Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Alessio Lomuscio"'
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:7272-7280
We present a novel semidefinite programming (SDP) relaxation that enables tight and efficient verification of neural networks. The tightness is achieved by combining SDP relaxations with valid linear cuts, constructed by using the reformulation-linea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a18e202efe80690175dae4637d2b087
https://doi.org/10.1609/aaai.v36i7.20689
https://doi.org/10.1609/aaai.v36i7.20689
Publikováno v:
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research, 2022, 73, pp.897-932. ⟨10.1613/jair.1.12539⟩
Journal of Artificial Intelligence Research, 2022, 73, pp.897-932. ⟨10.1613/jair.1.12539⟩
International audience; The model checking problem for multi-agent systems against specifications in the alternating-time temporal logic AT L, hence AT L∗ , under perfect recall and imperfect information is known to be undecidable. To tackle this p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c3f1b629f0840c79b031ce1e87f3307
https://doi.org/10.1613/jair.1.12539
https://doi.org/10.1613/jair.1.12539
Publikováno v:
AAAI
Francesco Belardinelli
Francesco Belardinelli
We study the problem of verifying multi-agent systems under the assumption of bounded recall. We introduce the logic CTLKBR, a bounded-recall variant of the temporal-epistemic logic CTLK. We define and study the model checking problem against CTLK sp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8077898865d881d0d8580d4be6c7e122
https://doi.org/10.1609/aaai.v34i05.6193
https://doi.org/10.1609/aaai.v34i05.6193
Publikováno v:
AAAI
We introduce an efficient method for the verification of ReLU-based feed-forward neural networks. We derive an automated procedure that exploits dependency relations between the ReLU nodes, thereby pruning the search tree that needs to be considered
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0780084990a3455466833ec2deb3452
https://doi.org/10.1609/aaai.v34i04.5729
https://doi.org/10.1609/aaai.v34i04.5729
Autor:
Mykel J. Kochenderfer, Ransalu Senanayake, Daniel J. Fremont, Alessio Lomuscio, Dragos D. Margineantu, Cheng Soon Ong
Publikováno v:
Machine Learning.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4eee402ecc1be3bdbb48a18c4b7fd981
https://doi.org/10.1007/s10994-021-06113-4
https://doi.org/10.1007/s10994-021-06113-4
Autor:
Denis Osipychev, Panagiotis Kouvaros, Trent Kyono, Dragos D. Margineantu, Alessio Lomuscio, Yang Zheng, Francesco Leofante
Publikováno v:
International Symposium on Formal Methods
Formal Methods ISBN: 9783030908690
FM
Formal Methods ISBN: 9783030908690
FM
Neural networks are being increasingly used for efficient decision making in the aircraft domain. Given the safety-critical nature of the applications involved, stringent safety requirements must be met by these networks. In this work we present a fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db9dfbf4087d2488e0ac0f23fdb47515
http://hdl.handle.net/10044/1/94034
http://hdl.handle.net/10044/1/94034
Publikováno v:
KR
Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning
Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning
Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning
Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning
We formally introduce and solve the synthesis problem for LTL goals in the case of multiple, even contradicting, assumptions about the environment. Our solution concept is based on ``best-effort strategies'' which are agent plans that, for each of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70ef9597f410afef24f9d8bc85ee99ec
https://doi.org/10.24963/kr.2021/5
https://doi.org/10.24963/kr.2021/5
Publikováno v:
AAMAS
We introduce a model for agent-environment systems where the agents are implemented via feed-forward ReLU neural networks and the environment is non-deterministic. We study the verification problem of such systems against CTL properties. We show that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6b492cd471e89f220627d9a1545a029
http://hdl.handle.net/10044/1/95834
http://hdl.handle.net/10044/1/95834
Autor:
Alessio Lomuscio, Patrick Henriksen
Publikováno v:
IJCAI
We propose a novel, complete algorithm for the verification and analysis of feed-forward, ReLU-based neural networks. The algorithm, based on symbolic interval propagation, introduces a new method for determining split-nodes which evaluates the indir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::293dfbc6812a5f3b93ea4023fb263348
https://doi.org/10.24963/ijcai.2021/351
https://doi.org/10.24963/ijcai.2021/351
Autor:
Alessio Lomuscio, Panagiotis Kouvaros
Publikováno v:
IJCAI
We introduce an efficient method for the complete verification of ReLU-based feed-forward neural networks. The method implements branching on the ReLU states on the basis of a notion of dependency between the nodes. This results in dividing the origi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21c97d9e06598bbe8c37ff6742a03c40
https://doi.org/10.24963/ijcai.2021/364
https://doi.org/10.24963/ijcai.2021/364