22,629 research outputs found
Probabilistic Guarantees for Safe Deep Reinforcement Learning
Deep reinforcement learning has been successfully applied to many control
tasks, but the application of such agents in safety-critical scenarios has been
limited due to safety concerns. Rigorous testing of these controllers is
challenging, particularly when they operate in probabilistic environments due
to, for example, hardware faults or noisy sensors. We propose MOSAIC, an
algorithm for measuring the safety of deep reinforcement learning agents in
stochastic settings. Our approach is based on the iterative construction of a
formal abstraction of a controller's execution in an environment, and leverages
probabilistic model checking of Markov decision processes to produce
probabilistic guarantees on safe behaviour over a finite time horizon. It
produces bounds on the probability of safe operation of the controller for
different initial configurations and identifies regions where correct behaviour
can be guaranteed. We implement and evaluate our approach on agents trained for
several benchmark control problems
Handwritten digit recognition by bio-inspired hierarchical networks
The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evidences, we propose a learning framework with a
strong biological plausibility that mimics prominent functions of cortical
circuitries. We developed the Inductive Conceptual Network (ICN), that is a
hierarchical bio-inspired network, able to learn invariant patterns by
Variable-order Markov Models implemented in its nodes. The outputs of the
top-most node of ICN hierarchy, representing the highest input generalization,
allow for automatic classification of inputs. We found that the ICN clusterized
MNIST images with an error of 5.73% and USPS images with an error of 12.56%
When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks
We introduce a framework for the modeling of sequential data capturing
pathways of varying lengths observed in a network. Such data are important,
e.g., when studying click streams in information networks, travel patterns in
transportation systems, information cascades in social networks, biological
pathways or time-stamped social interactions. While it is common to apply graph
analytics and network analysis to such data, recent works have shown that
temporal correlations can invalidate the results of such methods. This raises a
fundamental question: when is a network abstraction of sequential data
justified? Addressing this open question, we propose a framework which combines
Markov chains of multiple, higher orders into a multi-layer graphical model
that captures temporal correlations in pathways at multiple length scales
simultaneously. We develop a model selection technique to infer the optimal
number of layers of such a model and show that it outperforms previously used
Markov order detection techniques. An application to eight real-world data sets
on pathways and temporal networks shows that it allows to infer graphical
models which capture both topological and temporal characteristics of such
data. Our work highlights fallacies of network abstractions and provides a
principled answer to the open question when they are justified. Generalizing
network representations to multi-order graphical models, it opens perspectives
for new data mining and knowledge discovery algorithms.Comment: 10 pages, 4 figures, 1 table, companion python package pathpy
available on gitHu
Integrating heterogeneous knowledges for understanding biological behaviors: a probabilistic approach
Despite recent molecular technique improvements, biological knowledge remains
incomplete. Reasoning on living systems hence implies to integrate
heterogeneous and partial informations. Although current investigations
successfully focus on qualitative behaviors of macromolecular networks, others
approaches show partial quantitative informations like protein concentration
variations over times. We consider that both informations, qualitative and
quantitative, have to be combined into a modeling method to provide a better
understanding of the biological system. We propose here such a method using a
probabilistic-like approach. After its exhaustive description, we illustrate
its advantages by modeling the carbon starvation response in Escherichia coli.
In this purpose, we build an original qualitative model based on available
observations. After the formal verification of its qualitative properties, the
probabilistic model shows quantitative results corresponding to biological
expectations which confirm the interest of our probabilistic approach.Comment: 10 page
MeGARA: Menu-based Game Abstraction and Abstraction Refinement of Markov Automata
Markov automata combine continuous time, probabilistic transitions, and
nondeterminism in a single model. They represent an important and powerful way
to model a wide range of complex real-life systems. However, such models tend
to be large and difficult to handle, making abstraction and abstraction
refinement necessary. In this paper we present an abstraction and abstraction
refinement technique for Markov automata, based on the game-based and
menu-based abstraction of probabilistic automata. First experiments show that a
significant reduction in size is possible using abstraction.Comment: In Proceedings QAPL 2014, arXiv:1406.156
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