121616}, comment = {models the application and the knowledge of the organization previous to this the AI community was more interested in rapid prototyping using LISP and ESS move of KE from acquiring knowledge to modelling it - not just the expert knowledge, but how that knowledge is used in the org. - expertise modelling is what distinguish KADS from other software development - a suite of models: expertise, task, organization, agent, communication, design - proposes a "cyclic, risk-driven" model similar to Boehm's spiral model - place the KBS in the larger context of the organization (using task, agent, organization models), recognizing that it forms a small part of the org entirety - "first generation KBS used one relatively simple <i>inference engine </i> working on a knowledge base in a particular representational format, usually production rules. But such a knowledge base hides important properties of the reasoning process and knowledge structure in the application domain" (we lose the context in which certain rules were generated, so we want to expose the knowledge level model) - <i>knowledge typing</i>provide an application independent view of how various knowledge components interact during the problem solving process - defines a metamodel of components of knowledge: domain knowledge (static, concepts, relations and facts needed to reason), control knowledge - two sub parts, (inference knowledge, how to use domain knowledge in inferences), task knowledge, how to decompose top-level reasoning task - an ontology is used to model these components - <i>interaction problem </i> states domain knowledge and control knowledge are highly interdependent (Bylander and Chandrasekaran, Knowledge Acquisition for KBS, 1988) - ontologies are linked together to define this - they use different levels of ontologies to identify the different levels of interoperability and dependency - can we think of PSMs as software patterns? PSM has a competency (goal matching) level, and acceptance criteria, to identify whether a task can be solved with the PSM -structure preserving design approach, - distinctions in the expertise model are preserved in the implementation, and design decisions are explicitly documented -claims the expressiveness of this approach is greater than ER modelling or OO analysis}, description = {sdasda}, interhash = {e5c49660cd553257a92e802794dabe30}, intrahash = {bf949b33569e217df0fd317e7ecd4020}, journal = {IEEE Expert (IEEE Intelligent Systems)}, keywords = {AI acquisition knowledge methodology}, pages = {28--38}, priority = {0}, timestamp = {2007-07-24T17:29:21.000+0200}, title = {Common{KADS}: {A} comprehensive methodology for {KBS} development}, url = {http://www.cs.toronto.edu/~nernst/papers/schreiber-commonkads.pdf}, volume = {December}, year = 1994 }