28 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
Generalized Random Utility Models with Multiple Types
We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.EconomicsEngineering and Applied SciencesMathematic
Learning in the Wild with Incremental Skeptical Gaussian Processes
The ability to learn from human supervision is fundamental for personal
assistants and other interactive applications of AI. Two central challenges for
deploying interactive learners in the wild are the unreliable nature of the
supervision and the varying complexity of the prediction task. We address a
simple but representative setting, incremental classification in the wild,
where the supervision is noisy and the number of classes grows over time. In
order to tackle this task, we propose a redesign of skeptical learning centered
around Gaussian Processes (GPs). Skeptical learning is a recent interactive
strategy in which, if the machine is sufficiently confident that an example is
mislabeled, it asks the annotator to reconsider her feedback. In many cases,
this is often enough to obtain clean supervision. Our redesign, dubbed ISGP,
leverages the uncertainty estimates supplied by GPs to better allocate labeling
and contradiction queries, especially in the presence of noise. Our experiments
on synthetic and real-world data show that, as a result, while the original
formulation of skeptical learning produces over-confident models that can fail
completely in the wild, ISGP works well at varying levels of noise and as new
classes are observed.Comment: 7 pages, 3 figures, code:
https://gitlab.com/abonte/incremental-skeptical-g