24,531 research outputs found
Abstract Interpretation for Probabilistic Termination of Biological Systems
In a previous paper the authors applied the Abstract Interpretation approach
for approximating the probabilistic semantics of biological systems, modeled
specifically using the Chemical Ground Form calculus. The methodology is based
on the idea of representing a set of experiments, which differ only for the
initial concentrations, by abstracting the multiplicity of reagents present in
a solution, using intervals. In this paper, we refine the approach in order to
address probabilistic termination properties. More in details, we introduce a
refinement of the abstract LTS semantics and we abstract the probabilistic
semantics using a variant of Interval Markov Chains. The abstract probabilistic
model safely approximates a set of concrete experiments and reports
conservative lower and upper bounds for probabilistic termination
Answer Set Programming for Non-Stationary Markov Decision Processes
Non-stationary domains, where unforeseen changes happen, present a challenge
for agents to find an optimal policy for a sequential decision making problem.
This work investigates a solution to this problem that combines Markov Decision
Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming
(ASP) in a method we call ASP(RL). In this method, Answer Set Programming is
used to find the possible trajectories of an MDP, from where Reinforcement
Learning is applied to learn the optimal policy of the problem. Results show
that ASP(RL) is capable of efficiently finding the optimal solution of an MDP
representing non-stationary domains
Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
This paper presents a novel approach for augmenting proof-based verification
with performance-style analysis of the kind employed in state-of-the-art model
checking tools for probabilistic systems. Quantitative safety properties
usually specified as probabilistic system invariants and modeled in proof-based
environments are evaluated using bounded model checking techniques.
Our specific contributions include the statement of a theorem that is central
to model checking safety properties of proof-based systems, the establishment
of a procedure; and its full implementation in a prototype system (YAGA) which
readily transforms a probabilistic model specified in a proof-based environment
to its equivalent verifiable PRISM model equipped with reward structures. The
reward structures capture the exact interpretation of the probabilistic
invariants and can reveal succinct information about the model during
experimental investigations. Finally, we demonstrate the novelty of the
technique on a probabilistic library case study
A New Decision Support Framework for Managing Foot-and-mouth Disease Epidemics
Animal disease epidemics such as the foot-and-mouth disease (FMD) pose recurrent threat to countries with intensive livestock production. Efficient FMD control is crucial in limiting the damage of FMD epidemics and securing food production. Decision making in FMD control involves a hierarchy of decisions made at strategic, tactical, and operational levels. These decisions are interdependent and have to be made under uncertainty about future development of the epidemic. Addressing this decision problem, this paper presents a new decision-support framework based on multi-level hierarchic Markov processes (MLHMP). The MLHMP model simultaneously optimizes decisions at strategic, tactical, and operational levels, using Bayesian forecasting methods to model uncertainty and learning about the epidemic. As illustrated by the example, the framework is especially useful in contingency planning for future FMD epidemic
LTLf/LDLf Non-Markovian Rewards
In Markov Decision Processes (MDPs), the reward obtained in a state is Markovian, i.e., depends on the last state and action. This dependency makes it difficult to reward more interesting long-term behaviors, such as always closing a door after it has been opened, or providing coffee only following a request. Extending MDPs to handle non-Markovian reward functions was the subject of two previous lines of work. Both use LTL variants to specify the reward function and then compile the new model back into a Markovian model. Building on recent progress in temporal logics over finite traces, we adopt LDLf for specifying non-Markovian rewards and provide an elegant automata construction for building a Markovian model, which extends that of previous work and offers strong minimality and compositionality guarantees
Liveness of Randomised Parameterised Systems under Arbitrary Schedulers (Technical Report)
We consider the problem of verifying liveness for systems with a finite, but
unbounded, number of processes, commonly known as parameterised systems.
Typical examples of such systems include distributed protocols (e.g. for the
dining philosopher problem). Unlike the case of verifying safety, proving
liveness is still considered extremely challenging, especially in the presence
of randomness in the system. In this paper we consider liveness under arbitrary
(including unfair) schedulers, which is often considered a desirable property
in the literature of self-stabilising systems. We introduce an automatic method
of proving liveness for randomised parameterised systems under arbitrary
schedulers. Viewing liveness as a two-player reachability game (between
Scheduler and Process), our method is a CEGAR approach that synthesises a
progress relation for Process that can be symbolically represented as a
finite-state automaton. The method is incremental and exploits both
Angluin-style L*-learning and SAT-solvers. Our experiments show that our
algorithm is able to prove liveness automatically for well-known randomised
distributed protocols, including Lehmann-Rabin Randomised Dining Philosopher
Protocol and randomised self-stabilising protocols (such as the Israeli-Jalfon
Protocol). To the best of our knowledge, this is the first fully-automatic
method that can prove liveness for randomised protocols.Comment: Full version of CAV'16 pape
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