2,919 research outputs found
Quantitative Automata under Probabilistic Semantics
Automata with monitor counters, where the transitions do not depend on
counter values, and nested weighted automata are two expressive
automata-theoretic frameworks for quantitative properties. For a well-studied
and wide class of quantitative functions, we establish that automata with
monitor counters and nested weighted automata are equivalent. We study for the
first time such quantitative automata under probabilistic semantics. We show
that several problems that are undecidable for the classical questions of
emptiness and universality become decidable under the probabilistic semantics.
We present a complete picture of decidability for such automata, and even an
almost-complete picture of computational complexity, for the probabilistic
questions we consider
Sensor Synthesis for POMDPs with Reachability Objectives
Partially observable Markov decision processes (POMDPs) are widely used in
probabilistic planning problems in which an agent interacts with an environment
using noisy and imprecise sensors. We study a setting in which the sensors are
only partially defined and the goal is to synthesize "weakest" additional
sensors, such that in the resulting POMDP, there is a small-memory policy for
the agent that almost-surely (with probability~1) satisfies a reachability
objective. We show that the problem is NP-complete, and present a symbolic
algorithm by encoding the problem into SAT instances. We illustrate trade-offs
between the amount of memory of the policy and the number of additional sensors
on a simple example. We have implemented our approach and consider three
classical POMDP examples from the literature, and show that in all the examples
the number of sensors can be significantly decreased (as compared to the
existing solutions in the literature) without increasing the complexity of the
policies.Comment: arXiv admin note: text overlap with arXiv:1511.0845
POMDPs under Probabilistic Semantics
We consider partially observable Markov decision processes (POMDPs) with
limit-average payoff, where a reward value in the interval [0,1] is associated
to every transition, and the payoff of an infinite path is the long-run average
of the rewards. We consider two types of path constraints: (i) quantitative
constraint defines the set of paths where the payoff is at least a given
threshold lambda_1 in (0,1]; and (ii) qualitative constraint which is a special
case of quantitative constraint with lambda_1=1. We consider the computation of
the almost-sure winning set, where the controller needs to ensure that the path
constraint is satisfied with probability 1. Our main results for qualitative
path constraint are as follows: (i) the problem of deciding the existence of a
finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding
the existence of an infinite-memory controller is undecidable. For quantitative
path constraint we show that the problem of deciding the existence of a
finite-memory controller is undecidable.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
Limit Your Consumption! Finding Bounds in Average-energy Games
Energy games are infinite two-player games played in weighted arenas with
quantitative objectives that restrict the consumption of a resource modeled by
the weights, e.g., a battery that is charged and drained. Typically, upper
and/or lower bounds on the battery capacity are part of the problem
description. Here, we consider the problem of determining upper bounds on the
average accumulated energy or on the capacity while satisfying a given lower
bound, i.e., we do not determine whether a given bound is sufficient to meet
the specification, but if there exists a sufficient bound to meet it.
In the classical setting with positive and negative weights, we show that the
problem of determining the existence of a sufficient bound on the long-run
average accumulated energy can be solved in doubly-exponential time. Then, we
consider recharge games: here, all weights are negative, but there are recharge
edges that recharge the energy to some fixed capacity. We show that bounding
the long-run average energy in such games is complete for exponential time.
Then, we consider the existential version of the problem, which turns out to be
solvable in polynomial time: here, we ask whether there is a recharge capacity
that allows the system player to win the game.
We conclude by studying tradeoffs between the memory needed to implement
strategies and the bounds they realize. We give an example showing that memory
can be traded for bounds and vice versa. Also, we show that increasing the
capacity allows to lower the average accumulated energy.Comment: In Proceedings QAPL'16, arXiv:1610.0769
A guided tour of asynchronous cellular automata
Research on asynchronous cellular automata has received a great amount of
attention these last years and has turned to a thriving field. We survey the
recent research that has been carried out on this topic and present a wide
state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat
Two Variable vs. Linear Temporal Logic in Model Checking and Games
Model checking linear-time properties expressed in first-order logic has
non-elementary complexity, and thus various restricted logical languages are
employed. In this paper we consider two such restricted specification logics,
linear temporal logic (LTL) and two-variable first-order logic (FO2). LTL is
more expressive but FO2 can be more succinct, and hence it is not clear which
should be easier to verify. We take a comprehensive look at the issue, giving a
comparison of verification problems for FO2, LTL, and various sublogics thereof
across a wide range of models. In particular, we look at unary temporal logic
(UTL), a subset of LTL that is expressively equivalent to FO2; we also consider
the stutter-free fragment of FO2, obtained by omitting the successor relation,
and the expressively equivalent fragment of UTL, obtained by omitting the next
and previous connectives. We give three logic-to-automata translations which
can be used to give upper bounds for FO2 and UTL and various sublogics. We
apply these to get new bounds for both non-deterministic systems (hierarchical
and recursive state machines, games) and for probabilistic systems (Markov
chains, recursive Markov chains, and Markov decision processes). We couple
these with matching lower-bound arguments. Next, we look at combining FO2
verification techniques with those for LTL. We present here a language that
subsumes both FO2 and LTL, and inherits the model checking properties of both
languages. Our results give both a unified approach to understanding the
behaviour of FO2 and LTL, along with a nearly comprehensive picture of the
complexity of verification for these logics and their sublogics.Comment: 37 pages, to be published in Logical Methods in Computer Science
journal, includes material presented in Concur 2011 and QEST 2012 extended
abstract
Parametric LTL on Markov Chains
This paper is concerned with the verification of finite Markov chains against
parametrized LTL (pLTL) formulas. In pLTL, the until-modality is equipped with
a bound that contains variables; e.g., asserts that
holds within time steps, where is a variable on natural
numbers. The central problem studied in this paper is to determine the set of
parameter valuations for which the probability to
satisfy pLTL-formula in a Markov chain meets a given threshold , where is a comparison on reals and a probability. As for pLTL
determining the emptiness of is undecidable, we consider
several logic fragments. We consider parametric reachability properties, a
sub-logic of pLTL restricted to next and , parametric B\"uchi
properties and finally, a maximal subclass of pLTL for which emptiness of is decidable.Comment: TCS Track B 201
Policy iteration for perfect information stochastic mean payoff games with bounded first return times is strongly polynomial
Recent results of Ye and Hansen, Miltersen and Zwick show that policy
iteration for one or two player (perfect information) zero-sum stochastic
games, restricted to instances with a fixed discount rate, is strongly
polynomial. We show that policy iteration for mean-payoff zero-sum stochastic
games is also strongly polynomial when restricted to instances with bounded
first mean return time to a given state. The proof is based on methods of
nonlinear Perron-Frobenius theory, allowing us to reduce the mean-payoff
problem to a discounted problem with state dependent discount rate. Our
analysis also shows that policy iteration remains strongly polynomial for
discounted problems in which the discount rate can be state dependent (and even
negative) at certain states, provided that the spectral radii of the
nonnegative matrices associated to all strategies are bounded from above by a
fixed constant strictly less than 1.Comment: 17 page
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