GRAVITAS: A model checking based planning and goal reasoning framework for autonomous systems. (January 2021)

• Record Type: Journal Article
• Authors: Bride, Hadrien; Dong, Jin Song; Green, Ryan; Hóu, Zhé; Mahony, Brendan; Oxenham, Martin
• Is Part Of: Engineering applications of artificial intelligence. Volume 97(2021)
• Publication Date: 2021-01
• Subjects: Goal reasoning -- Planning -- Model-checking -- Verification -- Autonomous systems -- Trusted systems; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.104091
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This work follow the verification as planning paradigm and propose to use model-checking techniques to solve planning and goal reasoning problems for autonomous systems with high-degree of assurance. It presents a novel modelling framework — Goal Task Network (GTN) that encompass both goal reasoning and planning under a unified formal description that enables the use of assurance tools. The paper provides a systematic method that highlights how an industrial model checker (PAT) can be used to solve goal reasoning and planning problems modelled by GTNs. Further, this paper also introduces the design of an automated system framework for Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS). The proposed framework is demonstrated in an experiment that simulates a survey mission performed by the REMUS-100 autonomous underwater vehicle.