16 research outputs found
Parameter-Independent Strategies for pMDPs via POMDPs
Markov Decision Processes (MDPs) are a popular class of models suitable for
solving control decision problems in probabilistic reactive systems. We
consider parametric MDPs (pMDPs) that include parameters in some of the
transition probabilities to account for stochastic uncertainties of the
environment such as noise or input disturbances.
We study pMDPs with reachability objectives where the parameter values are
unknown and impossible to measure directly during execution, but there is a
probability distribution known over the parameter values. We study for the
first time computing parameter-independent strategies that are expectation
optimal, i.e., optimize the expected reachability probability under the
probability distribution over the parameters. We present an encoding of our
problem to partially observable MDPs (POMDPs), i.e., a reduction of our problem
to computing optimal strategies in POMDPs.
We evaluate our method experimentally on several benchmarks: a motivating
(repeated) learner model; a series of benchmarks of varying configurations of a
robot moving on a grid; and a consensus protocol.Comment: Extended version of a QEST 2018 pape
Parameter Synthesis in Markov Models: A Gentle Survey
This paper surveys the analysis of parametric Markov models whose transitions
are labelled with functions over a finite set of parameters. These models are
symbolic representations of uncountable many concrete probabilistic models,
each obtained by instantiating the parameters. We consider various analysis
problems for a given logical specification : do all parameter
instantiations within a given region of parameter values satisfy ?,
which instantiations satisfy and which ones do not?, and how can all
such instantiations be characterised, either exactly or approximately? We
address theoretical complexity results and describe the main ideas underlying
state-of-the-art algorithms that established an impressive leap over the last
decade enabling the fully automated analysis of models with millions of states
and thousands of parameters
Parameter Synthesis for Markov Models
Markov chain analysis is a key technique in reliability engineering. A
practical obstacle is that all probabilities in Markov models need to be known.
However, system quantities such as failure rates or packet loss ratios, etc.
are often not---or only partially---known. This motivates considering
parametric models with transitions labeled with functions over parameters.
Whereas traditional Markov chain analysis evaluates a reliability metric for a
single, fixed set of probabilities, analysing parametric Markov models focuses
on synthesising parameter values that establish a given reliability or
performance specification . Examples are: what component failure rates
ensure the probability of a system breakdown to be below 0.00000001?, or which
failure rates maximise reliability? This paper presents various analysis
algorithms for parametric Markov chains and Markov decision processes. We focus
on three problems: (a) do all parameter values within a given region satisfy
?, (b) which regions satisfy and which ones do not?, and (c)
an approximate version of (b) focusing on covering a large fraction of all
possible parameter values. We give a detailed account of the various
algorithms, present a software tool realising these techniques, and report on
an extensive experimental evaluation on benchmarks that span a wide range of
applications.Comment: 38 page
Shepherding Hordes of Markov Chains
This paper considers large families of Markov chains (MCs) that are defined
over a set of parameters with finite discrete domains. Such families occur in
software product lines, planning under partial observability, and sketching of
probabilistic programs. Simple questions, like `does at least one family member
satisfy a property?', are NP-hard. We tackle two problems: distinguish family
members that satisfy a given quantitative property from those that do not, and
determine a family member that satisfies the property optimally, i.e., with the
highest probability or reward. We show that combining two well-known
techniques, MDP model checking and abstraction refinement, mitigates the
computational complexity. Experiments on a broad set of benchmarks show that in
many situations, our approach is able to handle families of millions of MCs,
providing superior scalability compared to existing solutions.Comment: Full version of TACAS'19 submissio
Stochastic Finite State Control of POMDPs with LTL Specifications
Partially observable Markov decision processes (POMDPs) provide a modeling framework for autonomous decision making under uncertainty and imperfect sensing, e.g. robot manipulation and self-driving cars. However, optimal control of POMDPs is notoriously intractable. This paper considers the quantitative problem of synthesizing sub-optimal stochastic finite state controllers (sFSCs) for POMDPs such that the probability of satisfying a set of high-level specifications in terms of linear temporal logic (LTL) formulae is maximized. We begin by casting the latter problem into an optimization and use relaxations based on the Poisson equation and McCormick envelopes. Then, we propose an stochastic bounded policy iteration algorithm, leading to a controlled growth in sFSC size and an any time algorithm, where the performance of the controller improves with successive iterations, but can be stopped by the user based on time or memory considerations. We illustrate the proposed method by a robot navigation case study
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system
development and have played a key role in establishing the correctness of
safety-critical systems. The main building blocks of formal methods are models
and specifications, which are analogous to behaviors and requirements in system
design and give us the means to verify and synthesize system behaviors with
formal guarantees.
This monograph provides a survey of the current state of the art on
applications of formal methods in the autonomous systems domain. We consider
correct-by-construction synthesis under various formulations, including closed
systems, reactive, and probabilistic settings. Beyond synthesizing systems in
known environments, we address the concept of uncertainty and bound the
behavior of systems that employ learning using formal methods. Further, we
examine the synthesis of systems with monitoring, a mitigation technique for
ensuring that once a system deviates from expected behavior, it knows a way of
returning to normalcy. We also show how to overcome some limitations of formal
methods themselves with learning. We conclude with future directions for formal
methods in reinforcement learning, uncertainty, privacy, explainability of
formal methods, and regulation and certification