About: Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #90813 Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems
sameAs	Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems
Title	Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2020-09-12
Creator	Alessio Lomuscio Panagiotis Kouvaros Michael E. Akintunde Elena Botoeva
status	peer reviewed published
Publisher	International Joint Conferences on Artificial Intelligence Organization
abstract	We investigate the problem of verifying the strategic properties of multi-agent systems equipped with machine learning-based perception units. We introduce a novel model of agents comprising both a perception system implemented via feed-forward neural networks and an action selection mechanism implemented via traditional control logic. We define the verification problem for these systems against a bounded fragment of alternating-time temporal logic. We translate the verification problem on bounded traces into the feasibility problem of mixed integer linear programs and show the soundness and completeness of the translation. We show that the lower bound of the verification problem is PSPACE and the upper bound is coNEXPTIME. We present a tool implementing the compilation and evaluate the experimental results obtained on a complex scenario of multiple aircraft operating a recently proposed prototype for air-traffic collision avoidance.
Is Part Of	Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning https://kar.kent.ac.uk/id/repository
Subject	QA 75 Electronic computers. Computer science
list of authors	https://kar.kent.ac.uk/id/eprint/90813#authors
presented at	17th International Conference on Principles of Knowledge Representation and Reasoning (KR2020)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #90813 Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems

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