615 research outputs found
Computing the entropy of user navigation in the web
Navigation through the web, colloquially known as "surfing", is one of the main activities of users during web interaction. When users follow a navigation trail they often tend to get disoriented in terms of the goals of their original query and thus the discovery of typical user trails could be useful in providing navigation assistance. Herein, we give a theoretical underpinning of user navigation in terms of the entropy of an underlying Markov chain modelling the web topology. We present a novel method for online incremental computation of the entropy and a large deviation result regarding the length of a trail to realize the said entropy. We provide an error analysis for our estimation of the entropy in terms of the divergence between the empirical and actual probabilities. We then indicate applications of our algorithm in the area of web data mining. Finally, we present an extension of our technique to higher-order Markov chains by a suitable reduction of a higher-order Markov chain model to a first-order one
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Markov decision processes (MDPs) are the standard formalism for modelling
sequential decision making in stochastic environments. Policy synthesis
addresses the problem of how to control or limit the decisions an agent makes
so that a given specification is met. In this paper we consider PCTL*, the
probabilistic counterpart of CTL*, as the specification language. Because in
general the policy synthesis problem for PCTL* is undecidable, we restrict to
policies whose execution history memory is finitely bounded a priori.
Surprisingly, no algorithm for policy synthesis for this natural and
expressive framework has been developed so far. We close this gap and describe
a tableau-based algorithm that, given an MDP and a PCTL* specification, derives
in a non-deterministic way a system of (possibly nonlinear) equalities and
inequalities. The solutions of this system, if any, describe the desired
(stochastic) policies.
Our main result in this paper is the correctness of our method, i.e.,
soundness, completeness and termination.Comment: This is a long version of a conference paper published at TABLEAUX
2017. It contains proofs of the main results and fixes a bug. See the
footnote on page 1 for detail
An Inverse Method for Policy-Iteration Based Algorithms
We present an extension of two policy-iteration based algorithms on weighted
graphs (viz., Markov Decision Problems and Max-Plus Algebras). This extension
allows us to solve the following inverse problem: considering the weights of
the graph to be unknown constants or parameters, we suppose that a reference
instantiation of those weights is given, and we aim at computing a constraint
on the parameters under which an optimal policy for the reference instantiation
is still optimal. The original algorithm is thus guaranteed to behave well
around the reference instantiation, which provides us with some criteria of
robustness. We present an application of both methods to simple examples. A
prototype implementation has been done
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
Link Prediction Based on Local Random Walk
The problem of missing link prediction in complex networks has attracted much
attention recently. Two difficulties in link prediction are the sparsity and
huge size of the target networks. Therefore, the design of an efficient and
effective method is of both theoretical interests and practical significance.
In this Letter, we proposed a method based on local random walk, which can give
competitively good prediction or even better prediction than other
random-walk-based methods while has a lower computational complexity.Comment: 6 pages, 2 figure
Complexity of Manipulative Actions When Voting with Ties
Most of the computational study of election problems has assumed that each
voter's preferences are, or should be extended to, a total order. However in
practice voters may have preferences with ties. We study the complexity of
manipulative actions on elections where voters can have ties, extending the
definitions of the election systems (when necessary) to handle voters with
ties. We show that for natural election systems allowing ties can both increase
and decrease the complexity of manipulation and bribery, and we state a general
result on the effect of voters with ties on the complexity of control.Comment: A version of this paper will appear in ADT-201
Distribution-based bisimulation for labelled Markov processes
In this paper we propose a (sub)distribution-based bisimulation for labelled
Markov processes and compare it with earlier definitions of state and event
bisimulation, which both only compare states. In contrast to those state-based
bisimulations, our distribution bisimulation is weaker, but corresponds more
closely to linear properties. We construct a logic and a metric to describe our
distribution bisimulation and discuss linearity, continuity and compositional
properties.Comment: Accepted by FORMATS 201
Unary probabilistic and quantum automata on promise problems
We continue the systematic investigation of probabilistic and quantum finite
automata (PFAs and QFAs) on promise problems by focusing on unary languages. We
show that bounded-error QFAs are more powerful than PFAs. But, in contrary to
the binary problems, the computational powers of Las-Vegas QFAs and
bounded-error PFAs are equivalent to deterministic finite automata (DFAs).
Lastly, we present a new family of unary promise problems with two parameters
such that when fixing one parameter QFAs can be exponentially more succinct
than PFAs and when fixing the other parameter PFAs can be exponentially more
succinct than DFAs.Comment: Minor correction
Random walks on the Apollonian network with a single trap
Explicit determination of the mean first-passage time (MFPT) for trapping
problem on complex media is a theoretical challenge. In this paper, we study
random walks on the Apollonian network with a trap fixed at a given hub node
(i.e. node with the highest degree), which are simultaneously scale-free and
small-world. We obtain the precise analytic expression for the MFPT that is
confirmed by direct numerical calculations. In the large system size limit, the
MFPT approximately grows as a power-law function of the number of nodes, with
the exponent much less than 1, which is significantly different from the
scaling for some regular networks or fractals, such as regular lattices,
Sierpinski fractals, T-graph, and complete graphs. The Apollonian network is
the most efficient configuration for transport by diffusion among all
previously studied structure.Comment: Definitive version accepted for publication in EPL (Europhysics
Letters
Stochastic B\"acklund transformations
How does one introduce randomness into a classical dynamical system in order
to produce something which is related to the `corresponding' quantum system? We
consider this question from a probabilistic point of view, in the context of
some integrable Hamiltonian systems
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