dc.contributor.author | Bride, Hadrien | |
dc.contributor.author | Dong, jin | |
dc.contributor.author | Hou, Z | |
dc.contributor.author | Mahony, Brendan | |
dc.contributor.author | Oxenham, Martin | |
dc.date.accessioned | 2020-07-03T05:01:23Z | |
dc.date.available | 2020-07-03T05:01:23Z | |
dc.date.issued | 2018 | |
dc.identifier.isbn | 978-3-030-02449-9 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.doi | 10.1007/978-3-030-02450-5_24 | |
dc.identifier.uri | http://hdl.handle.net/10072/383956 | |
dc.description.abstract | Trust remains a major challenge in the development, implementation and deployment of artificial intelligence and autonomous systems in defence and law enforcement industries. To address the issue, we follow the verification as planning paradigm based on model checking techniques to solve planning and goal reasoning problems for autonomous systems. Specifically, we present a novel framework named Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS) and discuss how it helps provide trustworthy plans in uncertain and dynamic environment. | |
dc.description.peerreviewed | Yes | |
dc.publisher | Springer Nature | |
dc.publisher.place | Switzerland | |
dc.relation.ispartofconferencename | ICFEM 2018 | |
dc.relation.ispartofconferencetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.relation.ispartofdatefrom | 2018-11-12 | |
dc.relation.ispartofdateto | 2018-11-16 | |
dc.relation.ispartoflocation | Gold Coast, Australia | |
dc.relation.ispartofpagefrom | 407 | |
dc.relation.ispartofpagefrom | 5 pages | |
dc.relation.ispartofpageto | 411 | |
dc.relation.ispartofpageto | 5 pages | |
dc.relation.ispartofvolume | 11232 | |
dc.subject.fieldofresearch | Electrical engineering | |
dc.subject.fieldofresearch | Electronics, sensors and digital hardware | |
dc.subject.fieldofresearch | Information and computing sciences | |
dc.subject.fieldofresearchcode | 4008 | |
dc.subject.fieldofresearchcode | 4009 | |
dc.subject.fieldofresearchcode | 46 | |
dc.title | Towards trustworthy AI for autonomous systems | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dc.type.code | E - Conference Publications | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | © Springer Nature Switzerland AG 2018. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Dong, Jin-Song | |
gro.griffith.author | Hou, Zhe | |