4,978 research outputs found
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over this (possibly infinite) set, is under-studied. This paper aims
to fill this gap. We build on previous work on Gaussian processes and pairwise
comparisons for preference modelling, extend it to the multi-objective decision
support scenario, and propose new ordered preference elicitation strategies
based on ranking and clustering. Our main contribution is an in-depth
evaluation of these strategies using computer and human-based experiments. We
show that our proposed elicitation strategies outperform the currently used
pairwise methods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by using a linear
prior mean at the start and virtual comparisons to the nadir and ideal points,
increases performance. We demonstrate our decision support framework in a
real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at
https://github.com/lmzintgraf/gp_pref_elici
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Interactive product catalogue with user preference tracking
In the context of m-commerce, small screen size poses serious difficulty for users to browse effectively through a product catalogue, given the limited number of products that may be presented on-screen. Despite the availability of search engines, filters and recommender systems to aid users, these techniques focus on a narrow segment of product offering. The users are thus denied the opportunity to do a more expansive exploration of the products available. This paper describes a novel approach to overcome the constraints of small screen size. Through integration of a product catalogue with a recommender system, an adaptive system has been created that guides users through the process of product browsing. An original technique has been developed to cluster similar positive examples together to identify areas of interest of a user. The performance of this technique has been evaluated and the results proved to be promising
User preference extraction using dynamic query sliders in conjunction with UPS-EMO algorithm
One drawback of evolutionary multiobjective optimization algorithms (EMOA)
has traditionally been high computational cost to create an approximation of
the Pareto front: number of required objective function evaluations usually
grows high. On the other hand, for the decision maker (DM) it may be difficult
to select one of the many produced solutions as the final one, especially in
the case of more than two objectives.
To overcome the above mentioned drawbacks number of EMOA's incorporating the
decision makers preference information have been proposed. In this case, it is
possible to save objective function evaluations by generating only the part of
the front the DM is interested in, thus also narrowing down the pool of
possible selections for the final solution.
Unfortunately, most of the current EMO approaches utilizing preferences are
not very intuitive to use, i.e. they may require tweaking of unintuitive
parameters, and it is not always clear what kind of results one can get with
given set of parameters. In this study we propose a new approach to visually
inspect produced solutions, and to extract preference information from the DM
to further guide the search. Our approach is based on intuitive use of dynamic
query sliders, which serve as a means to extract preference information and are
part of the graphical user interface implemented for the efficient UPS-EMO
algorithm
EEMCS final report for the causal modeling for air transport safety (CATS) project
This document reports on the work realized by the DIAM in relation to the completion of the CATS model as presented in Figure 1.6 and tries to explain some of the steps taken for its completion. The project spans over a period of time of three years. Intermediate reports have been presented throughout the projectâs progress. These are presented in Appendix 1. In this report the continuousâdiscrete distributionâfree BBNs are briefly discussed. The human reliability models developed for dealing with dependence in the model variables are described and the software application UniNet is presente
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