25 research outputs found
Efficient Model Learning for Human-Robot Collaborative Tasks
We present a framework for learning human user models from joint-action
demonstrations that enables the robot to compute a robust policy for a
collaborative task with a human. The learning takes place completely
automatically, without any human intervention. First, we describe the
clustering of demonstrated action sequences into different human types using an
unsupervised learning algorithm. These demonstrated sequences are also used by
the robot to learn a reward function that is representative for each type,
through the employment of an inverse reinforcement learning algorithm. The
learned model is then used as part of a Mixed Observability Markov Decision
Process formulation, wherein the human type is a partially observable variable.
With this framework, we can infer, either offline or online, the human type of
a new user that was not included in the training set, and can compute a policy
for the robot that will be aligned to the preference of this new user and will
be robust to deviations of the human actions from prior demonstrations. Finally
we validate the approach using data collected in human subject experiments, and
conduct proof-of-concept demonstrations in which a person performs a
collaborative task with a small industrial robot
The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015
In this paper we retrace the recent history of statistics by analyzing all
the papers published in five prestigious statistical journals since 1970,
namely: Annals of Statistics, Biometrika, Journal of the American Statistical
Association, Journal of the Royal Statistical Society, series B and Statistical
Science. The aim is to construct a kind of "taxonomy" of the statistical papers
by organizing and by clustering them in main themes. In this sense being
identified in a cluster means being important enough to be uncluttered in the
vast and interconnected world of the statistical research. Since the main
statistical research topics naturally born, evolve or die during time, we will
also develop a dynamic clustering strategy, where a group in a time period is
allowed to migrate or to merge into different groups in the following one.
Results show that statistics is a very dynamic and evolving science, stimulated
by the rise of new research questions and types of data
Modeling the variability of rankings
For better or for worse, rankings of institutions, such as universities,
schools and hospitals, play an important role today in conveying information
about relative performance. They inform policy decisions and budgets, and are
often reported in the media. While overall rankings can vary markedly over
relatively short time periods, it is not unusual to find that the ranks of a
small number of "highly performing" institutions remain fixed, even when the
data on which the rankings are based are extensively revised, and even when a
large number of new institutions are added to the competition. In the present
paper, we endeavor to model this phenomenon. In particular, we interpret as a
random variable the value of the attribute on which the ranking should ideally
be based. More precisely, if items are to be ranked then the true, but
unobserved, attributes are taken to be values of independent and
identically distributed variates. However, each attribute value is observed
only with noise, and via a sample of size roughly equal to , say. These
noisy approximations to the true attributes are the quantities that are
actually ranked. We show that, if the distribution of the true attributes is
light-tailed (e.g., normal or exponential) then the number of institutions
whose ranking is correct, even after recalculation using new data and even
after many new institutions are added, is essentially fixed. Formally, is
taken to be of order for any fixed , and the number of institutions
whose ranking is reliable depends very little on .Comment: Published in at http://dx.doi.org/10.1214/10-AOS794 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Information-Theoretic Study of Voting Systems
The typical paradigm in voting theory involves n
voters and m candidates. Every voter ranks the candidates resulting
in a permutation of the m candidates. A key problem is
to derive the aggregate result of the voting. A popular method
for vote aggregation is based on the Condorcet criterion. The
Condorcet winner is the candidate who wins every other candidate
by pairwise majority. However, the main disadvantage of
this approach, known as the Condorcet paradox, is that such a
winner does not necessarily exist since this criterion does not admit
transitivity. This paradox is mathematically likely (if voters
assign rankings uniformly at random, then with probability approaching
one with the number of candidates, there will not be
a Condorcet winner), however, in real life scenarios such as elections,
it is not likely to encounter the Condorcet paradox. In this
paper we attempt to improve our intuition regarding the gap between
the mathematics and reality of voting systems. We study a
special case where there is global intransitivity between all candidates.
We introduce tools from information theory and derive
an entropy-based characterization of global intransitivity. In addition,
we tighten this characterization by assuming that votes
tend to be similar; in particular they can be modeled as permutations
that are confined to a sphere defined by the Kendalls Ï„
distance
Epitope profiling via mixture modeling of ranked data
We propose the use of probability models for ranked data as a useful
alternative to a quantitative data analysis to investigate the outcome of
bioassay experiments, when the preliminary choice of an appropriate
normalization method for the raw numerical responses is difficult or subject to
criticism. We review standard distance-based and multistage ranking models and
in this last context we propose an original generalization of the Plackett-Luce
model to account for the order of the ranking elicitation process. The
usefulness of the novel model is illustrated with its maximum likelihood
estimation for a real data set. Specifically, we address the heterogeneous
nature of experimental units via model-based clustering and detail the
necessary steps for a successful likelihood maximization through a hybrid
version of the Expectation-Maximization algorithm. The performance of the
mixture model using the new distribution as mixture components is compared with
those relative to alternative mixture models for random rankings. A discussion
on the interpretation of the identified clusters and a comparison with more
standard quantitative approaches are finally provided.Comment: (revised to properly include references
An R package for analyzing and modeling ranking data
published_or_final_versio