6,428 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
An extension of SPARQL for expressing qualitative preferences
In this paper we present SPREFQL, an extension of the SPARQL language that
allows appending a PREFER clause that expresses "soft" preferences over the
query results obtained by the main body of the query. The extension does not
add expressivity and any SPREFQL query can be transformed to an equivalent
standard SPARQL query. However, clearly separating preferences from the "hard"
patterns and filters in the WHERE clause gives queries where the intention of
the client is more cleanly expressed, an advantage for both human readability
and machine optimization. In the paper we formally define the syntax and the
semantics of the extension and we also provide empirical evidence that
optimizations specific to SPREFQL improve run-time efficiency by comparison to
the usually applied optimizations on the equivalent standard SPARQL query.Comment: Accepted to the 2017 International Semantic Web Conference, Vienna,
October 201
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