CONDITIONED SLICING FOR EFFICIENT MULTIWAY DECISION GRAPHS MODEL-CHECKER

Elmansori

doi:10.3844/jcssp.2013.314.326

Verification and Strategy Synthesis for Coalition Announcement Logic

Journal of Logic Language and Information ◽

10.1007/s10849-021-09339-6 ◽

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.

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

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10030042 ◽

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.

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