921 research outputs found
Convex Hulls under Uncertainty
We study the convex-hull problem in a probabilistic setting, motivated by the
need to handle data uncertainty inherent in many applications, including sensor
databases, location-based services and computer vision. In our framework, the
uncertainty of each input site is described by a probability distribution over
a finite number of possible locations including a \emph{null} location to
account for non-existence of the point. Our results include both exact and
approximation algorithms for computing the probability of a query point lying
inside the convex hull of the input, time-space tradeoffs for the membership
queries, a connection between Tukey depth and membership queries, as well as a
new notion of \some-hull that may be a useful representation of uncertain
hulls
Probabilistic Bisimulations for PCTL Model Checking of Interval MDPs
Verification of PCTL properties of MDPs with convex uncertainties has been
investigated recently by Puggelli et al. However, model checking algorithms
typically suffer from state space explosion. In this paper, we address
probabilistic bisimulation to reduce the size of such an MDPs while preserving
PCTL properties it satisfies. We discuss different interpretations of
uncertainty in the models which are studied in the literature and that result
in two different definitions of bisimulations. We give algorithms to compute
the quotients of these bisimulations in time polynomial in the size of the
model and exponential in the uncertain branching. Finally, we show by a case
study that large models in practice can have small branching and that a
substantial state space reduction can be achieved by our approach.Comment: In Proceedings SynCoP 2014, arXiv:1403.784
On the expected diameter, width, and complexity of a stochastic convex-hull
We investigate several computational problems related to the stochastic
convex hull (SCH). Given a stochastic dataset consisting of points in
each of which has an existence probability, a SCH refers to the
convex hull of a realization of the dataset, i.e., a random sample including
each point with its existence probability. We are interested in computing
certain expected statistics of a SCH, including diameter, width, and
combinatorial complexity. For diameter, we establish the first deterministic
1.633-approximation algorithm with a time complexity polynomial in both and
. For width, two approximation algorithms are provided: a deterministic
-approximation running in time, and a fully
polynomial-time randomized approximation scheme (FPRAS). For combinatorial
complexity, we propose an exact -time algorithm. Our solutions exploit
many geometric insights in Euclidean space, some of which might be of
independent interest
HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing
Autonomous robots can benefit greatly from human-provided semantic
characterizations of uncertain task environments and states. However, the
development of integrated strategies which let robots model, communicate, and
act on such 'soft data' remains challenging. Here, the Human Assisted Robotic
Planning and Sensing (HARPS) framework is presented for active semantic sensing
and planning in human-robot teams to address these gaps by formally combining
the benefits of online sampling-based POMDP policies, multimodal semantic
interaction, and Bayesian data fusion. This approach lets humans
opportunistically impose model structure and extend the range of semantic soft
data in uncertain environments by sketching and labeling arbitrary landmarks
across the environment. Dynamic updating of the environment model while during
search allows robotic agents to actively query humans for novel and relevant
semantic data, thereby improving beliefs of unknown environments and states for
improved online planning. Simulations of a UAV-enabled target search
application in a large-scale partially structured environment show significant
improvements in time and belief state estimates required for interception
versus conventional planning based solely on robotic sensing. Human subject
studies in the same environment (n = 36) demonstrate an average doubling in
dynamic target capture rate compared to the lone robot case, and highlight the
robustness of active probabilistic reasoning and semantic sensing over a range
of user characteristics and interaction modalities
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