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This work aims to develop a model checking method to verify the decision making system of Unmanned Surface Vehicle (USV) in a long range surveillance mission. The scenario in this work was captured from a long endurance USV surveillance mission using C-Enduro, an USV manufactured by ASV Ltd. The C-Enduro USV may encounter multiple non-deterministic and concurrent problems including lost communication signals, collision risk and malfunction. The vehicle is designed to utilise multiple energy sources from solar panel, wind turbine and diesel generator. The energy state can be affected by the solar irradiance condition, wind condition, states of the diesel generator, sea current condition and states of the USV. In this research, the states and the interactive relations between environmental uncertainties, sensors, USV energy system, USV and Ground Control Station (GCS) decision making systems are abstracted and modelled successfully using Kripke models. The desirable properties to be verified are expressed using temporal logic statement and finally the safety properties and the long endurance properties are verified using the model checker MCMAS, a model checker for multi-agent systems. The verification results are analyzed and show the feasibility of applying model checking method to retrospect the desirable property of the USV decision making system. This method could assist researcher to identify potential design error of decision making system in advance.
We present a survey of the saturation method for model-checking pushdown systems.
We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs). The state of a (discrete-time) BMCs is a collection of entities of various type
We consider Markov decision processes (MDP) as generators of sequences of probability distributions over states. A probability distribution is p-synchronizing if the probability mass is at least p in a single state, or in a given set of states. We co
We address the safety verification and synthesis problems for real-time systems. We introduce real-time programs that are made of instructions that can perform assignments to discrete and real-valued variables. They are general enough to capture inte
In this paper, we study the parameter synthesis problem for a class of parametric timed automata. The problem asks to construct the set of valuations of the parameters in the parametric timed automa- ton, referred to as the feasible region, under whi