775 research outputs found
Model checking for imprecise Markov chains.
We extend probabilistic computational tree logic for expressing properties of Markov chains to imprecise Markov chains, and provide an efficient algorithm for model checking of imprecise Markov chains. Thereby, we provide a formal framework to answer a very wide range of questions about imprecise Markov chains, in a systematic and computationally efficient way
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
Feature selection is playing an increasingly significant role with respect to
many computer vision applications spanning from object recognition to visual
object tracking. However, most of the recent solutions in feature selection are
not robust across different and heterogeneous set of data. In this paper, we
address this issue proposing a robust probabilistic latent graph-based feature
selection algorithm that performs the ranking step while considering all the
possible subsets of features, as paths on a graph, bypassing the combinatorial
problem analytically. An appealing characteristic of the approach is that it
aims to discover an abstraction behind low-level sensory data, that is,
relevancy. Relevancy is modelled as a latent variable in a PLSA-inspired
generative process that allows the investigation of the importance of a feature
when injected into an arbitrary set of cues. The proposed method has been
tested on ten diverse benchmarks, and compared against eleven state of the art
feature selection methods. Results show that the proposed approach attains the
highest performance levels across many different scenarios and difficulties,
thereby confirming its strong robustness while setting a new state of the art
in feature selection domain.Comment: Accepted at the IEEE International Conference on Computer Vision
(ICCV), 2017, Venice. Preprint cop
Risk Aversion in Finite Markov Decision Processes Using Total Cost Criteria and Average Value at Risk
In this paper we present an algorithm to compute risk averse policies in
Markov Decision Processes (MDP) when the total cost criterion is used together
with the average value at risk (AVaR) metric. Risk averse policies are needed
when large deviations from the expected behavior may have detrimental effects,
and conventional MDP algorithms usually ignore this aspect. We provide
conditions for the structure of the underlying MDP ensuring that approximations
for the exact problem can be derived and solved efficiently. Our findings are
novel inasmuch as average value at risk has not previously been considered in
association with the total cost criterion. Our method is demonstrated in a
rapid deployment scenario, whereby a robot is tasked with the objective of
reaching a target location within a temporal deadline where increased speed is
associated with increased probability of failure. We demonstrate that the
proposed algorithm not only produces a risk averse policy reducing the
probability of exceeding the expected temporal deadline, but also provides the
statistical distribution of costs, thus offering a valuable analysis tool
Analysis of Timed and Long-Run Objectives for Markov Automata
Markov automata (MAs) extend labelled transition systems with random delays
and probabilistic branching. Action-labelled transitions are instantaneous and
yield a distribution over states, whereas timed transitions impose a random
delay governed by an exponential distribution. MAs are thus a nondeterministic
variation of continuous-time Markov chains. MAs are compositional and are used
to provide a semantics for engineering frameworks such as (dynamic) fault
trees, (generalised) stochastic Petri nets, and the Architecture Analysis &
Design Language (AADL). This paper considers the quantitative analysis of MAs.
We consider three objectives: expected time, long-run average, and timed
(interval) reachability. Expected time objectives focus on determining the
minimal (or maximal) expected time to reach a set of states. Long-run
objectives determine the fraction of time to be in a set of states when
considering an infinite time horizon. Timed reachability objectives are about
computing the probability to reach a set of states within a given time
interval. This paper presents the foundations and details of the algorithms and
their correctness proofs. We report on several case studies conducted using a
prototypical tool implementation of the algorithms, driven by the MAPA
modelling language for efficiently generating MAs.Comment: arXiv admin note: substantial text overlap with arXiv:1305.705
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