1,720,998 research outputs found
Resistance to bribery when aggregating soft constraints, and exploitation of bribery cost schemes in preference compilation and optimization
No full textDesigning quiet rotorcraft landing trajectories with probabilistic road maps.
No full textNext generation commercial air transportation will
likely see an increased use of rotorcraft, including helicopter
and tilt-rotors. Two advantages of rotorcraft with
respect to fixed wing aircraft are the optimized aerodynamic
levitation and the possibility of takeoff and
landing without a runway, which minimizes their interference
with fixed wing traffic. A recurring obstacle
to rotorcraft integration is the ground noise they
generate, particularly near residential areas and hospitals.
The problem of rotorcraft ground noise can be
partially addressed by designing new flight profiles, in
particular landing trajectories, that have a lower impact
in the noise produced by a rotorcraft. In this paper
we approach this problem computationally by modeling
a high dimensional environment able to capture
many parameters of landing trajectories that contribute
to ground noise, and using probabilistic road maps
(PRMs), coupled with a robust ground noise simulation
system, for identifying noise-minimal approach trajectories
Soft Constraint Problems With Uncontrollable Variables
No full textPreferences and uncertainty are common in many real-life problems. In this article, we consider preferences modelled via soft constraints that allow for the representation of quantitative preferences. Moreover, we consider uncertainty modelled via uncontrollable variables, that is, variables whose value cannot be decided by us. We also assume that some information is provided for such variables in the form of a possibility distribution over their domains. Possibilities provide a way to model imprecise probabilities, and give us a way to know which values are more possible than others for the uncontrollable variables. For such problems, the aim is to find a solution with a high preference which is also very robust with respect to the uncontrollable part. To tackle such problems, we adopt an existing approach that eliminates the uncertain part of the problem while adding some constraints in the remaining part, and then solves the resulting problem. However, contrarily to the specific methods present in the literature, we formulate several desirable properties, on the robustness of the problem's solutions and its relationship with their preferences, that should be satisfied by any specific method based on this approach. We also define several semantics to order the solutions according to different attitudes to risk, and we discuss which of the desirable properties are satisfied by each of the considered semantics. Finally, we present a solver for this kind of problems, and we show some experimental results of its application over the classes of such problems
- …
