Cognitive Bias in Knowledge Engineering Course

The paper presents experience in teaching of knowledge and ontological engineering. The teaching framework is targeted on the development of cognitive skills that will allow facilitating the process of knowledge elicitation, structuring and ontology development for scaffolding students’ research. The structuring procedure is the kernel of ontological engineering. The 5-steps ontology designing process is described. Special stress is put on “beautification” principles of ontology creating. The academic curriculum includes interactive game-format training of lateral thinking, interpersonal cognitive intellect and visual mind mapping techniques.

Technology for Ontological Engineering Lifecycle Support

The research is partially supported by Russian Foundation for Basic Research (grants 06-01-81005 and 07-01- 00053)

Teaching Strategies and Ontologies for E-Learning

* The work is partially suported by Russian Foundation for Basic Studies (grant 02-01-00466).

Operating Model of Knowledge Quantum Engineering for Decision-Making in Conditions of Indeterminacy

The operating model of knowledge quantum engineering for identification and prognostic decision- making in conditions of α-indeterminacy is suggested in the article. The synthesized operating model solves three basic tasks: Аt-task to formalize tk-knowledge; Вt-task to recognize (identify) objects according to observed results; Сt-task to extrapolate (prognosticate) the observed results. Operating derivation of identification and prognostic decisions using authentic different-level algorithmic knowledge quantum (using tRAKZ-method) assumes synthesis of authentic knowledge quantum database (BtkZ) using induction operator as a system of implicative laws, and then using deduction operator according to the observed tk-knowledge and BtkZ a derivation of identification or prognostic decisions in a form of new tk-knowledge.

Knowledge-based Robot Control

The paper is related with the problem of developing autonomous intelligent robots for complex environments. In details it outlines a knowledge-based robot control architecture that combines several techniques in order to supply an ability to adapt and act autonomously in complex environments. The described architecture has been implemented as a robotic system that demonstrates its operation in dynamic environment. Although the robotic system demonstrates a certain level of autonomy, the experiments show that there are situation, in which the developed base architecture should be complemented with additional modules. The last few chapters of the paper describe the experimentation results and the current state of further research towards the developed architecture.

Semantic Modelling for Product Line Engineering

The aim of our work is to present solutions and a methodical support for automated techniques and procedures in domain engineering, in particular for variability modeling. Our approach is based upon Semantic Modeling concepts, for which semantic description, representation patterns and inference mechanisms are defined. Thus, model-driven techniques enriched with semantics will allow flexibility and variability in representation means, reasoning power and the required analysis depth for the identification, interpretation and adaptation of artifact properties and qualities.