ProbDiVinE-MC: Multi-core LTL Model Checker for Probabilistic Systems

2008 ◽  
Author(s):  
Jirí Barnat ◽  
Lubós Brim ◽  
Ivana Cerná ◽  
Milan Ceška ◽  
Jana Tumová
Keyword(s):  
Model Checker ◽  
Probabilistic Systems

Abstraction and refinement in probabilistic systems

2005 ◽  
Vol 32 (4) ◽  
pp. 41-47 ◽  
Author(s):  
Annabelle McIver ◽  
Carroll Morgan
Keyword(s):  
Probabilistic Systems

scholarly journals Verification and Strategy Synthesis for Coalition Announcement Logic

2021 ◽  
Author(s):  
Natasha Alechina ◽  
Hans van Ditmarsch ◽  
Rustam Galimullin ◽  
Tuo Wang
Keyword(s):  
Model Checking ◽  
Proof Of Concept ◽  
Model Checker ◽  
Complete Model ◽  
Concept Model ◽  
The Family ◽  
Winning Strategies ◽  
Epistemic Goals

AbstractCoalition announcement logic (CAL) is one of the family of the logics of quantified announcements. It allows us to reason about what a coalition of agents can achieve by making announcements in the setting where the anti-coalition may have an announcement of their own to preclude the former from reaching its epistemic goals. In this paper, we describe a PSPACE-complete model checking algorithm for CAL that produces winning strategies for coalitions. The algorithm is implemented in a proof-of-concept model checker.

scholarly journals Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 42
Author(s):  
Mohammed Al-Nuaimi ◽  
Sapto Wibowo ◽  
Hongyang Qu ◽  
Jonathan Aitken ◽  
Sandor Veres
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Practical Implementation ◽  
Model Checker ◽  
Multi Agent Systems ◽  
Time Operation ◽  
Bdi Agent ◽  
Perception System ◽  
Probability Of Success

The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.

A Node-Style Visual Programming Environment for the nuXmv Model Checker

2020 ◽  
Author(s):  
Hayato Naito ◽  
Tomoyuki Yokogawa ◽  
Nao Igawa ◽  
Sousuke Amasaki ◽  
Hirohisa Aman ◽  
...  
Keyword(s):  
Visual Programming ◽  
Programming Environment ◽  
Model Checker

scholarly journals Branching bisimulation congruence for probabilistic systems

2012 ◽  
Vol 413 (1) ◽  
pp. 58-72 ◽  
Author(s):  
Suzana Andova ◽  
Sonja Georgievska ◽  
Nikola Trčka
Keyword(s):  
Probabilistic Systems ◽  
Branching Bisimulation
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