24. A method for generating directions for use in navigation during a current driving session, comprising: storing a plurality of target attributes; for at least a selection of the stored target attributes, storing a plurality of conditional variant models associated with the selected target attributes; receiving a route request from a user, including a target destination; generating a set of candidate routes; assigning an attribute weight to each target attribute of each candidate route, based on attribute preferences of the user; computing a score for each candidate route based on the attribute weights and one or more attribute models learned from previous user driving sessions, wherein at least one of the one or more attribute models provides a summary statistic of attribute values that have been observed during previous driving sessions, and wherein the computing comprises: probabilistically determining for a target attribute that is one of the selected target attributes which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute; and applying the determined conditional variant model to the target attribute; and providing at least one scored route to the user.
24. A method for generating directions for use in navigation during a current driving session, comprising: storing a plurality of target attributes; for at least a selection of the stored target attributes, storing a plurality of conditional variant models associated with the selected target attributes; receiving a route request from a user, including a target destination; generating a set of candidate routes; assigning an attribute weight to each target attribute of each candidate route, based on attribute preferences of the user; computing a score for each candidate route based on the attribute weights and one or more attribute models learned from previous user driving sessions, wherein at least one of the one or more attribute models provides a summary statistic of attribute values that have been observed during previous driving sessions, and wherein the computing comprises: probabilistically determining for a target attribute that is one of the selected target attributes which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute; and applying the determined conditional variant model to the target attribute; and providing at least one scored route to the user. 25. The method of claim 24 wherein probabilistically determining which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute comprises: determining if a conditional variant corresponds to an explicit or implicit condition; in response to the conditional variant corresponding to an explicit condition, assigning a probability of 1 to a conditional variant model that corresponds to the explicit condition, and assigning a probability of 0 to other conditional variant models associated with the target attribute; in response to the conditional variant corresponding to an implicit condition, assigning a probability to each conditional variant model associated with the target attribute, using Bayesian reasoning and observed data of the current driving session; and recomputing probabilities periodically during the current driving session, using subsequently observed data of the current driving session.