One of the main difficulties with configuration
problem solving lies in the representation of the domain
knowledge because many different aspects, such as taxonomy,
topology, constraints, resource balancing, component generation,
etc., have to be captured in a single model. This model
must be expressive, declarative, and structured enough
to be easy to maintain and to be easily used by many different
kind of reasoning algorithms. This paper presents a new
framework where a configuration problem is considered both
as a classification problem and as a constraint satisfaction
problem (CSP). Our approach deeply blends concepts from
the CSP and object-oriented paradigms to adopt the strengths
of both. We expose how we have integrated taxonomic reasoning
in the constraint programming schema. We also introduce
new constrained variables with nonfinite domains to deal
with the fact that the set of components is previously
unknown and is constructed during the search for solution.
Our work strongly focuses on the representation and the
structuring of the domain knowledge, because the most common
drawback of previous works is the difficulty to maintain
the knowledge base that is due to a lack of structure and
expressiveness of the knowledge representation model. The
main contribution of our work is to provide an object-oriented
model completely integrated in the CSP schema, with inheritance
and classification mechanisms, and with specific arc consistency
algorithms.
Configuration knowledge representations for Semantic Web applications
