By Roger Nkambou, Riichiro Mizoguchi, Jacqueline Bourdeau
The notion for this ebook on clever Tutoring structures (ITS) was once sparked through the good fortune of the ITS’08 foreign convention. The variety of displays and their caliber bore witness to the energy and adulthood of the sphere, and the passion of the contributors held out a promise of sustainability and leading edge study. lengthy lifestyles to ITS examine! The booklet is split into 5 components. The introductory chapters to those components, which summarize foundations, advancements, strengths and weaknesses in all of the parts lined, are addressed to all readers. when you wish extra in-depth wisdom, we provide the ground to researchers who current their paintings, their effects, and their view of what the long run holds. it truly is our wish that each one readers will locate the e-book informative and thought-provoking.
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Extra resources for Advances in Intelligent Tutoring Systems
These reasoning services may subsequently be made available to applications that make use of the knowledge represented in the ontology or in the frames. There may be some drawbacks with more expressive DLs, which make them difficult to use in real world applications. These include the intractability of their satisfiability or subsumption algorithms, and the increase in the computational complexity of reasoning. However, some research results show that efficient and practical implementations of relatively expressive languages are feasible despite their theoretical complexity (Horrocks 1998; Horrocks and Sattler 2007).
Some of these extensions have focused on concept descriptions, a process equivalent to those of conceptual graphs and description logics (Coupey and Faron 1998; Delteil and Faron 2002). For instance, Delteil & Faron (2002) proposed a graph-based concept description language called GDL, which involves a thorough decisionmaking procedure. It can be used to represent concept descriptions with complex graph patterns as well as negation and disjunction, which leads to an expressive language. GDL combines features of both conceptual graphs and description logics (see subsequent sections).
The latter is presented in the next paragraph. 3 Conceptual Graphs This formalism (Sowa 1984) is based on semantic networks but is directly linked to the language of first-order predicate logic from which it takes its semantics. A simple conceptual graph is a bipartite (not necessarily connected) graph composed of concept nodes that represent entities, attributes, states or events, and relation nodes that describe the relationships between these concepts. Each concept node c of a graph G is labelled by a pair (type(c), ref(c)), in which ref(c) is either the generic marker * corresponding to the existential quantification or an individual marker corresponding to an identifier.
Advances in Intelligent Tutoring Systems by Roger Nkambou, Riichiro Mizoguchi, Jacqueline Bourdeau