2 research outputs found
Reasoning and querying bounds on differences with layered preferences
Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and
calories. Recently, some approaches have extended the
BoDs framework in a fuzzy, \u201cnoncrisp\u201d direction,
considering probabilities or preferences. While previous
approaches have mainly aimed at providing an
optimal solution to the set of constraints, we propose
an innovative class of approaches in which constraint
propagation algorithms aim at identifying the \u201cspace of
solutions\u201d (i.e., the minimal network) with their preferences,
and query answering mechanisms are provided
to explore the space of solutions as required, for
example, in decision support tasks. Aiming at generality,
we propose a class of approaches parametrized
over user\u2010defined scales of qualitative preferences (e.g.,
Low, Medium, High, and Very High), utilizing the resume
and extension operations to combine preferences,
and considering different formalisms to associate preferences
with BoDs. We consider both \u201cgeneral\u201d preferences
and a form of layered preferences that we call
\u201cpyramid\u201d preferences. The properties of the class of
approaches are also analyzed. In particular, we show
that, when the resume and extension operations are
defined such that they constitute a closed semiring, a
more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation
of the constraint propagation algorithms
Temporal Reasoning with Layered Preferences
Temporal representation and temporal reasoning is a central in Artificial Intelligence. The literature is moving to the treatment of “non-crisp” temporal constraints, in which also preferences or probabilities are considered. However, most approaches only support numeric preferences, while, in many domain applications, users naturally operate on “layered” scales of values (e.g., Low, Medium, High), which are domain- and task-dependent. For many tasks, including decision support, the evaluation of the minimal network of the constraints (i.e., the tightest constraints) is of primary importance. We propose the first approach in the literature coping with layered preferences on quantitative temporal constraints. We extend the widely used simple temporal problem (STP) framework to consider layered user-defined preferences, proposing (i) a formal representation of quantitative constraints with layered preferences, and (ii) a temporal reasoning algorithm, based on the general algorithm Compute-Summaries, for the propagation of such temporal constraints. We also prove that our temporal reasoning algorithm evaluates the minimal network