3 research outputs found
Possibilistic Conditional Preference Networks
International audienceThe paper discusses the use of product-based possibilistic networks for representing conditional preference statements on discrete variables. The approach uses non-instantiated possibility weights to define conditional preference tables. Moreover, additional information about the relative strengths of symbolic weights can be taken into account. It yields a partial preference order among possible choices corresponding to a symmetric form of Pareto ordering. In the case of Boolean variables, this partial ordering coincides with the inclusion between the sets of preference statements that are violated. Furthermore, this graphical model has two logical counterparts in terms of possibilistic logic and penalty logic. The flexibility and the representational power of the approach are stressed. Besides, algorithms for handling optimization and dominance queries are provided
Query Answering in Ontologies under Preference Rankings
We present an ontological framework, based on
preference rankings, that allows users to express
their preferences between the knowledge explicitly
available in the ontology. Using this formalism, the
answers for a given query to an ontology can be
ranked by preference, allowing users to retrieve the
most preferred answers only. We provide a host of
complexity results for the main computational tasks
in this framework, for the general case, and for EL
and DL-Litecore as underlying ontology languages
Possibilistic conditional preference networks
© Springer International Publishing Switzerland 2015. The paper discusses the use of product-based possibilistic networks for representing conditional preference statements on discrete variables. The approach uses non-instantiated possibility weights to define conditional preference tables. Moreover, additional information about the relative strengths of symbolic weights can be taken into account. It yields a partial preference order among possible choices corresponding to a symmetric form of Pareto ordering. In the case of Boolean variables, this partial ordering coincides with the inclusion between the sets of preference statements that are violated. Furthermore, this graphical model has two logical counterparts in terms of possibilistic logic and penalty logic. The flexibility and the representational power of the approach are stressed. Besides, algorithms for handling optimization and dominance queries are provided