755 research outputs found
Sapo: Reachability Computation and Parameter Synthesis of Polynomial Dynamical Systems
Sapo is a C++ tool for the formal analysis of polynomial dynamical systems.
Its main features are: 1) Reachability computation, i.e., the calculation of
the set of states reachable from a set of initial conditions, and 2) Parameter
synthesis, i.e., the refinement of a set of parameters so that the system
satisfies a given specification. Sapo can represent reachable sets as unions of
boxes, parallelotopes, or parallelotope bundles (symbolic representation of
polytopes). Sets of parameters are represented with polytopes while
specifications are formalized as Signal Temporal Logic (STL) formulas
Time-Constrained Temporal Logic Control of Multi-Affine Systems
In this paper, we consider the problem of controlling a dynamical system such
that its trajectories satisfy a temporal logic property in a given amount of
time. We focus on multi-affine systems and specifications given as
syntactically co-safe linear temporal logic formulas over rectangular regions
in the state space. The proposed algorithm is based on the estimation of time
bounds for facet reachability problems and solving a time optimal reachability
problem on the product between a weighted transition system and an automaton
that enforces the satisfaction of the specification. A random optimization
algorithm is used to iteratively improve the solution
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
Strategy Synthesis for Autonomous Agents Using PRISM
We present probabilistic models for autonomous agent search and retrieve missions derived from Simulink models for an Unmanned Aerial Vehicle (UAV) and show how probabilistic model checking and the probabilistic model checker PRISM can be used for optimal controller generation. We introduce a sequence of scenarios relevant to UAVs and other autonomous agents such as underwater and ground vehicles. For each scenario we demonstrate how it can be modelled using the PRISM language, give model checking statistics and present the synthesised optimal controllers. We conclude with a discussion of the limitations when using probabilistic model checking and PRISM in this context and what steps can be taken to overcome them. In addition, we consider how the controllers can be returned to the UAV and adapted for use on larger search areas
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