4,978 research outputs found

    Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

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    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

    User preference extraction using dynamic query sliders in conjunction with UPS-EMO algorithm

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    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

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    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 project’s progress. These are presented in Appendix 1. In this report the continuous‐discrete distribution‐free 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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